Change Password

Your password must have 6 characters or more:.

  • a lower case character, 
  • an upper case character, 
  • a special character 

Password Changed Successfully

Your password has been changed

Create your account

Forget yout password.

Enter your email address below and we will send you the reset instructions

If the address matches an existing account you will receive an email with instructions to reset your password

Forgot your Username?

Enter your email address below and we will send you your username

If the address matches an existing account you will receive an email with instructions to retrieve your username

Psychiatry Online

  • February 01, 2024 | VOL. 181, NO. 2 CURRENT ISSUE pp.83-170
  • January 01, 2024 | VOL. 181, NO. 1 pp.1-82

The American Psychiatric Association (APA) has updated its Privacy Policy and Terms of Use , including with new information specifically addressed to individuals in the European Economic Area. As described in the Privacy Policy and Terms of Use, this website utilizes cookies, including for the purpose of offering an optimal online experience and services tailored to your preferences.

Please read the entire Privacy Policy and Terms of Use. By closing this message, browsing this website, continuing the navigation, or otherwise continuing to use the APA's websites, you confirm that you understand and accept the terms of the Privacy Policy and Terms of Use, including the utilization of cookies.

The Critical Relationship Between Anxiety and Depression

  • Ned H. Kalin , M.D.

Search for more papers by this author

Anxiety and depressive disorders are among the most common psychiatric illnesses; they are highly comorbid with each other, and together they are considered to belong to the broader category of internalizing disorders. Based on statistics from the Substance Abuse and Mental Health Services Administration, the 12-month prevalence of major depressive disorder in 2017 was estimated to be 7.1% for adults and 13.3% for adolescents ( 1 ). Data for anxiety disorders are less current, but in 2001–2003, their 12-month prevalence was estimated to be 19.1% in adults, and 2001–2004 data estimated that the lifetime prevalence in adolescents was 31.9% ( 2 , 3 ). Both anxiety and depressive disorders are more prevalent in women, with an approximate 2:1 ratio in women compared with men during women’s reproductive years ( 1 , 2 ).

Across all psychiatric disorders, comorbidity is the rule ( 4 ), which is definitely the case for anxiety and depressive disorders, as well as their symptoms. With respect to major depression, a worldwide survey reported that 45.7% of individuals with lifetime major depressive disorder had a lifetime history of one or more anxiety disorder ( 5 ). These disorders also commonly coexist during the same time frame, as 41.6% of individuals with 12-month major depression also had one or more anxiety disorder over the same 12-month period. From the perspective of anxiety disorders, the lifetime comorbidity with depression is estimated to range from 20% to 70% for patients with social anxiety disorder ( 6 ), 50% for patients with panic disorder ( 6 ), 48% for patients with posttraumatic stress disorder (PTSD) ( 7 ), and 43% for patients with generalized anxiety disorder ( 8 ). Data from the well-known Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study demonstrate comorbidity at the symptom level, as 53% of the patients with major depression had significant anxiety and were considered to have an anxious depression ( 9 ).

Anxiety and depressive disorders are moderately heritable (approximately 40%), and evidence suggests shared genetic risk across the internalizing disorders ( 10 ). Among internalizing disorders, the highest level of shared genetic risk appears to be between major depressive disorder and generalized anxiety disorder. Neuroticism is a personality trait or temperamental characteristic that is associated with the development of both anxiety and depression, and the genetic risk for developing neuroticism also appears to be shared with that of the internalizing disorders ( 11 ). Common nongenetic risk factors associated with the development of anxiety and depression include earlier life adversity, such as trauma or neglect, as well as parenting style and current stress exposure. At the level of neural circuits, alterations in prefrontal-limbic pathways that mediate emotion regulatory processes are common to anxiety and depressive disorders ( 12 , 13 ). These findings are consistent with meta-analyses that reveal shared structural and functional brain alterations across various psychiatric illnesses, including anxiety and major depression, in circuits involving emotion regulation ( 13 ), executive function ( 14 ), and cognitive control ( 15 ).

Anxiety disorders and major depression occur during development, with anxiety disorders commonly beginning during preadolescence and early adolescence and major depression tending to emerge during adolescence and early to mid-adulthood ( 16 – 18 ). In relation to the evolution of their comorbidity, studies demonstrate that anxiety disorders generally precede the presentation of major depressive disorder ( 17 ). A European community-based study revealed, beginning at age 15, the developmental relation between comorbid anxiety and major depression by specifically focusing on social phobia (based on DSM-IV criteria) and then asking the question regarding concurrent major depressive disorder ( 18 ). The findings revealed a 19% concurrent comorbidity between these disorders, and in 65% of the cases, social phobia preceded major depressive disorder by at least 2 years. In addition, initial presentation with social phobia was associated with a 5.7-fold increased risk of developing major depressive disorder. These associations between anxiety and depression can be traced back even earlier in life. For example, childhood behavioral inhibition in response to novelty or strangers, or an extreme anxious temperament, is associated with a three- to fourfold increase in the likelihood of developing social anxiety disorder, which in turn is associated with an increased risk to develop major depressive disorder and substance abuse ( 19 ).

It is important to emphasize that the presence of comor‐bid anxiety symptoms and disorders matters in relation to treatment. Across psychiatric disorders, the presence of significant anxiety symptoms generally predicts worse outcomes, and this has been well demonstrated for depression. In the STAR*D study, patients with anxious major depressive disorder were more likely to be severely depressed and to have more suicidal ideation ( 9 ). This is consistent with the study by Kessler and colleagues ( 5 ), in which patients with anxious major depressive disorder, compared with patients with nonanxious major depressive disorder, were found to have more severe role impairment and more suicidal ideation. Data from level 1 of the STAR*D study (citalopram treatment) nicely illustrate the impact of comorbid anxiety symptoms on treatment. Compared with patients with nonanxious major depressive disorder, those 53% of patients with an anxious depression were less likely to remit and also had a greater side effect burden ( 20 ). Other data examining patients with major depressive disorder and comorbid anxiety disorders support the greater difficulty and challenge in treating patients with these comorbidities ( 21 ).

This issue of the Journal presents new findings relevant to the issues discussed above in relation to understanding and treating anxiety and depressive disorders. Drs. Conor Liston and Timothy Spellman, from Weill Cornell Medicine, provide an overview for this issue ( 22 ) that is focused on understanding mechanisms at the neural circuit level that underlie the pathophysiology of depression. Their piece nicely integrates human neuroimaging studies with complementary data from animal models that allow for the manipulation of selective circuits to test hypotheses generated from the human data. Also included in this issue is a review of the data addressing the reemergence of the use of psychedelic drugs in psychiatry, particularly for the treatment of depression, anxiety, and PTSD ( 23 ). This timely piece, authored by Dr. Collin Reiff along with a subgroup from the APA Council of Research, provides the current state of evidence supporting the further exploration of these interventions. Dr. Alan Schatzberg, from Stanford University, contributes an editorial in which he comments on where the field is in relation to clinical trials with psychedelics and to some of the difficulties, such as adequate blinding, in reliably studying the efficacy of these drugs ( 24 ).

In an article by McTeague et al. ( 25 ), the authors use meta-analytic strategies to understand the neural alterations that are related to aberrant emotion processing that are shared across psychiatric disorders. Findings support alterations in the salience, reward, and lateral orbital nonreward networks as common across disorders, including anxiety and depressive disorders. These findings add to the growing body of work that supports the concept that there are common underlying factors across all types of psychopathology that include internalizing, externalizing, and thought disorder dimensions ( 26 ). Dr. Deanna Barch, from Washington University in St. Louis, writes an editorial commenting on these findings and, importantly, discusses criteria that should be met when we consider whether the findings are actually transdiagnostic ( 27 ).

Another article, from Gray and colleagues ( 28 ), addresses whether there is a convergence of findings, specifically in major depression, when examining data from different structural and functional neuroimaging modalities. The authors report that, consistent with what we know about regions involved in emotion processing, the subgenual anterior cingulate cortex, hippocampus, and amygdala were among the regions that showed convergence across multimodal imaging modalities.

In relation to treatment and building on our understanding of neural circuit alterations, Siddiqi et al. ( 29 ) present data suggesting that transcranial magnetic stimulation (TMS) targeting can be linked to symptom-specific treatments. Their findings identify different TMS targets in the left dorsolateral prefrontal cortex that modulate different downstream networks. The modulation of these different networks appears to be associated with a reduction in different types of symptoms. In an editorial, Drs. Sean Nestor and Daniel Blumberger, from the University of Toronto ( 30 ), comment on the novel approach used in this study to link the TMS-related engagement of circuits with symptom improvement. They also provide a perspective on how we can view these and other circuit-based findings in relation to conceptualizing personalized treatment approaches.

Kendler et al. ( 31 ), in this issue, contribute an article that demonstrates the important role of the rearing environment in the risk to develop major depression. Using a unique design from a Swedish sample, the analytic strategy involves comparing outcomes from high-risk full sibships and high-risk half sibships where at least one of the siblings was home reared and one was adopted out of the home. The findings support the importance of the quality of the rearing environment as well as the presence of parental depression in mitigating or enhancing the likelihood of developing major depression. In an accompanying editorial ( 32 ), Dr. Myrna Weissman, from Columbia University, reviews the methods and findings of the Kendler et al. article and also emphasizes the critical significance of the early nurturing environment in relation to general health.

This issue concludes with an intriguing article on anxiety disorders, by Gold and colleagues ( 33 ), that demonstrates neural alterations during extinction recall that differ in children relative to adults. With increasing age, and in relation to fear and safety cues, nonanxious adults demonstrated greater connectivity between the amygdala and the ventromedial prefrontal cortex compared with anxious adults, as the cues were being perceived as safer. In contrast, neural differences between anxious and nonanxious youths were more robust when rating the memory of faces that were associated with threat. Specifically, these differences were observed in the activation of the inferior temporal cortex. In their editorial ( 34 ), Dr. Dylan Gee and Sahana Kribakaran, from Yale University, emphasize the importance of developmental work in relation to understanding anxiety disorders, place these findings into the context of other work, and suggest the possibility that these and other data point to neuroscientifically informed age-specific interventions.

Taken together, the papers in this issue of the Journal present new findings that shed light onto alterations in neural function that underlie major depressive disorder and anxiety disorders. It is important to remember that these disorders are highly comorbid and that their symptoms are frequently not separable. The papers in this issue also provide a developmental perspective emphasizing the importance of early rearing in the risk to develop depression and age-related findings important for understanding threat processing in patients with anxiety disorders. From a treatment perspective, the papers introduce data supporting more selective prefrontal cortical TMS targeting in relation to different symptoms, address the potential and drawbacks for considering the future use of psychedelics in our treatments, and present new ideas supporting age-specific interventions for youths and adults with anxiety disorders.

Disclosures of Editors’ financial relationships appear in the April 2020 issue of the Journal .

1 Substance Abuse and Mental Health Services Administration (SAMHSA): Key substance use and mental health indicators in the United States: results from the 2017 National Survey on Drug Use and Health (HHS Publication No. SMA 18-5068, NSDUH Series H-53). Rockville, Md, Center for Behavioral Health Statistics and Quality, SAMHSA, 2018. https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHFFR2017/NSDUHFFR2017.htm Google Scholar

2 Kessler RC, Chiu WT, Demler O, et al. : Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication . Arch Gen Psychiatry 2005 ; 62:617–627, correction, 62:709 Crossref , Medline ,  Google Scholar

3 Merikangas KR, He JP, Burstein M, et al. : Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A) . J Am Acad Child Adolesc Psychiatry 2010 ; 49:980–989 Crossref , Medline ,  Google Scholar

4 Kessler RC, McGonagle KA, Zhao S, et al. : Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey . Arch Gen Psychiatry 1994 ; 51:8–19 Crossref , Medline ,  Google Scholar

5 Kessler RC, Sampson NA, Berglund P, et al. : Anxious and non-anxious major depressive disorder in the World Health Organization World Mental Health Surveys . Epidemiol Psychiatr Sci 2015 ; 24:210–226 Crossref , Medline ,  Google Scholar

6 Dunner DL : Management of anxiety disorders: the added challenge of comorbidity . Depress Anxiety 2001 ; 13:57–71 Crossref , Medline ,  Google Scholar

7 Kessler RC, Sonnega A, Bromet E, et al. : Posttraumatic stress disorder in the National Comorbidity Survey . Arch Gen Psychiatry 1995 ; 52:1048–1060 Crossref , Medline ,  Google Scholar

8 Brawman-Mintzer O, Lydiard RB, Emmanuel N, et al. : Psychiatric comorbidity in patients with generalized anxiety disorder . Am J Psychiatry 1993 ; 150:1216–1218 Link ,  Google Scholar

9 Fava M, Alpert JE, Carmin CN, et al. : Clinical correlates and symptom patterns of anxious depression among patients with major depressive disorder in STAR*D . Psychol Med 2004 ; 34:1299–1308 Crossref , Medline ,  Google Scholar

10 Hettema JM : What is the genetic relationship between anxiety and depression? Am J Med Genet C Semin Med Genet 2008 ; 148C:140–146 Crossref , Medline ,  Google Scholar

11 Hettema JM, Neale MC, Myers JM, et al. : A population-based twin study of the relationship between neuroticism and internalizing disorders . Am J Psychiatry 2006 ; 163:857–864 Link ,  Google Scholar

12 Kovner R, Oler JA, Kalin NH : Cortico-limbic interactions mediate adaptive and maladaptive responses relevant to psychopathology . Am J Psychiatry 2019 ; 176:987–999 Link ,  Google Scholar

13 Etkin A, Schatzberg AF : Common abnormalities and disorder-specific compensation during implicit regulation of emotional processing in generalized anxiety and major depressive disorders . Am J Psychiatry 2011 ; 168:968–978 Link ,  Google Scholar

14 Goodkind M, Eickhoff SB, Oathes DJ, et al. : Identification of a common neurobiological substrate for mental illness . JAMA Psychiatry 2015 ; 72:305–315 Crossref , Medline ,  Google Scholar

15 McTeague LM, Huemer J, Carreon DM, et al. : Identification of common neural circuit disruptions in cognitive control across psychiatric disorders . Am J Psychiatry 2017 ; 174:676–685 Link ,  Google Scholar

16 Beesdo K, Knappe S, Pine DS : Anxiety and anxiety disorders in children and adolescents: developmental issues and implications for DSM-V . Psychiatr Clin North Am 2009 ; 32:483–524 Crossref , Medline ,  Google Scholar

17 Kessler RC, Wang PS : The descriptive epidemiology of commonly occurring mental disorders in the United States . Annu Rev Public Health 2008 ; 29:115–129 Crossref , Medline ,  Google Scholar

18 Ohayon MM, Schatzberg AF : Social phobia and depression: prevalence and comorbidity . J Psychosom Res 2010 ; 68:235–243 Crossref , Medline ,  Google Scholar

19 Clauss JA, Blackford JU : Behavioral inhibition and risk for developing social anxiety disorder: a meta-analytic study . J Am Acad Child Adolesc Psychiatry 2012 ; 51:1066–1075 Crossref , Medline ,  Google Scholar

20 Fava M, Rush AJ, Alpert JE, et al. : Difference in treatment outcome in outpatients with anxious versus nonanxious depression: a STAR*D report . Am J Psychiatry 2008 ; 165:342–351 Link ,  Google Scholar

21 Dold M, Bartova L, Souery D, et al. : Clinical characteristics and treatment outcomes of patients with major depressive disorder and comorbid anxiety disorders: results from a European multicenter study . J Psychiatr Res 2017 ; 91:1–13 Crossref , Medline ,  Google Scholar

22 Spellman T, Liston C : Toward circuit mechanisms of pathophysiology in depression . Am J Psychiatry 2020 ; 177:381–390 Link ,  Google Scholar

23 Reiff CM, Richman EE, Nemeroff CB, et al. : Psychedelics and psychedelic-assisted psychotherapy . Am J Psychiatry 2020 ; 177:391–410 Link ,  Google Scholar

24 Schatzberg AF : Some comments on psychedelic research (editorial). Am J Psychiatry 2020 ; 177:368–369 Link ,  Google Scholar

25 McTeague LM, Rosenberg BM, Lopez JW, et al. : Identification of common neural circuit disruptions in emotional processing across psychiatric disorders . Am J Psychiatry 2020 ; 177:411–421 Link ,  Google Scholar

26 Caspi A, Moffitt TE : All for one and one for all: mental disorders in one dimension . Am J Psychiatry 2018 ; 175:831–844 Link ,  Google Scholar

27 Barch DM : What does it mean to be transdiagnostic and how would we know? (editorial). Am J Psychiatry 2020 ; 177:370–372 Abstract ,  Google Scholar

28 Gray JP, Müller VI, Eickhoff SB, et al. : Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies . Am J Psychiatry 2020 ; 177:422–434 Link ,  Google Scholar

29 Siddiqi SH, Taylor SF, Cooke D, et al. : Distinct symptom-specific treatment targets for circuit-based neuromodulation . Am J Psychiatry 2020 ; 177:435–446 Link ,  Google Scholar

30 Nestor SM, Blumberger DM : Mapping symptom clusters to circuits: toward personalizing TMS targets to improve treatment outcomes in depression (editorial). Am J Psychiatry 2020 ; 177:373–375 Abstract ,  Google Scholar

31 Kendler KS, Ohlsson H, Sundquist J, et al. : The rearing environment and risk for major depression: a Swedish national high-risk home-reared and adopted-away co-sibling control study . Am J Psychiatry 2020 ; 177:447–453 Abstract ,  Google Scholar

32 Weissman MM : Is depression nature or nurture? Yes (editorial). Am J Psychiatry 2020 ; 177:376–377 Abstract ,  Google Scholar

33 Gold AL, Abend R, Britton JC, et al. : Age differences in the neural correlates of anxiety disorders: an fMRI study of response to learned threat . Am J Psychiatry 2020 ; 177:454–463 Link ,  Google Scholar

34 Gee DG, Kribakaran S : Developmental differences in neural responding to threat and safety: implications for treating youths with anxiety (editorial). Am J Psychiatry 2020 ; 177:378–380 Abstract ,  Google Scholar

  • Cited by None

research paper about stress and depression

  • Neuroanatomy
  • Neurochemistry
  • Neuroendocrinology
  • Other Research Areas

Change Password

Your password must have 6 characters or more:.

  • a lower case character, 
  • an upper case character, 
  • a special character 

Password Changed Successfully

Your password has been changed

Create your account

Forget yout password.

Enter your email address below and we will send you the reset instructions

If the address matches an existing account you will receive an email with instructions to reset your password

Forgot your Username?

Enter your email address below and we will send you your username

If the address matches an existing account you will receive an email with instructions to retrieve your username

Psychiatry Online

  • Winter 2024 | VOL. 36, NO. 1 CURRENT ISSUE pp.A5-81

The American Psychiatric Association (APA) has updated its Privacy Policy and Terms of Use , including with new information specifically addressed to individuals in the European Economic Area. As described in the Privacy Policy and Terms of Use, this website utilizes cookies, including for the purpose of offering an optimal online experience and services tailored to your preferences.

Please read the entire Privacy Policy and Terms of Use. By closing this message, browsing this website, continuing the navigation, or otherwise continuing to use the APA's websites, you confirm that you understand and accept the terms of the Privacy Policy and Terms of Use, including the utilization of cookies.

The Links Between Stress and Depression: Psychoneuroendocrinological, Genetic, and Environmental Interactions

  • Gustavo E. Tafet , M.D., Ph.D. ,
  • Charles B. Nemeroff , M.D., Ph.D.

Search for more papers by this author

The role of stress in the origin and development of depression may be conceived as the result of multiple converging factors, including the chronic effect of environmental stressors and the long-lasting effects of stressful experiences during childhood, all of which may induce persistent hyperactivity of the hypothalamic-pituitary-adrenal axis. These changes, including increased availability of corticotropin-releasing factor and cortisol, are also associated with hyperactivity of the amygdala, hypoactivity of the hippocampus, and decreased serotonergic neurotransmission, which together result in increased vulnerability to stress. The role of other monoaminergic neurotransmitters, genetic polymorphisms, epigenetic mechanisms, inflammatory processes, and altered cognitive processing has also been considered in the development of a comprehensive model of the interactions between different factors of vulnerability. Further understanding of the underlying mechanisms that link these factors may contribute significantly to the development of more effective treatments and preventive strategies in the interface between stress and mood disorders.

The link between stressful life events and the origin and development of depression has been widely investigated, providing an increasing body of evidence supporting this association. 1 – 3 Environmental factors likely affect individuals in somewhat different manners, therefore triggering an adaptive response to stress, which depends on both psychological and biological aspects in the interaction between stressors and individual resources. Psychological aspects include all of the cognitive processing related to incoming information; the subjective appraisal of different features related to stressors, such as magnitude and chronicity, predictability, and controllability; and potential resources to cope with them. Biological mediators include the activation of different neural structures underlying information processing, including sensory pathways, which convey environmental input to the CNS, and the resulting activation of neural and neuroendocrine cascades of molecular events, mediated by the subsequent activation of the sympathetic division of the autonomic nervous system and the hypothalamic-pituitary-adrenal (HPA) axis. 4 The efficacy of an adaptive response implies that it may be rapidly activated, to allow reacting in a successful and effective manner during stressful situations, and it should be efficiently controlled and concluded afterward. If it continues in a prolonged and excessive manner (e.g., during chronic stressful situations), it may lead to maladaptive changes, which in turn may contribute to the development of pathological conditions such as anxiety and mood disorders, including depression, particularly in individuals with increased genetic vulnerability. In this regard, various polymorphisms have been investigated as candidate genes, which are known to participate in important molecular pathways involved in the origin of depression. The presence of these genetic variations appears to be involved in the development of depression in response to stressful events, including adverse experiences during childhood and environmental stressors during adulthood. 5 – 10 Moreover, various studies have focused on the role of gene–environment interactions, including the search for these polymorphic variants and the role of transcriptional regulation by epigenetic mechanisms. 6 , 11 – 13 In addition, inflammatory processes associated with adaptive responses to stressful situations, with the consequent synthesis and release of proinflammatory cytokines, may lead to further maladaptive changes of neural and neuroendocrine systems, therefore contributing to the development of depressive symptoms, particularly in chronically stressed individuals.

This article aims to review the evidence for the role played by stress, associated with different converging factors, including a genetic diathesis, a history of adverse early life events, hyperactivity of the HPA axis, decreased monoamines, increased proinflammatory cytokines, and epigenetic mechanisms, such as those observed in response to environmental stressful conditions, and their potential interactions in the etiology of depression. An increased understanding of these factors and their potential interactions may lead to more effective strategies for the treatment of this disorder.

Processing of Environmental Stressors in the Brain

Environmental stressors are perceived and transmitted through sensory pathways to different structures in the CNS, such as the thalamus, which convey projections to the amygdala, and to sensory and association cortices, which in turn also project to different areas of the prefrontal cortex (PFC), including the orbitofrontal cortex, the medial PFC, and the anterior cingulate cortex (ACC). 4 , 7 Direct projections from the thalamus to the amygdala contribute to activate arousal and early alarm reactions, with the subsequent activation of the autonomic nervous system and the HPA axis, whereas indirect projections may reach the amygdala from sensory and association cortices as well as from transition cortices. The latter areas, including the entorhinal, perirhinal, and parahippocampal cortices, in turn project to the hippocampus, where sensory input is integrated with contextual cues, to convey more elaborated information to the amygdala. 14

The amygdala plays a critical role in emotional processing, including the assessment of the emotional relevance of environmental stimuli as well as internal stressors. It plays a key role in the regulation of autonomic and neuroendocrine responses, through projections to the lateral hypothalamus, which mediate the activation of the sympathetic branch of the autonomic nervous system; through direct projections to the paraventricular nucleus of the hypothalamus; or indirectly through the bed nucleus of the stria terminalis, which is involved in activation of the HPA axis. 14 In addition, the amygdala shares important connections with the orbitofrontal cortex and the medial PFC, 15 including Brodmann areas 10 and 32, and the subgenual ACC (Brodmann area 25). 16 The orbitofrontal cortex (Brodmann areas 11–14) has been associated with integration of multimodal sensory stimuli and primary appraisal of their positive or negative value, therefore participating in their affective assessment. 17 The medial PFC overlaps with the ACC, particularly in the subgenual ACC, 17 which regulates emotional responses generated by the amygdala. 18 These structures are in turn connected with the dorsolateral PFC (Brodmann areas 9 and 46) and the ventrolateral PFC (Brodmann areas 45 and 47), which participate in cognitive control and voluntary regulation of emotion. The dorsolateral PFC, which has been associated with executive aspects of cognitive processing 19 (most notably with conscious processing and working memory), receives input from the amygdala through the orbitofrontal cortex and ACC. 15 , 17 The dorsolateral PFC reciprocally projects back to limbic structures, mostly through indirect connections to the ventromedial PFC (Brodmann area 32), which projects to the subgenual ACC. 19 It has been proposed that projections from the ventromedial PFC and the subgenual ACC exert a modulatory effect on the amygdala, 19 , 20 which in turn sends excitatory output to the hypothalamus, 17 – 19 therefore regulating the activity of the HPA axis.

Decreased volume of the subgenual ACC has been described, together with hyperactivity of the amygdala, in individuals with mood disorders, 16 , 21 which has been associated with the role of the subgenual ACC in the top-down regulatory pathway between the dorsolateral PFC and the amygdala, allowing conscious down-regulation of negative emotions. These corticolimbic pathways may be dysfunctional in patients with depression, in which the dorsolateral PFC, dorsomedial PFC, orbitofrontal cortex, and ACC appear to be dysfunctional, particularly during cognitive-emotional tasks, with the consequent disruption of their top-down inhibitory effect expressed in the impaired cognitive modulation of emotions. 20 , 21 Recovery of conscious regulation of negative emotions has been associated with clinical recovery. In addition, decreased hippocampal volume has also been observed, along with increased activity of the amygdala and reduced activity of the dorsolateral PFC. 21 More recently, we documented changes in cortical thickness in patients exposed to child abuse and neglect, with the findings specific to the nature of the abuse. 22

Figure 1 illustrates the network of functional connections among different neural structures involved in adaptive responses to stress, including the processing of environmental stimuli through cortical and subcortical structures, and the activation of the HPA axis.

FIGURE 1. Schematic Representation of Neural Structures Involved in the Stress Response a

a Stressors are perceived by sensory receptors, which convey information to the thalamus, primary sensory cortices, association cortices, transition cortices, the hippocampus, and the amygdala. The amygdala also receives direct input from the thalamus. The orbitofrontal cortex and the medial prefrontal cortex are reciprocally connected and, together with the anterior cingulate cortex, convey information from sensory cortices and association cortices to subcortical structures, including direct connections to the hypothalamus and reciprocal connections with the amygdala. The amygdala participates in the activation of the HPA axis through stimulatory projections to the paraventricular nucleus of the hypothalamus, with consequent synthesis and release of CRF, which stimulates the release of ACTH from the pituitary. In turn, this stimulates the release of glucocorticoids from the adrenals, particularly cortisol. Cortisol exerts negative feedback at the level of the hypothalamus and the pituitary, as well as through the hippocampus, which exerts an inhibitory effect on the HPA axis. Activation of the HPA axis is also regulated by norepinephrine, through projections from the locus coeruleus, and serotonin, and through projections from the raphe nuclei. Both aminergic systems participate in regulation of the stress response through connections with the amygdala and the hippocampus, therefore exerting regulatory effects on both limbic structures. The amygdala is also involved in the activation of the autonomic component of the stress response through CRF inputs to the locus coeruleus. Solid lines indicate stimulatory inputs, whereas dotted lines indicate inhibitory inputs. ACC, anterior cingulate cortex; ACTH, adrenocorticotropin; CRF, corticotropin-releasing factor; DLPFC, dorsolateral prefrontal cortex; HPA, hypothalamic-pituitary-adrenal; MPFC, medial prefrontal cortex; OFC, orbitofrontal cortex.

Role of the HPA Axis

Activation of the HPA axis is initiated in limbic structures, including direct projections from the central nucleus of the amygdala, or indirectly through the bed nucleus of the stria terminalis, which projects to the hypothalamic paraventricular nucleus, where corticotropin-releasing factor (CRF) is synthesized in parvocellular neurons and released to reach the anterior pituitary. There, CRF regulates the transcription of the proopiomelanocortin gene (a common precursor for adrenocorticotropin, β-endorphin, and related peptides) and stimulates the release of adrenocorticotropin into the systemic circulation. Adrenocorticotropin acts upon the adrenal cortex to stimulate the biosynthesis and release of glucocorticoids, particularly cortisol. 23

At the molecular level, cortisol binds to mineralocorticoid receptors (type I) and glucocorticoid receptors (GRs; type II), constituting a hormone-receptor complex, which in turn undergoes conformational changes to allow its recognition and binding to a glucocorticoid response element, in the promoter region of many target genes. 24 Cortisol regulates the activity of the HPA axis through multiple negative feedback loops, which require its binding to GRs located in the paraventricular nucleus and the pituitary, where it down-regulates the synthesis and release of CRF and adrenocorticotropin, respectively, and GRs in the hippocampus, which in turn activates GABAergic projections to the paraventricular nucleus that inhibit HPA axis activity. Hence, many of the effects of cortisol may be understood as a result of transcriptional regulation of various genes, including those involved in the negative feedback loops responsible for the regulation of the HPA axis. 24

In response to short-term exposure to environmental stressors, the amygdala stimulates the HPA axis with the consequent synthesis and release of cortisol, 14 which is self-regulated by negative feedback mechanisms mediated by the glucocorticoid. In addition, the HPA system interacts with CRF neurons in the amygdala, activating a positive feedback loop involved in fear and anger reactions; the HPA also activates catecholaminergic neurons, stimulating arousal and improving cognitive functions. Hence, upon exposure to acute or short-term stressors, cortisol is expected to exert widespread metabolic effects, which is mostly necessary to maintain or restore homeostasis. 25 Cortisol is actively involved in the mobilization of energetic resources, including the stimulation of gluconeogenesis with the resulting increased levels of circulating glucose, and the down-regulation of inflammatory processes, therefore contributing to coping with the stressful situation.

Chronic and persistent activation of the HPA system may disrupt physiological mechanisms, including negative feedback loops, resulting in persistent activation of the system. Circadian rhythms normally characterized by wide variations, with morning zeniths and evening nadirs, are markedly altered during chronic stress, with the consequent increase in plasma cortisol levels and blunted circadian rhythm, mostly due to increased levels of cortisol during the evening and mild changes in the morning. 25 Prolonged exposure to increased levels of cortisol may induce detrimental effects on hippocampal neurons, reducing dendritic branching and inhibiting neurogenesis. 26 Moreover, hypersecretion of CRF and cortisol was also associated with decreased hippocampal volume, particularly in individuals exposed to childhood trauma. 27 Because the hippocampus is involved in the regulation of the HPA axis, it is conceivable that patients with major depression and early life trauma who exhibit reduced hippocampal volume 28 , 29 may also exhibit decreased hippocampal function, therefore resulting in further sensitization of stress responses. 5 These observations support previous reports that associated the origin of depressive symptoms with decreased expression of GRs at the hypothalamic and hippocampal levels, 24 with the resulting hypercortisolism. Hence, an increasing body of evidence supports the association between chronic stress and depression at the molecular level, where hyperactivity of the HPA axis, with the consequent increase of cortisol, represents one of the most consistent findings in both syndromal mood and certain anxiety disorders. 23 , 26

Various studies have focused on genes involved in the regulation of the HPA system, including both the mineralocorticoid receptor and GR genes, resulting in the identification of different single-nucleotide polymorphisms (SNPs). Among these, two different SNPs in the GR gene (BclI and Asp363Ser) have been associated with increased vulnerability for depression in the general population, probably through increased glucocorticoid sensitivity. 30 More recently, various studies have focused on the FK506-binding protein FKBP5, a cochaperone of hsp-90 involved in the regulation of GR sensitivity, 31 which is also involved in HPA axis responsivity. This protein is a component of the GR heterocomplex, which, upon binding of cortisol, is replaced by FKBP4, which in turn facilitates the nuclear translocation of the hormone-receptor complex and its transcriptional activity. 32 Altered GR function may lead to impaired feedback regulation, with the resulting HPA hyperactivation commonly observed in chronic stress and depression. Therefore, various SNPs have been identified in the FKBP5 gene, some of them associated with increased FKBP5 protein expression, which in turn may lead to changes in GR, with the resulting effect on HPA axis regulation. 32 Increased FKBP5 protein expression may reduce hormone-binding affinity and may interfere with the translocation of the hormone-receptor complex. It is noteworthy that glucocorticoids may induce increased expression of this cochaperone, constituting an intracellular negative feedback loop to regulate GR activity. 33 One of the SNPs of the FBPP5 gene, defined as the substitution of a cytosine (C) by a thymine (T) and therefore identified as the high-induction allele T, was associated with increased FKBP5 protein expression and altered HPA response. Upon exposure to stressful stimuli, carriers of the T allele exhibited slower recovery of the cortisol response and homozygous carriers of the allele who experienced severe abuse during childhood presented increased vulnerability for the development of depression during adulthood, 34 which may also be associated with having an increased number of depressive episodes. 32

Role of CRF

CRF-containing circuits in the CNS play a critical role in the coordination of the stress response, both as a neuroendocrine factor regulating the HPA axis and through its function as a neurotransmitter, mediating behavioral, immune, and autonomic responses to stress. 35 CRF neurons are localized throughout different cortical areas, participating in neural pathways involved in cognitive responses, and limbic areas such as the central nucleus of the amygdala and the bed nucleus of the stria terminalis, where it participates in the regulation of emotional responses. 23 CRF projections from the amygdala have been shown to reach the hypothalamic paraventricular nucleus (therefore enhancing the activation of the HPA axis in response to stress) and the monoaminergic nuclei in the brainstem, including the locus coeruleus (LC) and the raphe nuclei (RN). 3 Moreover, CRF stimulates norepinephrine release in the LC, 36 with the consequent noradrenergic activation of the autonomic nervous system and the HPA axis, while mainly inhibiting serotonergic neurons in the RN, 37 which in turn may affect other structures through serotonergic projections to the amygdala, hippocampus, and paraventricular nucleus. 3 Therefore, through the regulation of these monoaminergic systems, CRF participates in neurobiological processes underlying mood and anxiety disorders, producing anxiogenic and depressogenic effects. 35 Increased CSF concentrations of CRF have consistently been reported in depressed and suicidal patients. 38 In addition, CRF may also be involved in anxiety and the encoding of emotional memories, 23 , 35 playing a critical role in the stress response not only during adulthood but also in mediation of the long-lasting effects of trauma and other early life stressful experiences. Moreover, increased levels of CRF may also be involved in neuroplastic changes induced by chronic stress, 39 and this effect may also be enhanced by glucocorticoids as a component of the stress response. 40

Various studies have focused on CRF, CRF-binding protein, and CRF type 1 receptor (CRHR1) genes, resulting in several important findings. 41 Indeed, several SNPs in the CRHR1 and haplotypes formed by certain SNPs involved in mediating the effects of early adverse experiences on the risk for adult depression have been identified. 42 Upon binding to CRF, this receptor participates in the activation of the HPA axis and plays a critical role in emotional and cognitive functions mediated by CRF in extrahypothalamic brain regions, including the amygdala and the LC, 35 therefore influencing arousal, attention, conscious perception of emotional experiences, and memory consolidation. Two haplotypes formed by different SNPs in the CRHR1 gene were associated with reduced symptoms of depression in subjects exposed to early stressful experiences. Because CRHR1 may be critically involved in the consolidation of emotionally charged memories, such as those produced by childhood aversive experiences, it was proposed that carriers of two copies of these haplotypes, which also exhibited overrepresentation of the protective alleles of the studied SNPs, 42 may have altered activation of memory consolidation processes. This may lead to decreased emotional influence in the cognitive processing of these memories, therefore protecting the individual from his or her potentially depressogenic and anxiogenic effects. 43

Role of Serotonin

The serotonergic hypothesis of depression posits deficient serotonergic activity in the CNS with increased vulnerability for the development of depression. The main groups of serotonergic neurons in the CNS are located within the boundaries of the RN, where an array of ascending projections arise from the dorsal RN (B6 and B7) and the medial RN (B8). The dorsal RN–forebrain tract projects to the PFC, amygdala, nucleus accumbens, and ventral hippocampus, among other forebrain structures, 44 and it participates in the state of anticipatory anxiety and thus plays an adaptive role during stressful situations. 45 The dorsal RN–forebrain tract has been associated with activation of the limbic structures (e.g., the amygdala) in the presence of environmental stressors associated with unpleasant experiences, and it is also involved in the regulation of potential emotional reactions. Alterations of this system, particularly involving dorsal RN–amygdala projections, may be associated with symptoms of anxiety. 45 The medial RN–forebrain tract projects to the dorsal hippocampus and hypothalamus, among other neural structures, 44 , 45 and it participates in conferring tolerance to unpleasant, unavoidable, and persistent aversive stimuli such as those perceived during chronic stressful situations. The medial RN–forebrain tract is also associated with adaptive control on negative emotional experiences. Therefore, alterations of this system, particularly involving medial RN–hippocampal projections, may be associated with decreased tolerance to aversive stimuli, learned helplessness, and subsequent depression. 45 , 46 Serotonergic neurons in the RN are also interconnected and are physiologically integrated with other monoaminergic systems in the brainstem, including noradrenergic and dopaminergic circuits. 47 It has been shown that both the dorsal RN and the medial RN receive noradrenergic projections, 48 which appear to be excitatory. The LC receives serotonergic projections from the RN reciprocally, 48 which appear to exert an indirect modulatory effect by inhibiting glutamatergic activation of the LC. The dorsal RN also modulates dopaminergic activity through projections to the ventral tegmental area, which appear to be excitatory, 49 and dopaminergic projections to the dorsal RN reciprocally exert an indirect inhibitory effect by increasing the activity of somatodendritic 5-hydroxytryptamine (serotonin [5-HT]) autoreceptors. 44

Figure 2 illustrates the network of functional connections between different neurotransmitter systems in the CNS, as well as their respective connections with different cortical and limbic structures involved in the stress response.

FIGURE 2. Schematic Representation of Neurotransmitter Systems Involved in the Stress Response and Regulation of Emotional and Cognitive Functions a

a The raphe nuclei send serotonergic projections from their medial component to the hippocampus and from their dorsal component to the amygdala and the DLPFC. The locus coeruleus sends noradrenergic projections to the hippocampus and the amygdala. The ventral tegmental area sends dopaminergic projections to the nucleus accumbens and the DLPFC. The nucleus accumbens is reciprocally connected with the amygdala and the OFC, which in turn is reciprocally connected with the medial prefrontal cortex and the ACC. All of these are reciprocally connected with the amygdala and with the DLPFC. Reciprocal connections between the raphe nuclei, the locus coeruleus, and the ventral tegmental area are also represented. ACC, anterior cingulate cortex; D, dorsal; DLPFC, dorsolateral prefrontal cortex; M, medial; OFC, orbitofrontal cortex.

At the molecular level, 5-HT is released into the synaptic cleft, where it binds to both presynaptic and postsynaptic receptors. A growing number of 5-HT receptors have been identified, including 14 different types, classified in seven families with various subtypes each. Each of the serotonin receptor subtypes exhibits a unique regional neuroanatomic distribution, conferring specificity on the effects of activation of this widespread and diffuse serotonergic innervation. Synaptic concentrations of 5-HT are regulated by the serotonin transporter (5-HTT), which is responsible for its reuptake, therefore regulating its availability to bind and activate specific 5-HT receptors. 47 The 5-HTT is believed to be the primary molecular target of selective serotonin reuptake inhibitors antidepressants. Hence, 5-HTT blockade by selective serotonin reuptake inhibitors is translated into higher 5-HT concentrations in the synaptic cleft, allowing increased activation of 5-HT receptors. 46 , 47 The clinical efficacy of antidepressants is not directly associated with this acute mechanism; instead, it is linked to more adaptive changes. Continuous administration of selective serotonin reuptake inhibitors leads to desensitization or down-regulation of somatodendritic 5-HT 1A autoreceptors in the RN after several days (which are known to moderate the release of 5-HT into the synaptic cleft) and up-regulation of postsynaptic 5-HT 1A and desensitization of 5-HT 2A receptors. 50

In addition to serotonergic projections directly involved in cognitive and emotional functions, projections from the RN have been shown to innervate CRF-containing neurons in the paraventricular nucleus. 51 There is evidence that these projections stimulate the HPA axis and the autonomic nervous system; glucocorticoids and catecholamines may reciprocally affect the serotonergic system during stressful situations. 46 Various studies have shown that postsynaptic 5-HT 1A receptors in different limbic structures may be down-regulated or desensitized by glucocorticoids or exposure to chronic stress. 52 , 53 In addition, it has been shown that cortisol may increase 5-HT uptake in vitro, an effect attributed to increased expression of the 5-HTT gene by the glucocorticoid, 54 therefore providing further support for the reciprocal regulation of the HPA and 5-HT systems and their potential interplay in the interface between stress and depression. 46

Various studies have also focused on the structure of the 5-HTT gene, in which a polymorphism was identified in its promoter region. 55 The promoter activity is regulated by sequence elements located in the upstream regulatory region, known as the 5-HTT gene-linked polymorphic region (5-HTTLPR), where a short (S) and a long (L) allele have been identified. 6 Hence, the short promoter variant (5-HTTLPR-S) was associated with decreased transcriptional efficiency compared with the long allele (5-HTTLPR-L), resulting in decreased expression of the 5-HTT gene, 55 which may affect the modulation of serotonergic activity in response to stress. This notion has been supported by multiple clinical and preclinical studies, 56 including evidence observed in functional brain imaging studies, in which carriers of the S allele (homozygous or heterozygous for the short allele) exhibited increased amygdala reactivity to fearful and threatening stressors compared with those homozygous for the L allele, 57 which suggests that variations in the 5-HTT gene may be involved in psychological responses to stress. 6 Although various studies have shown increasing evidence that this polymorphism moderates the relationship between stress and depression, 56 there are still other studies suggesting certain controversy around this hypothesis.

The amygdala participates in the regulation of emotional reactions to stressful events, and its increased reactivity was associated with anxiety and altered mood regulation. 14 Hence, a potential association between 5-HTT gene polymorphism and increased reactivity of the amygdala in response to negative stressors 58 may contribute to a better understanding of the potential effect of the molecular mechanisms underlying this association. Moreover, the amygdala also plays a critical role in the activation of the HPA axis, and hyperactivation of the amygdala may also lead to increased plasma levels of cortisol. Indeed, carriers of the S allele exhibit increased activation of the amygdala and elevated cortisol levels in response to a laboratory stressor. 11 The association between the 5-HTTLPR-S variation and a potentially decreased expression of the 5-HTT gene may appear paradoxical, considering the potential vulnerability attributed to 5-HTTLPR-S carriers. Therefore, it is conceivable that alterations in 5-HTT gene regulation (and consequent effects on synaptic 5-HT levels) may differ, with the former expressed as a result of constitutive conditions and the latter triggered by environmental factors. It has been proposed that 5-HTTLPR-S carriers may exhibit “essentially” increased concentrations of 5-HT, which may result in down-regulation of postsynaptic 5-HT receptors. This may lead to a relative desensitization of the serotonergic system, 58 providing a potential explanation for the vulnerability exhibited by 5-HTTLPR-S carriers. By contrast, up-regulation of the 5-HTT gene, associated with the effect of environmental stressors and the resulting hyperactivation of the HPA axis and hypercortisolism, may lead to increased 5-HT reuptake and decreased concentrations of 5-HT in the synaptic cleft, 54 which has been widely associated with the development of mood disorders.

Role of Dopamine

Dopamine has also been implicated in the neural mechanisms of stress responses, including stress-related regulation of the HPA axis, as well as in the pathophysiology of depression. 59 , 60 The main groups of dopaminergic neurons in the CNS comprise the retro-rubro field (A8), the substantia nigra pars compacta (A9), and the ventral tegmental area (A10), where the mesolimbic and mesocortical pathways arise. The mesolimbic pathway projects mainly to the nucleus accumbens and other limbic structures, including the amygdala, hippocampus, bed nucleus of the stria terminalis, and septum. This pathway is implicated in the processing and reinforcement of rewarding stimuli, motivation, and the subjective experience of pleasure. 59 The mesocortical pathway projects mainly to the PFC, ACC, and entorhinal cortex and is critically involved in cognitive functions such as concentration and working memory. 59

Environmental stressors provoke increased activity in the amygdala, which in turn may increase the concentrations of dopamine in the mesocortical pathway (particularly in the PFC), therefore conferring exaggerated salience to relatively mild negative stimuli 60 and contributing to the resulting negative bias in cognitive processing. Regarding the mesolimbic pathway, it has been shown that stressful events may induce opposite responses, depending on the potential controllability of the stimuli, 61 and the consequent subjective assessment. Therefore, exposure to acute and controllable stressors was associated with increased dopamine release in the ventral striatum, whereas exposure to chronic and uncontrollable stressful stimuli was associated with decreased dopaminergic activity 61 with resulting anhedonia. Moreover, it has been shown that unavoidable or uncontrollable stressors may lead to decreased dopamine release in the nucleus accumbens and impaired response to environmental stimuli, which may result in the expression and exacerbation of depressive symptoms induced by stress. 62 The inability to experience pleasure, associated with loss of interest and motivation in usual activities, constitutes the pathognomonic anhedonia exhibited by patients with depression, 59 , 60 and it has been shown that impaired dopaminergic function is critically involved in altered reward processing underlying anhedonia. 63 , 64 Moreover, the mesolimbic dopaminergic pathway, particularly the nucleus accumbens, participates in the processing of rewarding and hedonic experiences in association with the orbitofrontal cortex, which may be involved in the subjective assessments of hedonic and rewarding value. 65 The orbitofrontal cortex is connected with the ACC and dorsolateral PFC, where this emotional input participates in cognitive processes; by contrast, the nucleus accumbens receives dopaminergic projections from the ventral tegmental area, which may be enhanced by glutamatergic stimulation from the amygdala, to increase motivation. 65 Substantial interaction has also been described between the ventral tegmental area and the RN, 59 which may be critically involved in emotional processing.

Because increased dopamine release in the mesolimbic pathway has been observed not only in response to rewarding stimuli but also in the presence of aversive situations (particularly when these are perceived as controllable and escapable 61 ), it has been suggested that dopamine plays an adaptive role associated with motivation, increased arousal, and behavioral control in response to stress, including both appetitive and aversive conditions. 66

Role of Norepinephrine

Catecholamines (and more specifically norepinephrine) have long been posited to play a major role in the pathophysiology of affective disorders, forming the catecholamine hypothesis of depression. The main group of norepinephrine-containing neurons in the CNS is located within the LC (A6), where various projections arise to widely innervate cortical and subcortical areas, 48 including the amygdala, the hippocampus, and the paraventricular nucleus of the hypothalamus. 36 Projections from the LC to the ventral tegmental area have been described, in which norepinephrine has been shown to potentiate dopamine release. Projections from the LC to the RN have also been described, in which norepinephrine exerts regulatory effects on 5-HT release. 48 There is also evidence of reciprocal regulation between norepinephrine and 5-HT, not only through connections between both aminergic systems but also through limbic structures such as the hippocampus. 67 In addition, reciprocal connections between norepinephrine - and CRF-containing neurons suggest a critical role of the LC in the regulation of neural and neuroendocrine responses to stress. 36

In response to acute stressors, norepinephrine is released throughout different structures in the CNS, resulting in enhanced arousal and hypervigilance, in the context of adaptive responses to stress. Moreover, activation of the LC has been associated with subsequent stimulation of the lateral hypothalamus, which in turn participates in the activation of the sympathetic branch of the autonomic nervous system, therefore complementing the adaptive response to stress. 36 A potential dysfunction of the LC has been observed during chronic stress (particularly upon exposure to unavoidable or uncontrollable stressors), leading to altered norepinephrine release, which was associated with some features of learned helplessness as well as problems in cognitive functions such as attention and memory, which are frequently observed in depression. In addition, dysregulation of the norepinephrine system has also been described in altered states of arousal, 48 which is commonly observed in anxiety disorders as well as in depression.

Neuroplasticity and Neurogenesis: Role of Neurotrophic Factors

Several studies have focused on the potential role of neurotrophic factors in critical neural processes, with particular attention on the neurotrophin family, which is composed of nerve growth factor, brain-derived neurotrophic factor (BDNF), neurotrophin-3, neurotrophin-4/5, and neurotrophin-6. Among these neurotrophins, a growing body of research has focused on the role of BDNF in the regulation of brain development, neuroplasticity, and neurogenesis. 68 Various studies strongly suggest that decreased levels of BDNF may lead to depressive symptoms, whereas up-regulation of BDNF is associated with clinical recovery. 69 In vitro studies have demonstrated that BDNF may decrease 5-HT uptake, suggesting a potential role of the neurotrophin in regulation of 5-HTT. 70 Chronic stress, with the resulting activation of the HPA axis, may damage neurons in certain CNS structures (particularly in the hippocampus, where high levels of GRs have been found) and these changes have been associated with decreased availability of neurotrophic factors such as BDNF. 71 Moreover, it has been shown that increased levels of glucocorticoids, at least partially, may be involved in down-regulation of BDNF. 72 By contrast, it has been demonstrated that various antidepressants increase the expression of BDNF in the hippocampus 69 in a dose-dependent and time-dependent manner, which is consistent with the time dependency of therapeutic effects of antidepressants, therefore suggesting a role for BDNF in their mechanism of action. 73 The potential association between successful pharmacotherapy and the observed up-regulation of BDNF in the hippocampus suggests that BDNF may be involved in the long-lasting effects of antidepressants through neuroplastic changes in certain neural structures such as the hippocampus, amygdala, and PFC. 69 Moreover, it has been shown that BDNF and 5-HT may induce hippocampal neurogenesis. 74

Most neurons in the CNS are generated during early periods of development, although more recent studies have demonstrated that some neural structures, such as the dentate gyrus of the hippocampus, actually continue generating neurons later in life. 75 Therefore, neurogenesis in the adult CNS may be stimulated by special conditions, particularly those related to enhanced hippocampal activity and increased levels of 5-HT, 76 , 77 but it may be inhibited by stressful situations and increased levels of glucocorticoids. 78 Under chronic stress conditions, with increased activation of the HPA axis, inhibition of hippocampal neurogenesis may interfere with the formation of new cognitions, therefore contributing to provoking and sustaining ongoing depressogenic conditions. According to this hypothesis, successful therapeutic interventions may require recovery of the normal rate of hippocampal neurogenesis. This recovery may be associated with a direct effect of antidepressants through increasing levels of 5-HT 75 or indirectly through modulation of the HPA axis and increasing levels of BDNF, which was associated with up-regulation of neuroplasticity and increasing neurogenesis. This hypothesis remains quite controversial because of failure to confirm the increase in neurogenesis after long-term antidepressant treatment. 79

Various studies have focused on BDNF gene regulation and variations potentially involved in mood disorders, resulting in the identification of different SNPs. Among these, an SNP has been identified at nucleotide position 196 in the coding region of the BDNF gene, where a guanine (G) is replaced by an adenine (A), resulting in the substitution of valine (Val) by methionine (Met) at codon 66, which is thus termed Val66Met. This is where the presence of a Met allele has been associated with a functional alteration (i.e., abnormal intracellular trafficking and decreased secretion of BDNF). 73 , 76 Studies on carriers of the Met-BDNF allele revealed relatively smaller hippocampal volumes compared with those individuals who were homozygous for the Val-BDNF allele. 73 This was also associated with reduced hippocampal activation and deficient cognitive performance, 12 , 73 which have also been associated with lower emotional stability and increased vulnerability for the development of depressive symptoms.

Inflammatory Processes: Role of Cytokines

It has been demonstrated that acute and chronic psychosocial stress may activate inflammatory responses. 80 Increased blood concentrations of proinflammatory cytokines, such as interleukin-1, interleukin-6, and tumor necrosis factor-alpha, have been associated with the effect of diverse environmental stimuli, including psychosocial stress, 81 and this immune activation has also been observed in major depression. 82 Moreover, major depression may induce increased inflammatory responses to stress, and this has been observed mostly in patients exposed to adverse early life events, therefore suggesting a link between these and increased inflammatory responses to stress later in life. 80 To understand the role of proinflammatory cytokines in chronic stress and the subsequent development of depression, various studies have focused on their potential mechanisms of action. Environmental stressors activate the sympathetic branch of the autonomic nervous system, with the resulting release of catecholamines, which in turn activates their receptors on immune cells and thus stimulates the release of proinflammatory cytokines. 83 Chronic inflammatory responses in the CNS may result in excessive release of proinflammatory cytokines, which in turn may lead to decreased concentrations of neurotrophins (including BDNF), leading to impaired neuroplasticity 83 and decreased neurogenesis (particularly in the hippocampus 82 ), which have been associated with the origin of cognitive impairment and mood disorders. Proinflammatory cytokines have also been involved in regulation of the HPA axis, stimulating release of CRF with resulting hypercortisolism, 83 which has been associated with reduced sensitivity of GRs and glucocorticoid resistance. 81 , 83 Increased levels of cortisol, such as those observed during chronic stress, may lead to decreased synthesis of 5-HT due to reduced activity of the rate-limiting enzyme tryptophan hydroxylase. Hypercortisolism has been also associated with increased activity of tryptophan dioxygenase (indoleamine-pyrrole 2,3-dioxygenase), which is responsible for the degradation of tryptophan to kynurenine, with the resulting decreased synthesis and release of 5-HT. 83 Proinflammatory cytokines such as interferon have also been involved in the modulation of this pathway, stimulating indoleamine-pyrrole 2,3-dioxygenase and thus leading to reduced synthesis of 5-HT and increased synthesis of kynurenine. 84 Degradation of kynurenine leads to the formation of 3-hydroxykynurenine, which produces free radical species involved in oxidative stress, and kynurenic acid and quinolinic acid, which activate the glutamatergic system. This leads to neurotoxicity and neuronal apoptosis, which are also involved in the pathophysiology of depression. 83 , 84 In addition, certain proinflammatory cytokines, such as interleukin-1 and tumor necrosis factor, have been shown to affect serotonergic neurotransmission by stimulating the 5-HTT and thus reducing intersynaptic concentrations of 5-HT in the CNS. 83 , 85

Understanding the molecular mechanisms underlying neuroinflammatory processes in the CNS, particularly the role played by proinflammatory cytokines in mood disorders, has inspired various studies aimed at improving depressive symptoms by attenuating these processes. Preclinical studies have demonstrated the efficacy of certain anti-inflammatory cytokines to block the depressive-like state induced by proinflammatory cytokines in rodents. 83 Other studies have also approached the consequences of proinflammatory cytokines, antagonizing the activity of the glutamatergic system, activated by the kynurenine pathway. 81

Stress, Appraisal, and Coping: Role of Psychological Vulnerability

Psychological vulnerability depends on various features related to stressful life events (including strength, intensity, and length of the impact) and the availability of personal resources to cope with them. More remarkably, however, it may depend on cognitive appraisal, particularly the balance between stressors and individual resources, and the resulting coping strategies. 86 Chronic exposure to unavoidable and uncontrollable stressors may lead to decreasing cognitive and behavioral coping strategies to handle environmental events, mostly as a result of cognitive appraisals that personal resources are not enough, which has been associated with increasing feelings of helplessness. 86 According to the cognitive model of depression, 87 early life experiences provide the background to develop cognitive schemas, which in turn represent the basis to transform simple data into cognitions that are learned and stored in long-term memory. Adverse early life events, including childhood sexual or physical abuse 88 and peer victimization 89 (also known as bullying), may contribute to the formation of particular cognitive schemas. These schemas may be inactive during long periods and reactivated by new experiences at a later time, particularly those with strong emotional valence. In response to stressful situations in adulthood, activated dysfunctional schemas may induce negative biases during information processing, with consequent dysfunctional effects, including cognitive processing, emotional reactions, and behavioral responses, constituting the essential core of cognitive vulnerability. 87 Therefore, dysfunctional schemas shaped during childhood, with systematic negative biases, may lead to negatively biased appraisals, with consequent limitations in further processing of the resulting cognitions, therefore leading to feelings of helplessness and subsequent depression.

Epigenetics: Role of Gene–Environment Interactions

The term epigenetics refers to heritable characteristics that are not determined by structural changes in the underlying genetic sequence. At the molecular level, epigenetic mechanisms involve biochemical changes of nucleotides, without altering the DNA sequence, and the associated histone proteins, which constitute chromatin. Changes in the structure of chromatin may affect gene expression by allowing transcription factors to gain access to gene regulatory elements. Hence, environmental factors may induce changes in the chromatin state, which in turn may improve exposure of genes to the impact of different transcription factors, therefore increasing or decreasing gene expression while the original DNA sequence remains unaltered. 90 Potential changes include DNA methylation, which has been associated with down-regulation of gene expression; histone acetylation, which may induce up-regulation of gene expression; and histone methylation and phosphorylation, both of which may lead to activation or repression of transcriptional events. 90 Recent research has contributed to identifying epigenetic mechanisms in the context of stressful situations, which may induce long-lasting changes in gene expression in different neural structures. In turn, such changes have been associated with the development of stress-related conditions such as anxiety disorders and depression. Preclinical studies have revealed that chronic stress may regulate histone acetylation in the hippocampus, inducing transient increases and subsequent decreases; transient increases have also been observed in the amygdala. 91

In addition, preclinical studies also revealed that increased levels of CRF, observed during chronic stress conditions, have been associated with decreased DNA methylation at the promoter region of the CRF gene. 92 Moreover, a history of early adverse experiences has been associated with changes in histone markers and DNA methylation of the GR gene, particularly in the hippocampus, and changes in DNA methylation have also been observed in the GR and BDNF genes. 41 Therefore, chronic stress, including early stressful experiences, may induce diverse epigenetic changes in different neural structures, with a subsequent effect on their respective functions. This, in turn, may predispose individuals to increased vulnerability to stress and to the development of diverse clinical conditions such as depression.

Childhood Trauma: Role of Early Adverse Experiences

Early life stress, defined as adverse conditions and traumatic events experienced during childhood, represents a major factor of vulnerability in the origin and development of depression and bipolar disorder. 3 , 5 , 27 The association between a history of adverse and traumatic experiences during childhood and the development of mood disorders later in life has been observed particularly after additional stressful events during adulthood. 5 It has been shown that adverse early life events (including abuse, neglect, or loss) contribute to the formation of dysfunctional cognitive schemas, which may induce negative biases in response to stressful situations at a later time, therefore contributing to generating cognitive vulnerability. 56 This mechanism was also recently described in victims of bullying. 89 Moreover, it has been proposed that certain early life events, such as neglect, may lead to the formation of dysfunctional attitudes; this has also been associated with long-term hyperactivity of the HPA axis. 93 The effect of adverse early life events has been conclusively demonstrated to induce long-lasting changes in neural and neuroendocrine systems involved in adaptive responses to stress, particularly in CRF neurotransmission. 23 This, in turn, may be translated into persistent sensitization and increased responsiveness to stress. 3 , 5 Increased levels of CRF may lead to hyperactivity of the HPA axis and hypercortisolism, which may induce morphologic changes such as reduced hippocampal volume. 72 In this regard, various studies have focused on the role of hippocampal GRs, and increased levels of cortisol (in a sustained and prolonged manner) have been shown to induce down-regulation of GRs in certain areas of the hippocampus. 94 Moreover, additional research has suggested that the availability and efficacy of hippocampal GRs may be permanently affected as a result of early stressful experiences, 88 therefore contributing to glucocorticoid resistance and the consequent hyperreactivity of the HPA axis observed in response to additional stressful situations. In addition, increased concentrations of cortisol and decreased GR availability, induced by stressful situations during childhood, have been associated with decreased hippocampal volume and neural activity in adulthood as well as increased reactivity of the HPA axis, with the consequent functional alterations observed in adulthood. 88 , 95 A history of early life adverse experiences was also associated with hyperreactivity of neural and neuroendocrine responses to stress, which is reflected through increased CRF activity, hypercortisolism, and glucocorticoid resistance. 27 , 96

Klengel et al. 97 reported that a polymorphism in the FKBP5 gene increases the risk for the development of stress-related psychiatric disorders in adults by an allelic-specific, child abuse/neglect–dependent DNA demethylation in functional glucocorticoid response elements of FKBP5. Thus, activation of a sensitized system in the presence of additional stressful situations later in life may result in an exaggerated and maladaptive activation of the stress response, therefore generating increased vulnerability to the development of depressive symptoms upon exposure to additional stressors in adulthood. 42 , 98

Conclusions

The role of stressful life events in the origin and development of depression may be conceptualized as the result of multiple interactions between the effect of environmental stressors and individual factors of vulnerability. Figure 3 illustrates the role of these factors and their potential interactions at the interface between chronic stress and depression. Genetic factors, including SNPs, may be associated with functional and structural alterations in certain neural structures, including increased reactivity of the amygdala and decreased function of the hippocampus. Adverse early life events have been shown to engender biological changes in the developing CNS, as well as psychological changes reflected in the formation of dysfunctional cognitive schemas, 99 with the resulting biased cognitive processing of environmental stimuli, which may be translated into cognitive and emotional vulnerability. 87 These may be further activated in response to stress in adulthood, contributing to increased vulnerability to depression. 27

FIGURE 3. Schematic Representation of Different Factors Involved in the Stress Response and Their Potential Role in Stress and Depression a

a Genetic polymorphisms (represented as genetic vulnerability) participate in the development of the CNS and, together with the influence of early environmental factors (represented by early life stress) and chronic stress, result in a particular CNS phenotype. Early life stress may also induce certain cognitive vulnerability, which in turn may result in emotional vulnerability. Upon the impact of traumatic events or chronic stress, a predisposed CNS responds with increased levels of CRF, hyperactivation of the HPA axis, and increased levels of cortisol, which may lead to molecular changes in different circuits (represented by molecular vulnerability), as well as altered cognitive and emotional responses (represented by emotional vulnerability). This, in turn, may result in increased vulnerability for the development of symptoms of anxiety and depression. CRF, corticotropin-releasing factor; HPA, hypothalamic-pituitary-adrenal.

The impact of abuse and neglect during childhood clearly leads to persistent changes in neural and neuroendocrine systems involved in the regulation of adaptive responses. Functional or structural alterations in the CNS, particularly in the cerebrocortical regions as well as in the amygdala and the hippocampus, along with cognitive biases, may induce biological changes such as increased levels of CRF. Upon exposure to environmental stressors, this mechanism may be translated into hyperactivity of the HPA system, with increased levels of CRF and cortisol, which in turn may lead to transcriptional events. Such molecular changes affecting different aminergic systems, particularly on the regulation of 5-HT together with altered cognitive processing, may result in emotional changes, thereby predisposing to symptoms of anxiety and depression. Therefore, multiple vulnerability factors (including psychological, biological, cognitive, genetic, and epigenetic factors) converge on different aspects of HPA regulation. This complex set of pathways likely links vulnerability to stress with the pathogenesis of depression. In addition, environmental stress has also been associated with inflammatory responses in the CNS with excessive release of proinflammatory cytokines, which may lead to further stimulation of the HPA axis, with resulting hypercortisolism and impaired 5-HT neurotransmission. Proinflammatory cytokines have also been associated with decreased neurotrophins, with resulting decreases in neuroplasticity and neurogenesis. Therefore, a better understanding of the molecular mechanisms underlying these processes may allow novel strategies aimed at improving depressive symptoms by attenuating neuroinflammation.

The observation that some individuals may exhibit stronger vulnerability to environmental stressors but others may be less sensitive, more resistant, or even resilient to similar experiences highlights the importance of further investigation of the nature of different risk factors. Future research should focus on further understanding the neurobiological background underlying these factors and should identify potential windows of intervention, including neural and molecular mechanisms involved in the interface between cognitive processing of environmental stressors and their potential effects in epigenetic processes. This may lead to the development of more successful treatments aimed at not only restoring altered neural and neuroendocrine mechanisms but also preventing the development of anxiety and mood disorders in vulnerable individuals.

This may be achieved either by identifying different vulnerability factors, which in turn may become targets for novel therapeutic interventions, or by increasing and promoting protective resources in individuals exposed to stressful conditions, particularly those exposed to traumatic events or adverse conditions during childhood.

Dr. Nemeroff has in the last 3 years consulted to Takeda, Xhale, Mitsubishi, Clintara, Taisho, Prismic, and Gerson Lehrman, has received grants/research support from NIH and the Agency for Healthcare Research and Quality, has served on the scientific advisory boards for Xhale, AFSP, the Brain and Behavior Research Foundation, Clintara, and the Anxiety and Depression Association of America, and holds stock in Celgene, Seattle Genetics, Abbvie, Titan, OPKO, and Xhale. Dr. Tafet reports no financial relationships with commercial interests.

1 Gold PW : The organization of the stress system and its dysregulation in depressive illness . Mol Psychiatry 2015 ; 20:32–47 Crossref , Medline ,  Google Scholar

2 Nemeroff CB : The preeminent role of early untoward experience on vulnerability to major psychiatric disorders: the nature-nurture controversy revisited and soon to be resolved . Mol Psychiatry 1999 ; 4:106–108 Crossref , Medline ,  Google Scholar

3 Heim C, Nemeroff CB : The role of childhood trauma in the neurobiology of mood and anxiety disorders: preclinical and clinical studies . Biol Psychiatry 2001 ; 49:1023–1039 Crossref , Medline ,  Google Scholar

4 López JF, Akil H, Watson SJ : Neural circuits mediating stress . Biol Psychiatry 1999 ; 46:1461–1471 Crossref , Medline ,  Google Scholar

5 Heim C, Newport DJ, Mletzko T, et al. : The link between childhood trauma and depression: insights from HPA axis studies in humans . Psychoneuroendocrinology 2008 ; 33:693–710 Crossref , Medline ,  Google Scholar

6 Caspi A, Sugden K, Moffitt TE, et al. : Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene . Science 2003 ; 301:386–389 Crossref , Medline ,  Google Scholar

7 Kaufman J, Plotsky PM, Nemeroff CB, et al. : Effects of early adverse experiences on brain structure and function: clinical implications . Biol Psychiatry 2000 ; 48:778–790 Crossref , Medline ,  Google Scholar

8 Kaufman J, Yang BZ, Douglas-Palumberi H, et al. : Brain-derived neurotrophic factor-5-HTTLPR gene interactions and environmental modifiers of depression in children . Biol Psychiatry 2006 ; 59:673–680 Crossref , Medline ,  Google Scholar

9 Bradley RG, Binder EB, Epstein MP, et al. : Influence of child abuse on adult depression: moderation by the corticotropin-releasing hormone receptor gene . Arch Gen Psychiatry 2008 ; 65:190–200 Crossref , Medline ,  Google Scholar

10 Nemeroff CB, Seligman F : The pervasive and persistent neurobiological and clinical aftermath of child abuse and neglect . J Clin Psychiatry 2013 ; 74:999–1001 Crossref , Medline ,  Google Scholar

11 Gotlib IH, Joormann J, Minor KL, et al. : HPA axis reactivity: a mechanism underlying the associations among 5-HTTLPR, stress, and depression . Biol Psychiatry 2008 ; 63:847–851 Crossref , Medline ,  Google Scholar

12 Gatt JM, Nemeroff CB, Dobson-Stone C, et al. : Interactions between BDNF Val66Met polymorphism and early life stress predict brain and arousal pathways to syndromal depression and anxiety . Mol Psychiatry 2009 ; 14:681–695 Crossref , Medline ,  Google Scholar

13 Binder EB, Owens MJ, Liu W, et al. : Association of polymorphisms in genes regulating the corticotropin-releasing factor system with antidepressant treatment response . Arch Gen Psychiatry 2010 ; 67:369–379 Crossref , Medline ,  Google Scholar

14 LeDoux JE : Brain mechanisms of emotion and emotional learning . Curr Opin Neurobiol 1992 ; 2:191–197 Crossref , Medline ,  Google Scholar

15 Ghashghaei HT, Hilgetag CC, Barbas H : Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala . Neuroimage 2007 ; 34:905–923 Crossref , Medline ,  Google Scholar

16 Drevets WC, Savitz J, Trimble M : The subgenual anterior cingulate cortex in mood disorders . CNS Spectr 2008 ; 13:663–681 Crossref , Medline ,  Google Scholar

17 Price JL, Drevets WC : Neural circuits underlying the pathophysiology of mood disorders . Trends Cogn Sci 2012 ; 16:61–71 Crossref , Medline ,  Google Scholar

18 Etkin A, Egner T, Kalisch R : Emotional processing in anterior cingulate and medial prefrontal cortex . Trends Cogn Sci 2011 ; 15:85–93 Crossref , Medline ,  Google Scholar

19 Ray RD, Zald DH : Anatomical insights into the interaction of emotion and cognition in the prefrontal cortex . Neurosci Biobehav Rev 2012 ; 36:479–501 Crossref , Medline ,  Google Scholar

20 Motzkin JC, Philippi CL, Wolf RC, et al. : Ventromedial prefrontal cortex is critical for the regulation of amygdala activity in humans . Biol Psychiatry 2015 ; 77:276–284 Crossref , Medline ,  Google Scholar

21 Zhong M, Wang X, Xiao J, et al. : Amygdala hyperactivation and prefrontal hypoactivation in subjects with cognitive vulnerability to depression . Biol Psychol 2011 ; 88:233–242 Crossref , Medline ,  Google Scholar

22 Heim CM, Mayberg HS, Mletzko T, et al. : Decreased cortical representation of genital somatosensory field after childhood sexual abuse . Am J Psychiatry 2013 ; 170:616–623 Crossref , Medline ,  Google Scholar

23 Arborelius L, Owens MJ, Plotsky PM, et al. : The role of corticotropin-releasing factor in depression and anxiety disorders . J Endocrinol 1999 ; 160:1–12 Crossref , Medline ,  Google Scholar

24 Holsboer F : The corticosteroid receptor hypothesis of depression . Neuropsychopharmacology 2000 ; 23:477–501 Crossref , Medline ,  Google Scholar

25 Chrousos GP : Stress and disorders of the stress system . Nat Rev Endocrinol 2009 ; 5:374–381 Crossref , Medline ,  Google Scholar

26 Baumeister D, Lightman SL, Pariante CM : The interface of stress and the HPA axis in behavioural phenotypes of mental illness . Curr Top Behav Neurosci 2014 ; 18:13–24 Crossref , Medline ,  Google Scholar

27 Nemeroff CB, Binder E : The preeminent role of childhood abuse and neglect in vulnerability to major psychiatric disorders: toward elucidating the underlying neurobiological mechanisms . J Am Acad Child Adolesc Psychiatry 2014 ; 53:395–397 Crossref , Medline ,  Google Scholar

28 Bremner JD, Narayan M, Anderson ER, et al. : Hippocampal volume reduction in major depression . Am J Psychiatry 2000 ; 157:115–118 Link ,  Google Scholar

29 Vythilingam M, Heim C, Newport J, et al. : Childhood trauma associated with smaller hippocampal volume in women with major depression . Am J Psychiatry 2002 ; 159:2072–2080 Crossref , Medline ,  Google Scholar

30 DeRijk RH, Schaaf M, de Kloet ER : Glucocorticoid receptor variants: clinical implications . J Steroid Biochem Mol Biol 2002 ; 81:103–122 Crossref , Medline ,  Google Scholar

31 Horstmann S, Lucae S, Menke A, et al. : Polymorphisms in GRIK4, HTR2A, and FKBP5 show interactive effects in predicting remission to antidepressant treatment . Neuropsychopharmacology 2010 ; 35:727–740 Crossref , Medline ,  Google Scholar

32 Binder EB, Salyakina D, Lichtner P, et al. : Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment . Nat Genet 2004 ; 36:1319–1325 Crossref , Medline ,  Google Scholar

33 Vermeer H, Hendriks-Stegeman BI, van der Burg B, et al. : Glucocorticoid-induced increase in lymphocytic FKBP51 messenger ribonucleic acid expression: a potential marker for glucocorticoid sensitivity, potency, and bioavailability . J Clin Endocrinol Metab 2003 ; 88:277–284 Crossref , Medline ,  Google Scholar

34 Appel K, Schwahn C, Mahler J, et al. : Moderation of adult depression by a polymorphism in the FKBP5 gene and childhood physical abuse in the general population . Neuropsychopharmacology 2011 ; 36:1982–1991 Crossref , Medline ,  Google Scholar

35 Nemeroff CB : Neurobiological consequences of childhood trauma . J Clin Psychiatry 2004 ; 65(Suppl 1):18–28 Medline ,  Google Scholar

36 Valentino RJ, Foote SL, Page ME : The locus coeruleus as a site for integrating corticotropin-releasing factor and noradrenergic mediation of stress responses . Ann N Y Acad Sci 1993 ; 697:173–188 Crossref , Medline ,  Google Scholar

37 Kirby LG, Rice KC, Valentino RJ : Effects of corticotropin-releasing factor on neuronal activity in the serotonergic dorsal raphe nucleus . Neuropsychopharmacology 2000 ; 22:148–162 Crossref , Medline ,  Google Scholar

38 Nemeroff CB, Widerlöv E, Bissette G, et al. : Elevated concentrations of CSF corticotropin-releasing factor-like immunoreactivity in depressed patients . Science 1984 ; 226:1342–1344 Crossref , Medline ,  Google Scholar

39 Regev L, Baram TZ : Corticotropin releasing factor in neuroplasticity . Front Neuroendocrinol 2014 ; 35:171–179 Crossref , Medline ,  Google Scholar

40 Timmermans W, Xiong H, Hoogenraad CC, et al. : Stress and excitatory synapses: from health to disease . Neuroscience 2013 ; 248:626–636 Crossref , Medline ,  Google Scholar

41 McEwen BS, Eiland L, Hunter RG, et al. : Stress and anxiety: structural plasticity and epigenetic regulation as a consequence of stress . Neuropharmacology 2012 ; 62:3–12 Crossref , Medline ,  Google Scholar

42 Kendler KS, Kuhn JW, Prescott CA : Childhood sexual abuse, stressful life events and risk for major depression in women . Psychol Med 2004 ; 34:1475–1482 Crossref , Medline ,  Google Scholar

43 Polanczyk G, Caspi A, Williams B, et al. : Protective effect of CRHR1 gene variants on the development of adult depression following childhood maltreatment: replication and extension . Arch Gen Psychiatry 2009 ; 66:978–985 Crossref , Medline ,  Google Scholar

44 Hensler JG : Serotonergic modulation of the limbic system . Neurosci Biobehav Rev 2006 ; 30:203–214 Crossref , Medline ,  Google Scholar

45 Deakin JF, Graeff FG : 5-HT and mechanisms of defence . J Psychopharmacol 1991 ; 5:305–315 Crossref , Medline ,  Google Scholar

46 Tafet GE, Bernardini R : Psychoneuroendocrinological links between chronic stress and depression . Prog Neuropsychopharmacol Biol Psychiatry 2003 ; 27:893–903 Crossref , Medline ,  Google Scholar

47 Hamon M, Blier P : Monoamine neurocircuitry in depression and strategies for new treatments . Prog Neuropsychopharmacol Biol Psychiatry 2013 ; 45:54–63 Crossref , Medline ,  Google Scholar

48 Ressler KJ, Nemeroff CB : Role of norepinephrine in the pathophysiology and treatment of mood disorders . Biol Psychiatry 1999 ; 46:1219–1233 Crossref , Medline ,  Google Scholar

49 Kranz GS, Kasper S, Lanzenberger R : Reward and the serotonergic system . Neuroscience 2010 ; 166:1023–1035 Crossref , Medline ,  Google Scholar

50 Gray NA, Milak MS, DeLorenzo C, et al. : Antidepressant treatment reduces serotonin-1A autoreceptor binding in major depressive disorder . Biol Psychiatry 2013 ; 74:26–31 Crossref , Medline ,  Google Scholar

51 Lesch KP, Gutknecht L : Focus on The 5-HT1A receptor: emerging role of a gene regulatory variant in psychopathology and pharmacogenetics . Int J Neuropsychopharmacol 2004 ; 7:381–385 Crossref , Medline ,  Google Scholar

52 López JF, Chalmers DT, Little KY, et al. : A.E. Bennett Research Award. Regulation of serotonin1A, glucocorticoid, and mineralocorticoid receptor in rat and human hippocampus: implications for the neurobiology of depression . Biol Psychiatry 1998 ; 43:547–573 Crossref , Medline ,  Google Scholar

53 van Riel E, van Gemert NG, Meijer OC, et al. : Effect of early life stress on serotonin responses in the hippocampus of young adult rats . Synapse 2004 ; 53:11–19 Crossref , Medline ,  Google Scholar

54 Tafet GE, Toister-Achituv M, Shinitzky M : Enhancement of serotonin uptake by cortisol: a possible link between stress and depression . Cogn Affect Behav Neurosci 2001 ; 1:96–104 Crossref , Medline ,  Google Scholar

55 Lesch KP, Bengel D, Heils A, et al. : Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region . Science 1996 ; 274:1527–1531 Crossref , Medline ,  Google Scholar

56 Karg K, Burmeister M, Shedden K, et al. : The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: evidence of genetic moderation . Arch Gen Psychiatry 2011 ; 68:444–454 Crossref , Medline ,  Google Scholar

57 Hariri AR, Mattay VS, Tessitore A, et al. : Serotonin transporter genetic variation and the response of the human amygdala . Science 2002 ; 297:400–403 Crossref , Medline ,  Google Scholar

58 Hariri AR, Drabant EM, Munoz KE, et al. : A susceptibility gene for affective disorders and the response of the human amygdala . Arch Gen Psychiatry 2005 ; 62:146–152 Crossref , Medline ,  Google Scholar

59 Dunlop BW, Nemeroff CB : The role of dopamine in the pathophysiology of depression . Arch Gen Psychiatry 2007 ; 64:327–337 Crossref , Medline ,  Google Scholar

60 Nestler EJ, Carlezon WA Jr : The mesolimbic dopamine reward circuit in depression . Biol Psychiatry 2006 ; 59:1151–1159 Crossref , Medline ,  Google Scholar

61 Suridjan I, Boileau I, Bagby M, et al. : Dopamine response to psychosocial stress in humans and its relationship to individual differences in personality traits . J Psychiatr Res 2012 ; 46:890–897 Crossref , Medline ,  Google Scholar

62 Cabib S, Puglisi-Allegra S : The mesoaccumbens dopamine in coping with stress . Neurosci Biobehav Rev 2012 ; 36:79–89 Crossref , Medline ,  Google Scholar

63 Krishnan V, Nestler EJ : Linking molecules to mood: new insight into the biology of depression . Am J Psychiatry 2010 ; 167:1305–1320 Crossref , Medline ,  Google Scholar

64 Bogdan R, Nikolova YS, Pizzagalli DA : Neurogenetics of depression: a focus on reward processing and stress sensitivity . Neurobiol Dis 2013 ; 52:12–23 Crossref , Medline ,  Google Scholar

65 Der-Avakian A, Markou A : The neurobiology of anhedonia and other reward-related deficits . Trends Neurosci 2012 ; 35:68–77 Crossref , Medline ,  Google Scholar

66 Pruessner JC, Champagne F, Meaney MJ, et al. : Dopamine release in response to a psychological stress in humans and its relationship to early life maternal care: a positron emission tomography study using [11C]raclopride . J Neurosci 2004 ; 24:2825–2831 Crossref , Medline ,  Google Scholar

67 Mongeau R, Blier P, de Montigny C : The serotonergic and noradrenergic systems of the hippocampus: their interactions and the effects of antidepressant treatments . Brain Res Brain Res Rev 1997 ; 23:145–195 Crossref , Medline ,  Google Scholar

68 Mitchelmore C, Gede L : Brain derived neurotrophic factor: epigenetic regulation in psychiatric disorders . Brain Res 2014 ; 1586:162–172 Crossref , Medline ,  Google Scholar

69 Duman RS, Monteggia LM : A neurotrophic model for stress-related mood disorders . Biol Psychiatry 2006 ; 59:1116–1127 Crossref , Medline ,  Google Scholar

70 Mössner R, Daniel S, Albert D, et al. : Serotonin transporter function is modulated by brain-derived neurotrophic factor (BDNF) but not nerve growth factor (NGF) . Neurochem Int 2000 ; 36:197–202 Crossref , Medline ,  Google Scholar

71 Ota KT, Duman RS : Environmental and pharmacological modulations of cellular plasticity: role in the pathophysiology and treatment of depression . Neurobiol Dis 2013 ; 57:28–37 Crossref , Medline ,  Google Scholar

72 Suri D, Vaidya VA : Glucocorticoid regulation of brain-derived neurotrophic factor: relevance to hippocampal structural and functional plasticity . Neuroscience 2013 ; 239:196–213 Crossref , Medline ,  Google Scholar

73 Duman RS : Role of neurotrophic factors in the etiology and treatment of mood disorders . Neuromolecular Med 2004 ; 5:11–25 Crossref , Medline ,  Google Scholar

74 Mahar I, Bambico FR, Mechawar N, et al. : Stress, serotonin, and hippocampal neurogenesis in relation to depression and antidepressant effects . Neurosci Biobehav Rev 2014 ; 38:173–192 Crossref , Medline ,  Google Scholar

75 Jacobs BL, van Praag H, Gage FH : Adult brain neurogenesis and psychiatry: a novel theory of depression . Mol Psychiatry 2000 ; 5:262–269 Crossref , Medline ,  Google Scholar

76 Malberg JE, Eisch AJ, Nestler EJ, et al. : Chronic antidepressant treatment increases neurogenesis in adult rat hippocampus . J Neurosci 2000 ; 20:9104–9110 Crossref , Medline ,  Google Scholar

77 Alenina N, Klempin F : The role of serotonin in adult hippocampal neurogenesis . Behav Brain Res 2015 ; 277:49–57 Crossref , Medline ,  Google Scholar

78 Krugers HJ, Lucassen PJ, Karst H, et al. : Chronic stress effects on hippocampal structure and synaptic function: relevance for depression and normalization by anti-glucocorticoid treatment . Front Synaptic Neurosci 2010 ; 2:24 Medline ,  Google Scholar

79 Hanson ND, Nemeroff CB, Owens MJ : Lithium, but not fluoxetine or the corticotropin-releasing factor receptor 1 receptor antagonist R121919, increases cell proliferation in the adult dentate gyrus . J Pharmacol Exp Ther 2011 ; 337:180–186 Crossref , Medline ,  Google Scholar

80 Haroon E, Raison CL, Miller AH : Psychoneuroimmunology meets neuropsychopharmacology: translational implications of the impact of inflammation on behavior . Neuropsychopharmacology 2012 ; 37:137–162 Crossref , Medline ,  Google Scholar

81 Capuron L, Miller AH : Immune system to brain signaling: neuropsychopharmacological implications . Pharmacol Ther 2011 ; 130:226–238 Crossref , Medline ,  Google Scholar

82 Cattaneo A, Macchi F, Plazzotta G, et al. : Inflammation and neuronal plasticity: a link between childhood trauma and depression pathogenesis . Front Cell Neurosci 2015 ; 9:40 Crossref , Medline ,  Google Scholar

83 Leonard BE : The concept of depression as a dysfunction of the immune system . Curr Immunol Rev 2010 ; 6:205–212 Crossref , Medline ,  Google Scholar

84 Loftis JM, Huckans M, Morasco BJ : Neuroimmune mechanisms of cytokine-induced depression: current theories and novel treatment strategies . Neurobiol Dis 2010 ; 37:519–533 Crossref , Medline ,  Google Scholar

85 Zhu CB, Blakely RD, Hewlett WA : The proinflammatory cytokines interleukin-1beta and tumor necrosis factor-alpha activate serotonin transporters . Neuropsychopharmacology 2006 ; 31:2121–2131 Crossref , Medline ,  Google Scholar

86 Folkman S, Lazarus RS : The relationship between coping and emotion: implications for theory and research . Soc Sci Med 1988 ; 26:309–317 Crossref , Medline ,  Google Scholar

87 Beck AT : The evolution of the cognitive model of depression and its neurobiological correlates . Am J Psychiatry 2008 ; 165:969–977 Crossref , Medline ,  Google Scholar

88 Heim C, Binder EB : Current research trends in early life stress and depression: review of human studies on sensitive periods, gene-environment interactions, and epigenetics . Exp Neurol 2012 ; 233:102–111 Crossref , Medline ,  Google Scholar

89 Gibb BE, Abramson LY, Alloy LB : Emotional maltreatment from parents, verbal peer victimization, and cognitive vulnerability to depression . Cogn Ther Res 2004 ; 28:1–21 Crossref ,  Google Scholar

90 Stankiewicz AM, Swiergiel AH, Lisowski P : Epigenetics of stress adaptations in the brain . Brain Res Bull 2013 ; 98:76–92 Crossref , Medline ,  Google Scholar

91 Peña CJ, Bagot RC, Labonté B, et al. : Epigenetic signaling in psychiatric disorders . J Mol Biol 2014 ; 426:3389–3412 Crossref , Medline ,  Google Scholar

92 Elliott E, Ezra-Nevo G, Regev L, et al. : Resilience to social stress coincides with functional DNA methylation of the Crf gene in adult mice . Nat Neurosci 2010 ; 13:1351–1353 Crossref , Medline ,  Google Scholar

93 Peng H, Long Y, Li J, et al. : Hypothalamic-pituitary-adrenal axis functioning and dysfunctional attitude in depressed patients with and without childhood neglect . BMC Psychiatry 2014 ; 14:45 Crossref , Medline ,  Google Scholar

94 Sapolsky RM, Krey LC, McEwen BS : Stress down-regulates corticosterone receptors in a site-specific manner in the brain . Endocrinology 1984 ; 114:287–292 Crossref , Medline ,  Google Scholar

95 Korosi A, Naninck EF, Oomen CA, et al. : Early-life stress mediated modulation of adult neurogenesis and behavior . Behav Brain Res 2012 ; 227:400–409 Crossref , Medline ,  Google Scholar

96 Meaney MJ, Szyf M : Environmental programming of stress responses through DNA methylation: life at the interface between a dynamic environment and a fixed genome . Dialogues Clin Neurosci 2005 ; 7:103–123 Medline ,  Google Scholar

97 Klengel T, Mehta D, Anacker C, et al. : Allele-specific FKBP5 DNA demethylation mediates gene-childhood trauma interactions . Nat Neurosci 2013 ; 16:33–41 Crossref , Medline ,  Google Scholar

98 Hammen C, Henry R, Daley SE : Depression and sensitization to stressors among young women as a function of childhood adversity . J Consult Clin Psychol 2000 ; 68:782–787 Crossref , Medline ,  Google Scholar

99 Gould F, Clarke J, Heim C, et al. : The effects of child abuse and neglect on cognitive functioning in adulthood . J Psychiatr Res 2012 ; 46:500–506 Crossref , Medline ,  Google Scholar

  • Evaluation of the anxiolytic activity of ethanolic extract of Ziziphus mauritiana Lam. in Swiss albino mice Pharmacological Research - Modern Chinese Medicine, Vol. 10
  • Unraveling the associations between unhealthy lifestyle behaviors and mental health in the general adult Chinese population: A cross-sectional study Journal of Affective Disorders, Vol. 349
  • Fermented mixed grain ameliorates chronic stress-induced depression-like behavior and memory deficit 27 July 2023 | Food Science and Biotechnology, Vol. 33, No. 4
  • Virtual Reality Breathing Interventions for Mental Health: A Systematic Review and Meta-Analysis of Randomized Controlled Trials 18 January 2024 | Applied Psychophysiology and Biofeedback, Vol. 49, No. 1
  • Alleviating Stress in Parkinson’s Disease: Symptomatic Treatment, Disease Modification, or Both? Journal of Parkinson's Disease, Vol. 9
  • Life Stressors and Mental Well-Being: Experiences of African American Women Navigating Perinatal Loss and Pregnancy Subsequent to Loss 6 February 2024 | Journal of Black Psychology, Vol. 2
  • Behavioural characteristics and sex differences of a treatment-resistant depression model: Chronic mild stress in the Wistar-Kyoto rat Behavioural Brain Research, Vol. 457
  • A mouse model of oral contraceptive exposure: Depression, motivation, and the stress response Hormones and Behavior, Vol. 158
  • Effects of relaxation interventions during pregnancy on maternal mental health, and pregnancy and newborn outcomes: A systematic review and meta-analysis 25 January 2024 | PLOS ONE, Vol. 19, No. 1
  • Histone 3 Trimethylation Patterns are Associated with Resilience or Stress Susceptibility in a Rat Model of Major Depression Disorder 16 January 2024 | Molecular Neurobiology, Vol. 13
  • Diet Quality Mediates the Relationship Between Chronic Stress and Inflammation in Patients With Metabolic Syndrome 9 January 2024 | Journal of Cardiovascular Nursing, Vol. 14
  • Socioecological Model of a Military Family’s Health and Well-Being: Inside a Slovenian Military Family 24 August 2022 | Armed Forces & Society, Vol. 50, No. 1
  • Association between inflammatory bowel disease and subsequent depression or anxiety disorders - A retrospective cohort study of 31,728 outpatients Journal of Psychiatric Research, Vol. 169
  • Clotrimazole ameliorates chronic mild stress-induced depressive-like behavior in rats; crosstalk between the HPA, NLRP3 inflammasome, and Wnt/β-catenin pathways International Immunopharmacology, Vol. 127
  • A neuronal coping mechanism linking stress-induced anxiety to motivation for reward Science Advances, Vol. 9, No. 49
  • Effect of breathwork on stress and mental health: A meta-analysis of randomised-controlled trials 9 January 2023 | Scientific Reports, Vol. 13, No. 1
  • Chronic social defeat alters brain vascular-associated cell gene expression patterns leading to vascular dysfunction and immune system activation 28 June 2023 | Journal of Neuroinflammation, Vol. 20, No. 1
  • Wistar-Kyoto rats and chronically stressed Wistar rats present similar depression- and anxiety-like behaviors but different corticosterone and endocannabinoid system modulation Progress in Neuro-Psychopharmacology and Biological Psychiatry, Vol. 127
  • Investigating the potential effects of α-synuclein aggregation on susceptibility to chronic stress in a mouse Parkinson’s disease model 19 September 2023 | Pharmacological Reports, Vol. 75, No. 6
  • A comparative analysis of depressive-like behavior: Exploring sex-related differences and insights 29 November 2023 | PLOS ONE, Vol. 18, No. 11
  • Effect of consumption of blue maize tortilla on anxiety-like behaviour, learning, memory and hippocampal BDNF expression in a chronic stress model in rats 29 September 2022 | Nutritional Neuroscience, Vol. 26, No. 11
  • Lactobacillus reuteri ATG-F4 Alleviates Chronic Stress-induced Anhedonia by Modulating the Prefrontal Serotonergic System Experimental Neurobiology, Vol. 32, No. 5
  • The presentation of anxiety and depression among children and youth diagnosed with hidradenitis suppurativa: A review 10 October 2023 | Pediatric Dermatology, Vol. 63
  • Psychological, behavioural and biological factors associated with gastrointestinal symptoms in autistic adults and adults with autistic traits 16 February 2023 | Autism, Vol. 27, No. 7
  • Effects of Online Pilates and Face-to-Face Pilates Intervention on Body Composition, Muscle Mechanical Properties, Cardiometabolic Parameters, Mental Health, and Physical Fitness in Middle-Aged Women with Obesity 19 October 2023 | Healthcare, Vol. 11, No. 20
  • Antidepressant effects of coumarins and their derivatives: A critical analysis of research advances European Journal of Pharmacology, Vol. 956
  • Adverse Childhood Experiences, Intimate Partner Violence, and Mental Well-Being Among Mothers of Toddlers in Tirana, Albania: A Cross-Sectional Mediation Analysis 29 September 2023 | Violence Against Women, Vol. 9
  • Emphasizing the Crosstalk Between Inflammatory and Neural Signaling in Post-traumatic Stress Disorder (PTSD) 25 April 2023 | Journal of Neuroimmune Pharmacology, Vol. 18, No. 3
  • Stress-mediated dysregulation of the Rap1 small GTPase impairs hippocampal structure and function iScience, Vol. 26, No. 9
  • Exploring Novel Therapeutic Approaches for Depressive Disorders: The Role of Allopregnanolone Agonists Cureus, Vol. 291
  • Nervous-System-Wise Functional Estimation of Directed Brain–Heart Interplay Through Microstate Occurrences IEEE Transactions on Biomedical Engineering, Vol. 70, No. 8
  • Stress in the social context: a behavioural and eco-evolutionary perspective 2 August 2023 | Journal of Experimental Biology, Vol. 226, No. 15
  • Pathways to Psychopathology Among Autistic Adults 28 June 2023 | Current Psychiatry Reports, Vol. 25, No. 8
  • STAPP@Work: A Mobile Self-Management Application for Reducing Work Stress and Preventing Burnout: Single-Case Experimental Design Study (Preprint) 13 May 2023 | Journal of Medical Internet Research
  • Stress-Activated Protein Kinase JNK Modulates Depression-like Behaviors in Mice 18 January 2023 | Molecular Neurobiology, Vol. 60, No. 5
  • Chronic oral ketamine prevents anhedonia and alters neuronal activation in the lateral habenula and nucleus accumbens in rats under chronic unpredictable mild stress Neuropharmacology, Vol. 228
  • Dietary tryptophan-rich protein hydrolysate can acutely impact physiological and psychological measures of mood and stress in healthy adults 28 March 2022 | Nutritional Neuroscience, Vol. 26, No. 4
  • Mental health as part of the integrative palliative care in all levels of care in urooncology 23 April 2022 | Aging & Mental Health, Vol. 27, No. 4
  • Antidepressant and Neuroprotective Effects of 3-Hydroxy Paroxetine, an Analog of Paroxetine in Rats 26 November 2022 | International Journal of Neuropsychopharmacology, Vol. 26, No. 3
  • Cortisol Biosensors: From Sensing Principles to Applications 15 March 2023 | World Scientific Annual Review of Functional Materials
  • Elevated Hippocampal CRMP5 Mediates Chronic Stress-Induced Cognitive Deficits by Disrupting Synaptic Plasticity, Hindering AMPAR Trafficking, and Triggering Cytokine Release 3 March 2023 | International Journal of Molecular Sciences, Vol. 24, No. 5
  • Can a single session of noninvasive brain stimulation applied over the prefrontal cortex prevent stress-induced cortisol release? Progress in Neuro-Psychopharmacology and Biological Psychiatry, Vol. 121
  • Psychoneuro-oncology: How chronic stress grows cancer Clinics in Dermatology, Vol. 54
  • Peripheral brain-derived neurotrophic factor (BDNF) and salivary cortisol levels in college students with different levels of academic stress. Study protocol 22 February 2023 | PLOS ONE, Vol. 18, No. 2
  • The Role of Genetics in the Development and Pharmacotherapy of Depression and Its Impact on Drug Discovery 2 February 2023 | International Journal of Molecular Sciences, Vol. 24, No. 3
  • Role of group II metabotropic glutamate receptors in ketamine's antidepressant actions Pharmacology Biochemistry and Behavior, Vol. 223
  • Relating parental stress with sleep disorders in parents and children 25 January 2023 | PLOS ONE, Vol. 18, No. 1
  • Anxiety, depression, and academic stress among medical students during the COVID-19 pandemic 11 January 2023 | Frontiers in Psychology, Vol. 13
  • Associations Between Demographic, Clinical, and Symptom Characteristics and Stress in Oncology Patients Receiving Chemotherapy 4 March 2022 | Cancer Nursing, Vol. 46, No. 1
  • Prevalence of Common Alleles of Some Stress Resilience Genes among Adolescents Born in Different Periods Relative to the Socioeconomic Crisis of the 1990s in Russia 21 December 2022 | Current Issues in Molecular Biology, Vol. 45, No. 1
  • Introducing a depression-like syndrome for translational neuropsychiatry: a plea for taxonomical validity and improved comparability between humans and mice 14 September 2022 | Molecular Psychiatry, Vol. 28, No. 1
  • Targeting the Arginine Vasopressin V1b Receptor System and Stress Response in Depression and Other Neuropsychiatric Disorders 1 April 2023 | Neuropsychiatric Disease and Treatment, Vol. Volume 19
  • Efficacy and Anti-Inflammatory Activity of Ashwagandha Sustained-Release Formulation on Depression and Anxiety Induced by Chronic Unpredictable Stress: in vivo and in vitro Studies 1 July 2023 | Journal of Experimental Pharmacology, Vol. Volume 15
  • Gender Differences in Older Adults’ Muscle Strength and Depressive Symptoms: A Relationship Mediated Through Perceived Stress 27 September 2023 | Research in Community and Public Health Nursing, Vol. 34
  • Increased academic stress is associated with decreased plasma BDNF in Chilean college students 3 November 2023 | PeerJ, Vol. 11
  • Interaction effect between childhood abuse and interleukin-1β levels on suicidality in depressed patients 25 April 2022 | European Archives of Psychiatry and Clinical Neuroscience, Vol. 272, No. 8
  • Prevalence, associated factors and perinatal outcomes of antepartum depression in Ibadan Nigeria 18 March 2022 | BMC Pregnancy and Childbirth, Vol. 22, No. 1
  • Stress, mental disorder and ketamine as a novel, rapid acting treatment European Neuropsychopharmacology, Vol. 65
  • Neighbourhood crime and major depression in Sweden: A national cohort study Health & Place, Vol. 78
  • Preschool Depression and Hippocampal Volume: The Moderating Role of Family Income Journal of the American Academy of Child & Adolescent Psychiatry, Vol. 61, No. 11
  • HPA Axis Responsiveness Associates with Central Serotonin Transporter Availability in Human Obesity and Non-Obesity Controls 25 October 2022 | Brain Sciences, Vol. 12, No. 11
  • Restraint Stress and Repeated Corticosterone Administration Differentially Affect Neuronal Excitability, Synaptic Transmission and 5-HT7 Receptor Reactivity in the Dorsal Raphe Nucleus of Young Adult Male Rats 18 November 2022 | International Journal of Molecular Sciences, Vol. 23, No. 22
  • Sleep disorders mediate the link between childhood trauma and depression severity in children and adolescents with depression 28 October 2022 | Frontiers in Psychiatry, Vol. 13
  • The cumulative effect of chronic stress and depressive symptoms affects heart rate in a working population 13 October 2022 | Frontiers in Psychiatry, Vol. 13
  • Depression among people with dyspepsia and H. pylori infection: A community based cross-sectional study in Ethiopia 6 October 2022 | PLOS ONE, Vol. 17, No. 10
  • Cross-Sectional Analysis of Mental Health among University Students: Do Sex and Academic Level Matter? 3 October 2022 | International Journal of Environmental Research and Public Health, Vol. 19, No. 19
  • Hormonal contraceptives, stress, and the brain: The critical need for animal models Frontiers in Neuroendocrinology, Vol. 67
  • Olfactory loss is a predisposing factor for depression, while olfactory enrichment is an effective treatment for depression 28 September 2022 | Frontiers in Neuroscience, Vol. 16
  • Understanding the basis of major depressive disorder in oncological patients: Biological links, clinical management, challenges, and lifestyle medicine 16 September 2022 | Frontiers in Oncology, Vol. 12
  • The Lived Experiences of Depression in Huntington’s Disease: A Qualitative Study Journal of Huntington's Disease, Vol. 11, No. 3
  • Exaggerated amygdala response to threat and association with immune hyperactivity in depression Brain, Behavior, and Immunity, Vol. 104
  • Causal Symbolic Information Transfer for the Assessment of functional Brain-Heart Interplay through EEG Microstates Occurrences: a proof-of-concept study
  • Mental Health Conditions According to Stress and Sleep Disorders 29 June 2022 | International Journal of Environmental Research and Public Health, Vol. 19, No. 13
  • Different Areas of Chronic Stress and Their Associations with Depression 19 July 2022 | International Journal of Environmental Research and Public Health, Vol. 19, No. 14
  • Vestibular Disability/Handicap in Fibromyalgia: A Questionnaire Study 11 July 2022 | Journal of Clinical Medicine, Vol. 11, No. 14
  • Perceived stress and E-cigarette use during emerging adulthood: A longitudinal examination of initiation, progression, and continuation Preventive Medicine, Vol. 160
  • Efficacy of Withania somnifera supplementation on adult’s cognition and mood Journal of Ayurveda and Integrative Medicine, Vol. 13, No. 2
  • CCR2 monocytes repair cerebrovascular damage caused by chronic social defeat stress Brain, Behavior, and Immunity, Vol. 101
  • The interplay between self-esteem, expectancy, cognitive control, rumination, and the experience of stress: A network analysis 14 February 2022 | Current Psychology, Vol. 79
  • The Cortisol Assessment List (CoAL) A tool to systematically document and evaluate cortisol assessment in blood, urine and saliva Comprehensive Psychoneuroendocrinology, Vol. 9
  • Introduction to the Study of Stress 17 May 2022
  • Neurobiological Approach to Stress 17 May 2022
  • Clinical Approach to Stress 17 May 2022
  • Medical Comorbidities of Hispanics with Mental Health Problems 2 December 2022
  • Specific and cumulative lifetime stressors in the aetiology of major depression: A longitudinal community-based population study 26 January 2022 | Epidemiology and Psychiatric Sciences, Vol. 31
  • Associations of state-level and county-level hate crimes with individual-level cardiovascular risk factors in a prospective cohort study of middle-aged Americans: the National Longitudinal Survey of Youths 1979 19 January 2022 | BMJ Open, Vol. 12, No. 1
  • The role of pre-pandemic depression for changes in depression, anxiety, and loneliness during the COVID-19 pandemic: Results from a longitudinal probability sample of adults from Germany 3 November 2022 | European Psychiatry, Vol. 65, No. 1
  • Interleukin-6 expression and its modulation by diacerein in a rat model of chronic stress induced cardiac dysfunction Heliyon, Vol. 7, No. 12
  • Psychometric properties and correlates of Chinese version of Perceived Stress Scale (CPSS-10) in people with common mental disorders with different employment Statuses 4 August 2021 | Hong Kong Journal of Occupational Therapy, Vol. 34, No. 2
  • Association between the expression of lncRNA BASP-AS1 and volume of right hippocampal tail moderated by episode duration in major depressive disorder: a CAN-BIND 1 report 8 September 2021 | Translational Psychiatry, Vol. 11, No. 1
  • Psychoneuroimmunology in the context of perinatal depression - Tools for improved clinical practice Brain, Behavior, & Immunity - Health, Vol. 17
  • MicroRNAs as Critical Biomarkers of Major Depressive Disorder: A Comprehensive Perspective 10 November 2021 | Biomedicines, Vol. 9, No. 11
  • Treadmill exercise sex-dependently alters susceptibility to depression-like behaviour, cytokines and BDNF in the hippocampus and prefrontal cortex of rats with sporadic Alzheimer-like disease Physiology & Behavior, Vol. 241
  • Childhood trauma and depression in college students: Mediating and moderating effects of psychological resilience Asian Journal of Psychiatry, Vol. 65
  • Back to nature: herbal treatment, environmental enrichment, and social play can protect against unpredictable chronic stress in Long-Evans rats (Rattus norvegicus) 1 August 2021 | Psychopharmacology, Vol. 238, No. 10
  • Maternal chewing improves prenatal stress-induced cognitive deficit and anxiety-like behavior associated with alterations of the apoptotic response and serotonin pathway in mouse offspring Archives of Oral Biology, Vol. 130
  • HPA Axis in the Pathomechanism of Depression and Schizophrenia: New Therapeutic Strategies Based on Its Participation 30 September 2021 | Brain Sciences, Vol. 11, No. 10
  • Roles of fibroblast growth factor 21 in the control of depression‐like behaviours after social defeat stress in male rodents 1 September 2021 | Journal of Neuroendocrinology, Vol. 33, No. 10
  • Weak and uneven associations of home, neighborhood, and school environments with stress hormone output across multiple timescales 4 May 2020 | Molecular Psychiatry, Vol. 26, No. 9
  • Impact of depression and stress on placental DNA methylation in ethnically diverse pregnant women Epigenomics, Vol. 13, No. 18
  • Stress and Western diets increase vulnerability to neuropsychiatric disorders: A common mechanism 16 September 2019 | Nutritional Neuroscience, Vol. 24, No. 8
  • Associations of job strain and family strain with risk of major depressive episode: A prospective cohort study in U.S. working men and women Journal of Psychosomatic Research, Vol. 147
  • Prolonged Maternal Separation Induces the Depression-Like Behavior Susceptibility to Chronic Unpredictable Mild Stress Exposure in Mice BioMed Research International, Vol. 2021
  • Endometriosis and related pelvic pain: association with stress, anxiety and depressive symptoms Minerva Obstetrics and Gynecology, Vol. 73, No. 3
  • Title: “Labels Matter: Is it stress or is it Trauma?” 10 July 2021 | Translational Psychiatry, Vol. 11, No. 1
  • Depression, anxiety, and the COVID-19 pandemic: Severity of symptoms and associated factors among university students after the end of the movement lockdown 27 May 2021 | PLOS ONE, Vol. 16, No. 5
  • Traditional Chinese Medicine Body Constitutions and Psychological Determinants of Depression among University Students in Malaysia: A Pilot Study 18 May 2021 | International Journal of Environmental Research and Public Health, Vol. 18, No. 10
  • A pioneering FreeSurfer volumetric study of a series of patients with mesial temporal lobe epilepsy and hippocampal sclerosis with comorbid depression Psychiatry Research: Neuroimaging, Vol. 311
  • Increased Stress Resistance and Lifespan in Chaenorhabditis elegans Wildtype and Knockout Mutants—Implications for Depression Treatment by Medicinal Herbs 24 March 2021 | Molecules, Vol. 26, No. 7
  • Activation of PPG neurons following acute stressors differentially involves hindbrain serotonin in male rats Neuropharmacology, Vol. 187
  • Comorbid anxiety-like behavior in a rat model of colitis is mediated by an upregulation of corticolimbic fatty acid amide hydrolase 15 January 2021 | Neuropsychopharmacology, Vol. 46, No. 5
  • Prenatal Alcohol Exposure and Hypothalamic-Pituitary-Adrenal Axis Activity of the Offspring in Humans: a Systematic Review 5 January 2021 | Current Addiction Reports, Vol. 8, No. 1
  • Depression and anxiety in the Malaysian urban population and their association with demographic characteristics, quality of life, and the emergence of the COVID-19 pandemic 19 February 2021 | Current Psychology, Vol. 16
  • Hypothalamic Inflammation as a Potential Pathophysiologic Basis for the Heterogeneity of Clinical, Hormonal, and Metabolic Presentation in PCOS 5 February 2021 | Nutrients, Vol. 13, No. 2
  • Potential Interactions Between Environmental and Psychoneurobiological Factors in the Interface Between Stress and Depression: A Road Map to Resilience 24 January 2021
  • The 2020 Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders 22 December 2020 | Australian & New Zealand Journal of Psychiatry, Vol. 55, No. 1
  • Association between depression, anxiety, stress and perceived quality of life in a Malaysian B40 urban community during the COVID-19 lockdown: A cross-sectional study 29 July 2021 | F1000Research, Vol. 10
  • Effects of an African Circle Dance Programme on Internally Displaced Persons with Depressive Symptoms: A Quasi-Experimental Study 19 January 2021 | International Journal of Environmental Research and Public Health, Vol. 18, No. 2
  • Yoga and Lifestyle Factors in Heart Disease
  • Fluoxetine increases hippocampal neural survival by improving axonal transport in stress-induced model of depression male rats Physiology & Behavior, Vol. 227
  • A Systematic Review of the Association Between Psychological Stress and Dementia Risk in Humans Journal of Alzheimer's Disease, Vol. 78, No. 1
  • A Gut Feeling: The Importance of the Intestinal Microbiota in Psychiatric Disorders 19 October 2020 | Frontiers in Immunology, Vol. 11
  • Efficacy of probiotics on stress in healthy volunteers: A systematic review and meta‐analysis based on randomized controlled trials 14 July 2020 | Brain and Behavior, Vol. 10, No. 9
  • The influence of the duration of chronic unpredictable mild stress on the behavioural responses of C57BL/6J mice 18 May 2020 | Behavioural Pharmacology, Vol. 31, No. 6
  • Corticosterone Induced the Increase of proBDNF in Primary Hippocampal Neurons Via Endoplasmic Reticulum Stress 6 May 2020 | Neurotoxicity Research, Vol. 38, No. 2
  • Monoaminergic balances predict non-depression-like phenotype in Learned Helplessness Paradigm Neuroscience, Vol. 440
  • Abnormal functional connectivity as neural biological substrate of trait and state characteristics in major depressive disorder Progress in Neuro-Psychopharmacology and Biological Psychiatry, Vol. 102
  • Chronic stress and endothelial dysfunction: mechanisms, experimental challenges, and the way ahead American Journal of Physiology-Heart and Circulatory Physiology, Vol. 319, No. 2
  • Heart Rate Variability as an Index of Differential Brain Dynamics at Rest and After Acute Stress Induction 2 July 2020 | Frontiers in Neuroscience, Vol. 14
  • Early life stress and glutamate neurotransmission in major depressive disorder European Neuropsychopharmacology, Vol. 35
  • Prevalence of and factors associated with depression among hill tribe individuals aged 30 years and over in Thailand Heliyon, Vol. 6, No. 6
  • State-of-the-Art: Inflammatory and Metabolic Markers in Mood Disorders 6 June 2020 | Life, Vol. 10, No. 6
  • Pharmacological Treatment of Anxiety Disorders: The Role of the HPA Axis 15 May 2020 | Frontiers in Psychiatry, Vol. 11
  • Neuroinflammation and depressive disorder: The role of the hypothalamus Journal of Clinical Neuroscience, Vol. 75
  • From Work Well-Being to Burnout: A Hypothetical Phase Model 30 April 2020 | Frontiers in Neuroscience, Vol. 14
  • Finding intestinal fortitude: Integrating the microbiome into a holistic view of depression mechanisms, treatment, and resilience Neurobiology of Disease, Vol. 135
  • Role of glucocorticoid- and monoamine-metabolizing enzymes in stress-related psychopathological processes 19 July 2019 | Stress, Vol. 23, No. 1
  • Is Stress a Cause or a Consequence of Endometriosis? 6 January 2020 | Reproductive Sciences, Vol. 27, No. 1
  • Hypothalamic–pituitary–adrenal axis and stress
  • Immune response to stress induction as a predictor of cognitive-behavioral therapy outcomes in adolescent mood disorders: A pilot study Journal of Psychiatric Research, Vol. 120
  • Enduring and Sex-specific Changes in Hippocampal Gene Expression after a Subchronic Immune Challenge Neuroscience, Vol. 428
  • Childhood maltreatment and risk for suicide attempts in major depression: a sex-specific approach 7 May 2019 | European Journal of Psychotraumatology, Vol. 10, No. 1
  • How Do Couples Cope With Unemployment: Examining Relationships Among Support, Undermining, and Depression 10 March 2019 | The Family Journal, Vol. 27, No. 3
  • REM sleep’s unique associations with corticosterone regulation, apoptotic pathways, and behavior in chronic stress in mice 25 January 2019 | Proceedings of the National Academy of Sciences, Vol. 116, No. 7
  • Subclinical Lipopolysaccharide from Salmonella Enteritidis Induces Dysregulation of Bioactive Substances from Selected Brain Sections and Glands of Neuroendocrine Axes 2 February 2019 | Toxins, Vol. 11, No. 2
  • Advance in Stress for Depressive Disorder 30 November 2019
  • Antidepressant and anxiolytic effects of the proprietary Chinese medicine Shexiang Baoxin pill in mice with chronic unpredictable mild stress Journal of Food and Drug Analysis, Vol. 27, No. 1
  • Frontiers in Neuroscience, Vol. 13
  • A gene–gene interaction between the vascular endothelial growth factor a and brain-derived neurotrophic factor genes is associated with psychological distress in the Taiwanese population Taiwanese Journal of Psychiatry, Vol. 33, No. 3
  • An investigation into the stress-relieving and pharmacological actions of an ashwagandha (Withania somnifera) extract 1 September 2019 | Medicine, Vol. 98, No. 37
  • Cortisol and alpha-amylase assessment in psychotherapeutic intervention studies: A systematic review Neuroscience & Biobehavioral Reviews, Vol. 95
  • Neighborhood disorder, exposure to violence, and perceived discrimination in relation to symptoms in midlife women 19 October 2018 | Women's Midlife Health, Vol. 4, No. 1
  • Potassium 2-(1-hydroxypentyl)-benzoate improves depressive-like behaviors in rat model Acta Pharmaceutica Sinica B, Vol. 8, No. 6
  • Emotional stress regulation: The role of relative frontal alpha asymmetry in shaping the stress response Biological Psychology, Vol. 138
  • Late chronotype is associated with enhanced amygdala reactivity and reduced fronto-limbic functional connectivity to fearful versus happy facial expressions NeuroImage, Vol. 171
  • Behavioral and structural adaptations to stress Frontiers in Neuroendocrinology, Vol. 49
  • The Neurochemistry of Stress: the Chemistry of the Stress Response and Stress Vulnerability 30 June 2018 | Neurochemical Journal, Vol. 12, No. 2
  • Hair cortisol and the relationship with chronic pain and quality of life in endometriosis patients Psychoneuroendocrinology, Vol. 89
  • miRNAs As Critical Epigenetic Players in Determining Neurobiological Correlates of Major Depressive Disorder 25 October 2017
  • Hepatology International, Vol. 12, No. 4
  • Neuropsychopharmacology, Vol. 43, No. 5
  • Bibliographie
  • Medical Yoga Therapy 10 February 2017 | Children, Vol. 4, No. 2
  • Psihijatrija danas, Vol. 49, No. 1
  • Hippocampal volume and integrity as predictors of cognitive decline in intact elderly NeuroReport, Vol. 27, No. 11
  • Psychosocial stress based on public speech in humans: is there a real life/laboratory setting cross-adaptation? 4 July 2016 | Stress, Vol. 19, No. 4

research paper about stress and depression

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Int J Environ Res Public Health

Logo of ijerph

Anxiety, Depression and Quality of Life—A Systematic Review of Evidence from Longitudinal Observational Studies

Associated data.

Not applicable.

This review aimed to systematically review observational studies investigating the longitudinal association between anxiety, depression and quality of life (QoL). A systematic search of five electronic databases (PubMed, PsycINFO, PSYNDEX, NHS EED and EconLit) as well as forward/backward reference searches were conducted to identify observational studies on the longitudinal association between anxiety, depression and QoL. Studies were synthesized narratively. Additionally, a random-effects meta-analysis was performed using studies applying the mental and physical summary scores (MCS, PCS) of the Short Form Health Survey. The review was prospectively registered with PROSPERO and a study protocol was published. n = 47 studies on heterogeneous research questions were included, with sample sizes ranging from n = 28 to 43,093. Narrative synthesis indicated that QoL was reduced before disorder onset, dropped further during the disorder and improved with remission. Before onset and after remission, QoL was lower in comparison to healthy comparisons. n = 8 studies were included in random-effects meta-analyses. The pooled estimates of QoL at follow-up (FU) were of small to large effect sizes and showed that QoL at FU differed by disorder status at baseline as well as by disorder course over time. Disorder course groups differed in their MCS scores at baseline. Effect sizes were generally larger for MCS relative to PCS. The results highlight the relevance of preventive measures and treatment. Future research should consider individual QoL domains, individual anxiety/depressive disorders as well as the course of both over time to allow more differentiated statements in a meta-analysis.

1. Introduction

The World Health Organization [ 1 ] estimates that 264 million people worldwide were suffering from an anxiety disorder and 322 million from a depressive disorder in 2015, corresponding to prevalence rates of 3.6% and 4.4%. While their prevalence varies slightly by age and gender [ 1 ], they are among the most common mental disorders in the general population [ 2 , 3 , 4 , 5 , 6 ]. During the COVID-19 pandemic, multiple challenges have arisen for many, such as loneliness [ 7 ] or financial hardship. A meta-analysis showed a prevalence of anxiety of about 32% (95% CI: 28–37) and a prevalence of depression ( n = 14 studies) of about 34% (95% CI: 28–41) in general populations during the COVID-19 pandemic [ 8 ].

Anxiety and depression have been associated with adverse societal and individual correlates, including higher health care costs [ 9 , 10 , 11 ] and an increased risk for physical comorbidities, such as cardiovascular illnesses [ 12 , 13 ]. Moreover, they have been linked to a reduced quality of life (QoL) in numerous cross-sectional as well as longitudinal studies in which they significantly predicted QoL outcomes [ 14 , 15 , 16 , 17 , 18 ]. Other studies have reported a reverse association, whereby QoL was predictive of mental health outcomes [ 19 ] or a bi-directional association [ 20 , 21 ]. Some very recent studies also examined these associations among quite different samples (e.g., [ 22 , 23 , 24 , 25 ]).

Looking at longitudinal rather than cross-sectional data from observational studies has several advantages. It allows for the identification of trajectories over time within the same individuals rather than focusing on group differences at one point in time only [ 26 ]. Moreover, when appropriate methods are applied to longitudinal data, intraindividual heterogeneity can be taken into account, resulting in more consistent estimates [ 27 ]. This has previously been demonstrated in QoL research [ 28 ]. A need to analyze longitudinal changes in QoL domains in QoL research in people with mental disorders has also been previously identified [ 29 ]. Beyond individual longitudinal studies suggesting a link between anxiety or depression and QoL, several systematic reviews have synthesized longitudinal evidence on these associations and mostly reported negative associations between the variables. These reviews have tended to focus on specific age groups, such as older adults [ 30 ], samples with specific diseases [ 31 , 32 ], or have investigated the effect of specific treatments on QoL in patients with anxiety [ 33 ]. Investigating these associations in samples without these limitations could reduce the effect of specific conditions and treatments on the association and strengthen the conclusions that can be drawn.

In light of the previous findings, this study aims to add to the present literature by systematically synthesizing evidence from observational studies on the longitudinal association between anxiety, depression and QoL across all age groups in samples who do not have other specific illnesses and do not receive specific treatments.

2. Materials and Methods

This review was registered with PROSPERO (CRD42018108008) and a study protocol was published [ 34 ].

2.1. Search Strategy

Five electronic databases from several fields of research (PubMed, PsycINFO, PSYNDEX, NHS EED and EconLit) were examined until December 2020. Where possible, search terms were entered as Medical Subject Headings (MeSH) or as keywords in the title/abstract. The PubMed search strategy was: (anxi*[Title/Abstract] or depress*[Title/Abstract] or anxiety disorder[MeSH] or depressive disorder[MeSH]) and quality of life[MeSH] and longitudinal study[MeSH]. Please note that “*” is a truncation symbol. Time or location were not restricted. In addition, we applied backward and forward reference searches of included studies to identify additional references. The forward reference search was conducted until January 2021 using Web of Science to identify cited papers.

2.2. Study Selection Process

The study selection process is displayed in Figure 1 . Most identified studies were screened in a two-step process (title/abstract; full-text screening) independently by two reviewers (J.K.H., E.Q.) against defined criteria (see Table 1 ). The last updated literature screening before submission was conducted by one reviewer (J.K.H.) and encompassed 9% of the studies included for title/abstract screening. Before the final criteria were applied, they were pretested and refined. Disagreements during the selection process were resolved through discussion or by the inclusion of a third party (A.H.) if a consensus could not be reached.

An external file that holds a picture, illustration, etc.
Object name is ijerph-18-12022-g001.jpg

Study flow (PRISMA flow chart).

Study selection criteria.

Abbreviations: QoL = quality of life; ICD = International Classification of Diseases; DSM = Diagnostic and Statistical Manual of Mental Disorders; BL = study baseline; KIDSCREEN = Health Related Quality of Life Questionnaire for Children and Young People and their Parents; KINDL = German generic quality of life instrument for children

2.3. Data Extraction and Synthesis

We extracted information regarding the study design, operationalization of the variables, sample characteristics, statistical methods and results regarding the research question of interest. If several analyses were presented for the same research question, we extracted the final covariate-adjusted model for narrative synthesis. Data were extracted by one reviewer (J.K.H.) and cross-checked by a second reviewer (E.Q.). If needed, extracted data were standardized (e.g., by calculating the weighted average means when combining groups) to present comparable information. If clarification was needed, the corresponding authors were contacted.

For the narrative synthesis, all studies were first grouped by research question, e.g., whether disorders or the degree of symptoms were analyzed, which comparison groups were used, which QoL domains were considered, and at which waves the variables of interest were considered in the analyses. Because research questions and analyses were heterogeneous, a concise narrative synthesis of the main results of all studies was not feasible. Therefore, we provide an overview of all identified studies in the tables and a detailed narrative synthesis of those studies, analyzing trajectories of disorders or changes in symptoms in association with changes in QoL over time.

Additionally, we examined whether data were appropriate for meta-analysis. The specific research questions, the operationalization of main variables and statistical methods were heterogeneous across studies and not all the statistical estimates needed could be obtained from covariate-adjusted analyses. Therefore, to enhance the comparability of the underlying data and the interpretation of the pooled estimates, we used descriptive information. Because most papers applied variations of the Short Form Health Survey and analyzed mental and physical component scores (MCS, PCS), we considered these studies as eligible for meta-analysis. The necessary information could be obtained for 8 publications. Random-effects meta-analysis was used for pooling. Heterogeneity was assessed by means of I 2 , with higher values representing a larger degree of heterogeneity in terms of variability in effect size estimates between studies [ 41 ]. Pooled estimates are reported as Hedge’s g standardized mean difference (SMD), representing the difference in mean outcomes between groups relative to outcome measure variability [ 42 ]. According to Cohen (as cited in [ 43 ]), SMDs can be grouped into small ≤0.20, medium = 0.50 and large effects ≥0.80. Stata 16 was used for meta-analyses.

2.4. Quality/Risk of Bias Assessment

Two reviewers (J.K.H., E.Q.) independently assessed the quality and risk of bias of the included studies using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies, which was developed by the National Heart, Lung, and Blood Institute [ 44 ].

3.1. Selection Process

The literature search yielded 4027 unique references. After title/abstract screening, 215 studies were included for full-text screening. Finally, 47 publications were included in the final synthesis. During full-text screening, most studies were excluded because they exclusively analyzed data on a cross-sectional level (56.5%). For further details, see the PRISMA flow chart ( Figure 1 ).

3.2. Overview of Included Studies

Descriptive characteristics and quality/risk of bias assessment of the included studies are provided in Table S1 (Supplementary Material) . In short, sample size ranged from 28 to 43,093. Most studies focused on adults; only four analyzed children/adolescents. Regarding the settings, 17 of the analyzed samples were exclusively recruited in a health care setting, 12 of the studies analyzed general population samples, 14 recruited in another or in several settings, and all studies on children/adolescents recruited in schools ( n = 4). Twenty studies (42.6%) applied data from the same seven underlying datasets. Most studies reported on depression ( n = 36), less reported on anxiety ( n = 20) and some reported on the comorbidity between depression and anxiety ( n = 7). To assess mental disorders, half (48.9%) used structured interviews. Regarding QoL, most studies applied variations of the Short Form Health Survey (SF, n = 27) or the WHOQOL ( n = 12). A total of 38.3% of the studies were rated as “good”, 55.3% as “fair” and 6.4% as “poor” in the quality assessment.

3.3. Overview of Studies on the Association between Anxiety/Depression as Independent Variables and QoL Outcomes

Detailed results on all studies investigating the association between anxiety/depression as independent variables and QoL outcomes are reported in Table 2 . As described in the methods section, the following paragraphs give an overview of those studies focusing on disorder trajectories/changes in symptoms over time and changes in QoL outcomes over time, because they allow for more differentiated interpretations.

Studies on depression/anxiety as independent variables and QoL outcomes.

Abbreviations: QoL = quality of life; MD = major depression; FU = follow-up; DSM = Diagnostic and Statistical Manual of Mental Disorders; HDRS = Hamilton Depression Rating Scale; PCS = Physical Component Score; MDS = Mental Component Score; MDD = major depressive disorder; ANOVA = analysis of variance; BL = baseline; MDE = major depressive episode; CIDI = Composite International Diagnostic Interview; SF-36 = Short Form 36; AUDADIS = Alcohol Use Disorders and Associated Disabilities Interview Schedule; SF-12 = Short Form 12; PHQ = Patient Health Questionnaire; SF-12v2: Short Form 12, Version 2; HRSD = Hamilton Rating Scale for Depression; HADS = Hospital Anxiety and Depression Scale; QLDS = Quality of Life in Depression Scale; EQ-VAS = EQ Visual Analogue Scale; DIS = Diagnostic Interview Schedule; BDI = Beck Depression Inventory; SCID = Short Children’s Depression Inventory; MINI = Mini-International Neuropsychiatric Interview; PTSD = post-traumatic stress disorder; hrqol = health-related quality of life, IES-15 = Impact of Event Scale 15; Q-DIS = Quick Version of the Mental Health’s Diagnostic Interview Schedule; MADRS = Montgomery–Åsberg Depression Rating Scale; FDD-DSM-IV = Fragebogen zur Depressionsdiagnostik nach Diagnostic and Statistical Manual of Mental Disorders IV; SCAN = Schedule for Clinical Assessment in Neuropsychiatry; DASS = Depression Anxiety Stress Scales; MOS SF = Medical Outcomes Study Short Form; CES-D = Center for Epidemiological Studies Depression Scale; WHOQOL-Bref-TW = WHOQOL-Bref Taiwan Version; MHI-5 = Mental Health Inventory 5; OCD = obsessive compulsive disorder; Y-BOCS = Yale–Brown Obsessive Compulsive Scale; BAI = Beck Angst Inventar; DD = depressive disorder; PD = psychiatric disorder; SAD = social anxiety disorder; Q-LES-Q = Quality of Life Enjoyment and Satisfaction Questionnaire; GHQ-28 = General Health Questionnaire 28; PCL-S = Post-traumatic Stress Disorder Checklist Scale; VETR-PTSD = Vietnam Era Twin Registry Posttraumatic Stress Disorder; DRPST = Disaster-Related Psychological Screening Test; SCL-90 = Symptomcheckliste bei psychischen Störungen 90; SASC = SpLD Assessment Standards Committee; QOLS = Quality of Life Scale; CDI = Children’s Depression Inventory.

Depression as independent variable and QoL as outcome. One study investigated QoL at several time points during the entire course of an episode of MD .

Buist-Bouwman, Ormel, de Graaf and Vollebergh [ 46 ] analyzed an MD group from a general population setting (NEMESIS) with data on SF-36 domains in the onset, acute and recovery phase of the depressive episode. The onset of MD was associated with a significant drop in several QoL domains and recovery with a significant increase. Pre- and post-morbid QoL levels were not significantly different for most domains, and post-morbid QoL was even higher for the psychological role functioning and psychological health domains. In comparison to a group without MD, pre- and post-morbid QoL levels in the MD group were significantly lower, except for the psychological role functioning domain, where no significant differences were found. Additionally, it should be noted that 40% of the sample had lower post-morbid QoL compared to pre-morbid levels.

Two studies investigated changes in QoL for people experiencing an onset of depression relative to different comparison groups over two points in time.

One study investigated incident MD in a general population sample (NESARC; Rubio, Olfson, Perez-Fuentes, Garcia-Toro, Wang and Blanco [ 14 ]). Here, incident MD (compared to those without a history of MD as well as to a group without any mental disorder) was associated with a significant drop in QoL (SF-12 MCS). Additionally, analyzing two waves, Pyne, Patterson, Kaplan, Ho, Gillin, Golshan and Grant [ 67 ] compared the QoL (Quality of Well-Being scale) between MD patients and community controls. The patient group was further divided into those continuously not receiving an MD diagnosis, those who continuously received the diagnosis and those who only received the diagnosis at FU (onset). The authors found that changes in QoL did not differ between the groups. At both points in time, QoL scores differed significantly between the groups, except for the incident and the continuous depression group [ 67 ].

Six studies investigated different courses of depression over time in people with depression at BL with or without a healthy comparison group as reference.

Two primary care studies analyzed groups with clinical depression at BL with different FU depression statuses (remission, no remission). One study [ 51 ] analyzed changes in generic QoL measures (SF-12, WHOQOL-Bref) and the disease-specific Quality of Life in Depression Scale. In this study, remission was associated with an improvement in all QoL domains, whereas QoL did not change significantly over time for the non-remitted group. Another study [ 60 ] investigated SF-12 MCS and PCS scores and reported a significant increase in MCS over time in the remitting group. MCS scores in the continuously depressed group and PCS scores in both groups improved, albeit not significantly.

Another study [ 47 ] investigated whether chronic MD in a general population sample (NESARC) was associated with domain-specific reduced QoL (SF-12). They found that chronic MD was a significant risk factor for persistently reduced QoL in all domains and for the onset of reduced QoL at FU in all domains except for physical role.

Two population-based studies further differentiated between the depressive disorders. Analyzing MCS scores (NESARC), Rubio, Olfson, Villegas, Perez-Fuentes, Wang and Blanco [ 15 ] reported a significant increase in QoL for those who remitted from MD and from dysthymia relative to those who had a persistent disorder. Rhebergen, Beekman, de Graaf, Nolen, Spijker, Hoogendijk and Penninx [ 69 ] differentiated between people with MD, double depression or dysthymia at BL who remitted until FU relative to a group without a mental health diagnosis (NEMESIS). Physical health (SF-36) was lowest at BL for double depression, dysthymia and then the MD group. Over time, the MD and double depression groups improved significantly in their physical health, while the dysthymia group did not improve significantly. QoL was significantly lower relative to healthy comparisons for all depression groups at all waves. There were no significant differences regarding physical health trajectories over time among the depressive disorder groups.

Stegenga, Kamphuis, King, Nazareth and Geerlings [ 75 ] investigated more than two MD course groups over time (remitted, intermittent and chronic MD) in association with SF-12 MCS and PCS over time in a primary care-recruited sample with BL MD (Predict study). MCS increased over time in all groups, while changes in PCS were small. Compared to those who remitted, MCS at BL was significantly lower for the chronic course group. While the intermittent group also displayed a lower mean MCS at BL, the coefficient was not significant.

Three studies investigated changes in depressive symptom levels as the independent variable and changes in QoL as outcomes in adults.

One study found no significant association between an initial change in depressive symptoms and subsequent change in QoL (EQ-VAS) in older adults recruited in primary care [ 21 ]. The two other studies analyzed changes in depressive symptoms in samples with MD at BL [ 50 , 51 ]. Chung, Tso, Yeung and Li [ 50 ] found that changes in depressive symptom levels was associated with changes in several QoL domains (SF-36: general health, vitality, social functioning, mental health and MCS). Diehr, Derleth, McKenna, Martin, Bushnell, Simon and Patrick [ 51 ] investigated whether quartiles of change in depressive symptoms were associated with changes in QoL (SF-12, QLDS and WHOQOL-Bref). Those without any change in depressive symptoms generally showed no change in QoL. For all QoL domains and scores except for SF-12 PCS, improvement in depressive symptoms over time was associated with a significant increase in QoL, while a reduction in depressive symptoms was associated with a significant reduction in QoL. Those who had the largest reduction in depressive symptoms also had the largest improvement in QoL measures.

Anxiety as an independent variable and QoL as an outcome. Two publications used a general population sample (NESARC) to investigate incident anxiety disorders [ 14 ] and the remission of anxiety disorders [ 15 ] in association with SF-12 MCS. Both studies separated generalized anxiety disorder (GAD), social anxiety disorder (SAD), panic disorder (PD) and social phobia (SP). All incident disorders were associated with a significant reduction in QoL compared to people without a history of the specific disorders. When the analysis was restricted to incident cases without comorbidities, QoL levels were not significantly different compared to people without a history of any psychiatric disorder [ 14 ]. Those who remitted from SAD showed a significant increase in QoL compared to persistent cases. While QoL improved for all remitting anxiety disorders, change scores for PD and SP were not significant [ 15 ].

Another study investigated different courses (intermittent, chronic or remitting) of obsessive compulsive disorder (OCD) and course in QoL (EQ-5D) as well as a comparison group from the general population [ 68 ]. They found that the OCD groups mostly reported a lower QoL compared to the general population. Moreover, the course groups differed regarding their QoL over time, with remitters reporting small to moderate improvements compared to the chronic group.

One study investigated changes in anxiety symptoms in association with changes in all SF-36 domains and both summary scores over time in a sample with MD at BL [ 50 ]. Changes in anxiety symptoms were significantly associated with changes in bodily pain, general health and the mental health domain.

3.4. Overview of Studies on the Association between QoL as Independent Variable and Anxiety/Depression as Outcomes

Additionally, we identified publications operationalizing QoL as the independent variable and anxiety/depression as outcomes with details on all studies reported in Table 3 . Only one study reported on change in QoL over time and change/trajectories in mental health outcomes over time. This study operationalized change in QoL as a predictor of future change in depressive symptoms over time and reported that an initial improvement in EQ-VAS was associated with a future reduction in depressive symptoms in older adults [ 21 ].

Studies on QoL as the independent variable and depression/anxiety as outcome.

Abbreviations: CES-D-20 = Center for Epidemiological Studies Depression Scale 20; BL = baseline; FU = follow-up; QoL = quality of life; CIDI = Composite International Diagnostic Interview; QLDS = Quality of Life in Depression Scale; SF-12 = Short Form 12; PCS = Physical Component Score; MCS = Mental Component Score; GDS = Geriatric Depression Scale; EQ-VAS = EQ Visual Analogue Scale; MD = mental disorder; AUDADIS-IV = Alcohol Use Disorders and Associated Disabilities Interview Schedule; SF-12v2 = Short Form 12 Version 2; PTSD = post-traumatic stress disorder; IES-15 = Impact of Event Scale 15; MADRS = Montgomery–Åsberg Depression Rating Scale; MDD = major depressive disorder; PHQ = Patient Health Questionnaire; SASC = SpLD Assessment Standards Committee; QOLS = Quality of Life Scale; CDI = Children’s Depression Inventory.

3.5. Meta-Analyses on Anxiety, Depression and SF Summary Scores

In total, eight studies on adults were included in a supplementary meta-analyses of several research questions on SF PCS and MCS in association with anxiety and depressive disorders. Forest plots for the analyses are provided in the supplementary materials (Figures S1–S10) .

Differences in SF summary scores at FU among adults with and without depressive disorders at BL. Based on a pooling of four studies [ 45 , 49 , 52 , 54 ], those with depression at BL showed lower MCS scores at FU compared to a group without depression at BL with a large effect size (SMD = −0.96, 95% CI: −1.04 to −0.88, p < 0.001, I 2 = 0.0%). PCS scores at FU were lower for the depression group compared to the non-depression group with a medium effect size (SMD = −0.68, 95% CI: −1.06 to −0.30, p < 0.001, I 2 = 94.6%). Excluding the study rated “poor” in the quality/risk of bias assessment from the pooling did not substantially affect the results (MCS: SMD = −0.96, 95% CI: −1.03 to −0.88, p < 0.001, I 2 = 0.01%; PCS: SMD = −0.63, 95% CI: −1.08 to −0.19, p < 0.01, I 2 = 96.8%).

BL differences in SF summary scores among adults with MD at BL with and without remitting courses over time. Based on a pooling of two studies [ 19 , 84 ] of samples with MD at BL, those with persistent MD at FU had significantly lower MCS at BL (SMD = −0.25, 95% CI: −0.41 to −0.10, p = 0.001, I 2 = 74.95) and PCS scores at BL (SMD = −0.24, 95% CI: −0.39 to −0.09, p = 0.002, I 2 = 73.14) compared to those who achieved remission until FU. Effect sizes were small for both summary scores.

FU differences in SF summary scores among adults with depressive and anxiety disorders at BL with and without remitting courses . Based on the pooling of two studies [ 71 , 81 ] of samples with MD and/or dysthymia, the group where the disorder had persisted/a co-morbid condition was present/had a suicide attempt until FU had significantly lower MCS scores at FU compared to the group where the disorder had remitted without treatment until FU, with a medium effect size for depressive disorders (SMD = −0.59, 95% CI: −0.75 to −0.42, p < 0.001, I 2 = 37.72) and a small effect size for anxiety disorders (SMD = −0.44, 95% CI: −0.58 to −0.30, p < 0.001, I 2 = 58.87). The SMD for PCS scores at FU was negligible in terms of effect size for both disorder groups (depressive disorders: SMD = 0.02, 95% CI: −0.24 to 0.27, p = 0.90, I 2 = 73.65; anxiety disorders: SMD = −0.09, 95% CI: −0.17 to −0.01, p = 0.03, I 2 = 0.01).

4. Discussion

4.1. main results.

This review adds to the present literature by providing an overview of longitudinal observational studies investigating the association between depression, anxiety and QoL in samples without other specific illnesses or specific treatments. Additional meta-analyses investigated group differences according to SF MCS and PCS.

While a concise synthesis of all the identified studies is challenging due to heterogeneity, the following picture emerges from studies investigating change–change associations: before the onset of disorders, QoL is already lower in disorder groups in comparison to healthy comparisons. The onset of the disorders further reduces the QoL. Remission is associated with an increase in QoL, mostly to pre-morbid levels. Additionally, some studies show that remission patterns are relevant for QoL outcomes as well. Moreover, a bi-directional effect was reported, whereby QoL is also predictive of mental health outcomes.

Evidence for a bi-directional association as well as studies showing lower QoL across the entire course of the disorders (before onset, during disorder, after disorder) relative to a healthy comparison group seem to suggest that impairments in QoL may result from a certain pre-disorder vulnerability in these groups. Longitudinal studies using general population data have investigated different hypotheses on (QoL) impairments after remission of anxiety disorders and MD [ 87 , 88 ]. One hypothesis suggests that impairments after the illness episode reflect a pre-disorder vulnerability (vulnerability or trait hypothesis), while the another states that impairments develop during the mental health episode and remain as a residual after recovery (scar hypothesis). Generally, both studies favored the vulnerability hypothesis [ 87 , 88 ]. For subgroups with recurrent anxiety disorders, scarring effects were also found for mental functioning [ 88 ]. Yet, it has to be noted that it was not the aim of our review to gather evidence for these hypotheses using QoL as an indicator, which represents an opportunity for future research.

To be able to investigate possible domain-specific differences across studies, we aimed to conduct a meta-analysis on all studies on the same research question which reported on QoL subdomains (e.g., using WHOQOL and SF). However, as described in the Methods section above, only eight studies reported comparable information on different research questions and could be included in meta-analyses. Due to the limited number of studies included in each meta-analysis, the focus on SF MCS and PCS scores, and most studies reporting on depression, the results of the meta-analyses should be viewed with caution. Keeping this in mind, our results indicate that both mental and physical QoL are significantly impacted by anxiety and depressive disorders and that the course of the disorder is also relevant for QoL outcomes. Not surprisingly, effect sizes for MCS were larger compared to PCS for most research questions. A pooling of two studies on different courses of anxiety and depressive disorders found that effect sizes for MCS at FU were of moderate size for depressive (SMD = −0.59) and of small size for anxiety disorders (SMD = −0.44), while SMDs for PCS at FU were negligible in size.

Overall, effect sizes from meta-analyses ranged from negligible to large, and heterogeneity varied considerably (I 2 between 0% and 95%). Because of the small number of studies, possible influential study-level factors (e.g., setting, operationalization of the variables, length of FU) could not be investigated in further detail by means of a meta-regression, which remains a question for future research.

4.2. Implications for Future Research

Based on the results described and study heterogeneity discussed above, we provide recommendations for future research.

First recommendation: future research should differentiate between individual disorders and focus on anxiety disorders. The majority of the studies investigated depressive disorders or symptoms. On the level of individual disorders, most focused on MD, while two studies additionally reported on dysthymia [ 15 , 69 ]. One of these investigated double depression [ 69 ]. On the level of anxiety disorders, three publications differentiated between individual anxiety disorders within the same study [ 14 , 15 , 63 ]. While it was not possible to conduct a meta-analysis comparing different anxiety disorders in our case, individual studies suggest possible disorder-specific differences when analyzing changes in QoL over time: Rubio, Olfson, Villegas, Perez-Fuentes, Wang and Blanco [ 15 ] suggest that QoL significantly improved for those remitting from GAD and SAD (compared to non-remission). QoL improved for PD and SP as well, but differences in change scores were smaller and did not reach statistical significance. The incidences of all of these disorders were associated with a significant drop in QoL [ 14 ]. In summary, future longitudinal studies should focus on anxiety disorders and generally differentiate between individual disorders to investigate possible disorder-specific differences.

Second recommendation: future research should consider trajectories of disorders/change in symptoms and changes in QoL over time. We would have liked to include a meta-analysis of disorder trajectories and change scores in QoL over time. Because of the small, diverse number of studies on this association in general and the number of assumptions that would have had to have been made for a meta-analysis, we refrained from pooling effects for this research question. In total, 17 studies investigated changes in independent variables associated with changes in outcomes. This approach has several advantages. On the one hand, different disorder or symptom trajectories can be identified. Several studies reported that QoL outcomes differ according to disorder course and the degree of change in symptoms. The focus on the change in characteristics over time in future research could additionally reduce the problem of unobserved time-constant heterogeneity in observational studies when appropriate methods are applied [ 26 ].

Third recommendation: future research should investigate individual QoL domains. Several systematic reviews on cross-sectional studies found that effect sizes differed by QoL domains [ 32 , 89 ]. For example, Olatunji, Cisler and Tolin [ 89 ] reported that health and social functioning were most impaired for anxiety disorders (compared to non-clinical controls). Comparing individuals with diabetes and depressive symptoms to those with diabetes only, Schram, Baan and Pouwer [ 32 ] reported that while SF pain scores were mild to moderately impaired, role and social functioning displayed moderate to severe impairments in those with comorbid depressive symptoms. The other scores were moderately impaired. As described above in detail, a meta-analysis using all subdomains was not feasible in this review. Further research differentiating between QoL domains would thus allow future meta-analyses to investigate whether the observed domain-specific differences reported in previous reviews of cross-sectional data can be observed in longitudinal studies as well.

Fourth recommendation: future research should consider bi-directional effects. While investigating QoL as the outcome measure and anxiety/depression as independent variables seems relatively straightforward, ten studies investigated QoL as the independent variable and anxiety/depression as outcomes. In light of possible bi-directional effects and pre-existing vulnerability suggested by individual studies, future research considering QoL as an independent variable could inform a deeper understanding of this complex association.

4.3. Strengths and Limitations

A strength of this work is the transparent methodological process: the review was prospectively registered with PROSPERO and a study protocol was published [ 34 ]. Two reviewers were included in screening, data extraction and quality assessment processes. There were no limitations regarding the time or location of the publications. Moreover, all versions of the ICD/DSM and validated questionnaires were considered eligible to identify anxiety or depression. Another strength is the thorough literature search that enabled us to identify all relevant studies. Additionally, we did not limit the age range and were therefore able to shed light on studies investigating children/adolescents. Moreover, some studies could be pooled using random-effects meta-analyses, which allows for stronger conclusions regarding effect sizes compared to individual studies. Besides the content analysis, this review emphasizes difficulties in meta-analysis from observational, longitudinal studies. We hope that our work can facilitate discussion on this topic.

The study has some limitations. We did not limit our search to specific research questions, which led to the inclusion of heterogeneous studies. Heterogeneity particularly stemmed from the operationalization of the variables of interest. Due to this, a concise narrative synthesis of all results was not feasible. The positive aspect of this broad focus is that it allowed us to provide an overview of studies and research questions analyzed and to formulate more nuanced recommendations for future research. We have to acknowledge that there is an abundance of QoL assessments used in medicine and health sciences [ 37 ]. The list applied in this work was derived with respect to previous relevant reviews on QoL research. It was not designed to be fully comprehensive or exhaustive. Rather, it provided us with a working definition for this review and helped to enhance the transparency of our selection processes. Additionally, because we included validated QoL measures frequently used in research, we assume that exclusion would particularly have been the case for novel or study-specific measures. Finally, the focus on peer-reviewed literature means that studies in other languages and gray literature were not considered. Nonetheless, this focus on literature published in peer-reviewed journals should ensure a certain scientific quality.

5. Conclusions and Relevance for Clinical Practice

Overall, the results indicate that QoL is lower before the onset of anxiety and depressive disorders, further reduces upon onset of the disorders and generally improves with remission to pre-morbid levels. Moreover, disorder course (e.g., remitted, intermittent, chronic) seems to play an important role; however, only a few studies analyzed this. Changes in anxiety and depressive symptoms were also associated with changes in QoL over time. Meta-analyses found that effect sizes were larger for MCS relative to PCS, highlighting the relevance of differentiation between QoL domains. While our review identified some gaps in the current literature and made recommendations for future research, the following should be noted for clinical practice. On the one hand, an improvement in mental health is associated with better QoL, which emphasizes the relevance of support during the disorders. This is also shown by meta-analyses, which show that cognitive behavioral therapy additionally improves QoL [ 90 , 91 ]. Moreover, the results indicate reduced QoL even before disorder onset, highlighting the relevance of early preventive measures in vulnerable groups. In line with this, studies on school-based prevention programs show a significant reduction in anxiety and depressive symptoms [ 92 , 93 ], and psychosocial prevention programs may additionally improve QoL [ 94 ].

During the COVID-19 pandemic, it is of high relevance to tackle the arising challenges associated with this pandemic. For example, it is important to face the high prevalence rates of both depression and anxiety with appropriate measures.

Acknowledgments

The authors would like to thank Elzbieta Kuzma for her consultation (Albertinen-Haus Centre for Geriatrics and Gerontology, University of Hamburg, Hamburg, Germany; University of Exeter Medical School, Exeter, UK).

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijerph182212022/s1 , Table S1: detailed descriptive information for included studies ( n = 47); Figure S1: forest plot for differences in SF MCS at FU among adults with and without depressive disorders at BL; Figure S2: forest plot for differences in SF PCS at FU among adults with and without depressive disorders at BL; Figure S3: forest plot for differences in SF MCS at FU among adults with and without depressive disorders at BL (sensitivity analysis); Figure S4: forest plot for differences in SF PCS at FU among adults with and without depressive disorders at BL (sensitivity analysis); Figure S5: forest plot for BL differences in SF MCS among adults with MD at BL with and without remitting courses over time; Figure S6: forest plot for BL differences in SF PCS among adults with MD at BL with and without remitting courses over time; Figure S7: forest plot for FU differences in SF MCS among adults with depressive disorders at BL with and without remitting courses; Figure S8: forest plot for FU differences in SF PCS among adults with depressive disorders at BL with and without remitting courses; Figure S9: forest plot for FU differences in SF MCS among adults with anxiety disorders at BL with and without remitting courses; Figure S10: forest plot for FU differences in SF PCS among adults with anxiety disorders at BL with and without remitting courses.

Author Contributions

J.K.H.: conceptualization of research question; development of search strategy; study screening and selection; risk of bias/quality assessment; study synthesis; writing—original draft, review and editing; H.-H.K.: conceptualization of research question; writing—review and editing; E.Q.: study screening and selection; risk of bias/quality assessment; writing—review and editing; A.H.: conceptualization of research question; development of search strategy; study screening and selection (third party); study synthesis; writing—review and editing. All authors have read and agreed to the published version of the manuscript.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Institutional Review Board Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • My Account Login
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Systematic Review
  • Open access
  • Published: 20 July 2022

The serotonin theory of depression: a systematic umbrella review of the evidence

  • Joanna Moncrieff 1 , 2 ,
  • Ruth E. Cooper 3 ,
  • Tom Stockmann 4 ,
  • Simone Amendola 5 ,
  • Michael P. Hengartner 6 &
  • Mark A. Horowitz 1 , 2  

Molecular Psychiatry volume  28 ,  pages 3243–3256 ( 2023 ) Cite this article

1.21m Accesses

207 Citations

9455 Altmetric

Metrics details

  • Diagnostic markers

The serotonin hypothesis of depression is still influential. We aimed to synthesise and evaluate evidence on whether depression is associated with lowered serotonin concentration or activity in a systematic umbrella review of the principal relevant areas of research. PubMed, EMBASE and PsycINFO were searched using terms appropriate to each area of research, from their inception until December 2020. Systematic reviews, meta-analyses and large data-set analyses in the following areas were identified: serotonin and serotonin metabolite, 5-HIAA, concentrations in body fluids; serotonin 5-HT 1A receptor binding; serotonin transporter (SERT) levels measured by imaging or at post-mortem; tryptophan depletion studies; SERT gene associations and SERT gene-environment interactions. Studies of depression associated with physical conditions and specific subtypes of depression (e.g. bipolar depression) were excluded. Two independent reviewers extracted the data and assessed the quality of included studies using the AMSTAR-2, an adapted AMSTAR-2, or the STREGA for a large genetic study. The certainty of study results was assessed using a modified version of the GRADE. We did not synthesise results of individual meta-analyses because they included overlapping studies. The review was registered with PROSPERO (CRD42020207203). 17 studies were included: 12 systematic reviews and meta-analyses, 1 collaborative meta-analysis, 1 meta-analysis of large cohort studies, 1 systematic review and narrative synthesis, 1 genetic association study and 1 umbrella review. Quality of reviews was variable with some genetic studies of high quality. Two meta-analyses of overlapping studies examining the serotonin metabolite, 5-HIAA, showed no association with depression (largest n  = 1002). One meta-analysis of cohort studies of plasma serotonin showed no relationship with depression, and evidence that lowered serotonin concentration was associated with antidepressant use ( n  = 1869). Two meta-analyses of overlapping studies examining the 5-HT 1A receptor (largest n  = 561), and three meta-analyses of overlapping studies examining SERT binding (largest n  = 1845) showed weak and inconsistent evidence of reduced binding in some areas, which would be consistent with increased synaptic availability of serotonin in people with depression, if this was the original, causal abnormaly. However, effects of prior antidepressant use were not reliably excluded. One meta-analysis of tryptophan depletion studies found no effect in most healthy volunteers ( n  = 566), but weak evidence of an effect in those with a family history of depression ( n  = 75). Another systematic review ( n  = 342) and a sample of ten subsequent studies ( n  = 407) found no effect in volunteers. No systematic review of tryptophan depletion studies has been performed since 2007. The two largest and highest quality studies of the SERT gene, one genetic association study ( n  = 115,257) and one collaborative meta-analysis ( n  = 43,165), revealed no evidence of an association with depression, or of an interaction between genotype, stress and depression. The main areas of serotonin research provide no consistent evidence of there being an association between serotonin and depression, and no support for the hypothesis that depression is caused by lowered serotonin activity or concentrations. Some evidence was consistent with the possibility that long-term antidepressant use reduces serotonin concentration.

Introduction

The idea that depression is the result of abnormalities in brain chemicals, particularly serotonin (5-hydroxytryptamine or 5-HT), has been influential for decades, and provides an important justification for the use of antidepressants. A link between lowered serotonin and depression was first suggested in the 1960s [ 1 ], and widely publicised from the 1990s with the advent of the Selective Serotonin Reuptake Inhibitor (SSRI) antidepressants [ 2 , 3 , 4 ]. Although it has been questioned more recently [ 5 , 6 ], the serotonin theory of depression remains influential, with principal English language textbooks still giving it qualified support [ 7 , 8 ], leading researchers endorsing it [ 9 , 10 , 11 ], and much empirical research based on it [ 11 , 12 , 13 , 14 ]. Surveys suggest that 80% or more of the general public now believe it is established that depression is caused by a ‘chemical imbalance’ [ 15 , 16 ]. Many general practitioners also subscribe to this view [ 17 ] and popular websites commonly cite the theory [ 18 ].

It is often assumed that the effects of antidepressants demonstrate that depression must be at least partially caused by a brain-based chemical abnormality, and that the apparent efficacy of SSRIs shows that serotonin is implicated. Other explanations for the effects of antidepressants have been put forward, however, including the idea that they work via an amplified placebo effect or through their ability to restrict or blunt emotions in general [ 19 , 20 ].

Despite the fact that the serotonin theory of depression has been so influential, no comprehensive review has yet synthesised the relevant evidence. We conducted an ‘umbrella’ review of the principal areas of relevant research, following the model of a similar review examining prospective biomarkers of major depressive disorder [ 21 ]. We sought to establish whether the current evidence supports a role for serotonin in the aetiology of depression, and specifically whether depression is associated with indications of lowered serotonin concentrations or activity.

Search strategy and selection criteria

The present umbrella review was reported in accordance with the 2009 PRISMA statement [ 22 ]. The protocol was registered with PROSPERO in December 2020 (registration number CRD42020207203) ( https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=207203 ). This was subsequently updated to reflect our decision to modify the quality rating system for some studies to more appropriately appraise their quality, and to include a modified GRADE to assess the overall certainty of the findings in each category of the umbrella review.

In order to cover the different areas and to manage the large volume of research that has been conducted on the serotonin system, we conducted an ‘umbrella’ review. Umbrella reviews survey existing systematic reviews and meta-analyses relevant to a research question and represent one of the highest levels of evidence synthesis available [ 23 ]. Although they are traditionally restricted to systematic reviews and meta-analyses, we aimed to identify the best evidence available. Therefore, we also included some large studies that combined data from individual studies but did not employ conventional systematic review methods, and one large genetic study. The latter used nationwide databases to capture more individuals than entire meta-analyses, so is likely to provide even more reliable evidence than syntheses of individual studies.

We first conducted a scoping review to identify areas of research consistently held to provide support for the serotonin hypothesis of depression. Six areas were identified, addressing the following questions: (1) Serotonin and the serotonin metabolite 5-HIAA–whether there are lower levels of serotonin and 5-HIAA in body fluids in depression; (2) Receptors - whether serotonin receptor levels are altered in people with depression; (3) The serotonin transporter (SERT) - whether there are higher levels of the serotonin transporter in people with depression (which would lower synaptic levels of serotonin); (4) Depletion studies - whether tryptophan depletion (which lowers available serotonin) can induce depression; (5) SERT gene – whether there are higher levels of the serotonin transporter gene in people with depression; (6) Whether there is an interaction between the SERT gene and stress in depression.

We searched for systematic reviews, meta-analyses, and large database studies in these six areas in PubMed, EMBASE and PsycINFO using the Healthcare Databases Advanced Search tool provided by Health Education England and NICE (National Institute for Health and Care Excellence). Searches were conducted until December 2020.

We used the following terms in all searches: (depress* OR affective OR mood) AND (systematic OR meta-analysis), and limited searches to title and abstract, since not doing so produced numerous irrelevant hits. In addition, we used terms specific to each area of research (full details are provided in Table  S1 , Supplement). We also searched citations and consulted with experts.

Inclusion criteria were designed to identify the best available evidence in each research area and consisted of:

Research synthesis including systematic reviews, meta-analysis, umbrella reviews, individual patient meta-analysis and large dataset analysis.

Studies that involve people with depressive disorders or, for experimental studies (tryptophan depletion), those in which mood symptoms are measured as an outcome.

Studies of experimental procedures (tryptophan depletion) involving a sham or control condition.

Studies published in full in peer reviewed literature.

Where more than five systematic reviews or large analyses exist, the most recent five are included.

Exclusion criteria consisted of:

Animal studies.

Studies exclusively concerned with depression in physical conditions (e.g. post stroke or Parkinson’s disease) or exclusively focusing on specific subtypes of depression such as postpartum depression, depression in children, or depression in bipolar disorder.

No language or date restrictions were applied. In areas in which no systematic review or meta-analysis had been done within the last 10 years, we also selected the ten most recent studies at the time of searching (December 2020) for illustration of more recent findings. We performed this search using the same search string for this domain, without restricting it to systematic reviews and meta-analyses.

Data analysis

Each member of the team was allocated one to three domains of serotonin research to search and screen for eligible studies using abstract and full text review. In case of uncertainty, the entire team discussed eligibility to reach consensus.

For included studies, data were extracted by two reviewers working independently, and disagreement was resolved by consensus. Authors of papers were contacted for clarification when data was missing or unclear.

We extracted summary effects, confidence intervals and measures of statistical significance where these were reported, and, where relevant, we extracted data on heterogeneity. For summary effects in the non-genetic studies, preference was given to the extraction and reporting of effect sizes. Mean differences were converted to effect sizes where appropriate data were available.

We did not perform a meta-analysis of the individual meta-analyses in each area because they included overlapping studies [ 24 ]. All extracted data is presented in Table  1 . Sensitivity analyses were reported where they had substantial bearing on interpretation of findings.

The quality rating of systematic reviews and meta-analyses was assessed using AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews) [ 25 ]. For two studies that did not employ conventional systematic review methods [ 26 , 27 ] we used a modified version of the AMSTAR-2 (see Table  S3 ). For the genetic association study based on a large database analysis we used the STREGA assessment (STrengthening the REporting of Genetic Association Studies) (Table  S4 ) [ 28 ]. Each study was rated independently by at least two authors. We report ratings of individual items on the relevant measure, and the percentage of items that were adequately addressed by each study (Table  1 , with further detail in Tables  S3 and S4 ).

Alongside quality ratings, two team members (JM, MAH) rated the certainty of the results of each study using a modified version of the GRADE guidelines [ 29 ]. Following the approach of Kennis et al. [ 21 ], we devised six criteria relevant to the included studies: whether a unified analysis was conducted on original data; whether confounding by antidepressant use was adequately addressed; whether outcomes were pre-specified; whether results were consistent or heterogeneity was adequately addressed if present; whether there was a likelihood of publication bias; and sample size. The importance of confounding by effects of current or past antidepressant use has been highlighted in several studies [ 30 , 31 ]. The results of each study were scored 1 or 0 according to whether they fulfilled each criteria, and based on these ratings an overall judgement was made about the certainty of evidence across studies in each of the six areas of research examined. The certainty of each study was based on an algorithm that prioritised sample size and uniform analysis using original data (explained more fully in the supplementary material), following suggestions that these are the key aspects of reliability [ 27 , 32 ]. An assessment of the overall certainty of each domain of research examining the role of serotonin was determined by consensus of at least two authors and a direction of effect indicated.

Search results and quality rating

Searching identified 361 publications across the 6 different areas of research, among which seventeen studies fulfilled inclusion criteria (see Fig.  1 and Table  S1 for details of the selection process). Included studies, their characteristics and results are shown in Table  1 . As no systematic review or meta-analysis had been performed within the last 10 years on serotonin depletion, we also identified the 10 latest studies for illustration of more recent research findings (Table  2 ).

figure 1

Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow diagramme.

Quality ratings are summarised in Table  1 and reported in detail in Tables  S2 – S3 . The majority (11/17) of systematic reviews and meta-analyses satisfied less than 50% of criteria. Only 31% adequately assessed risk of bias in individual studies (a further 44% partially assessed this), and only 50% adequately accounted for risk of bias when interpreting the results of the review. One collaborative meta-analysis of genetic studies was considered to be of high quality due to the inclusion of several measures to ensure consistency and reliability [ 27 ]. The large genetic analysis of the effect of SERT polymorphisms on depression, satisfied 88% of the STREGA quality criteria [ 32 ].

Serotonin and 5-HIAA

Serotonin can be measured in blood, plasma, urine and CSF, but it is rapidly metabolised to 5-hydroxyindoleacetic acid (5-HIAA). CSF is thought to be the ideal resource for the study of biomarkers of putative brain diseases, since it is in contact with brain interstitial fluid [ 33 ]. However, collecting CSF samples is invasive and carries some risk, hence large-scale studies are scarce.

Three studies fulfilled inclusion criteria (Table  1 ). One meta-analysis of three large observational cohort studies of post-menopausal women, revealed lower levels of plasma 5-HT in women with depression, which did not, however, reach statistical significance of p  < 0.05 after adjusting for multiple comparisons. Sensitivity analyses revealed that antidepressants were strongly associated with lower serotonin levels independently of depression.

Two meta-analyses of a total of 19 studies of 5-HIAA in CSF (seven studies were included in both) found no evidence of an association between 5-HIAA concentrations and depression.

Fourteen different serotonin receptors have been identified, with most research on depression focusing on the 5-HT 1A receptor [ 11 , 34 ]. Since the functions of other 5-HT receptors and their relationship to depression have not been well characterised, we restricted our analysis to data on 5-HT 1A receptors [ 11 , 34 ]. 5-HT 1A receptors, known as auto-receptors, inhibit the release of serotonin pre-synaptically [ 35 ], therefore, if depression is the result of reduced serotonin activity caused by abnormalities in the 5-HT 1A receptor, people with depression would be expected to show increased activity of 5-HT 1A receptors compared to those without [ 36 ].

Two meta-analyses satisfied inclusion criteria, involving five of the same studies [ 37 , 38 ] (see Table  1 ). The majority of results across the two analyses suggested either no difference in 5-HT 1A receptors between people with depression and controls, or a lower level of these inhibitory receptors, which would imply higher concentrations or activity of serotonin in people with depression. Both meta-analyses were based on studies that predominantly involved patients who were taking or had recently taken (within 1–3 weeks of scanning) antidepressants or other types of psychiatric medication, and both sets of authors commented on the possible influence of prior or current medication on findings. In addition, one analysis was of very low quality [ 37 ], including not reporting on the numbers involved in each analysis and using one-sided p-values, and one was strongly influenced by three studies and publication bias was present [ 38 ].

The serotonin transporter (SERT)

The serotonin transporter protein (SERT) transports serotonin out of the synapse, thereby lowering the availability of serotonin in the synapse [ 39 , 40 ]. Animals with an inactivated gene for SERT have higher levels of extra-cellular serotonin in the brain than normal [ 41 , 42 , 43 ] and SSRIs are thought to work by inhibiting the action of SERT, and thus increasing levels of serotonin in the synaptic cleft [ 44 ]. Although changes in SERT may be a marker for other abnormalities, if depression is caused by low serotonin availability or activity, and if SERT is the origin of that deficit, then the amount or activity of SERT would be expected to be higher in people with depression compared to those without [ 40 ]. SERT binding potential is an index of the concentration of the serotonin transporter protein and SERT concentrations can also be measured post-mortem.

Three overlapping meta-analyses based on a total of 40 individual studies fulfilled inclusion criteria (See Table  1 ) [ 37 , 39 , 45 ]. Overall, the data indicated possible reductions in SERT binding in some brain areas, although areas in which effects were detected were not consistent across the reviews. In addition, effects of antidepressants and other medication cannot be ruled out, since most included studies mainly or exclusively involved people who had a history of taking antidepressants or other psychiatric medications. Only one meta-analysis tested effects of antidepressants, and although results were not influenced by the percentage of drug-naïve patients in each study, numbers were small so it is unlikely that medication-related effects would have been reliably detected [ 45 ]. All three reviews cited evidence from animal studies that antidepressant treatment reduces SERT [ 46 , 47 , 48 ]. None of the analyses corrected for multiple testing, and one review was of very low quality [ 37 ]. If the results do represent a positive finding that is independent of medication, they would suggest that depression is associated with higher concentrations or activity of serotonin.

Depletion studies

Tryptophan depletion using dietary means or chemicals, such as parachlorophenylalanine (PCPA), is thought to reduce serotonin levels. Since PCPA is potentially toxic, reversible tryptophan depletion using an amino acid drink that lacks tryptophan is the most commonly used method and is thought to affect serotonin within 5–7 h of ingestion. Questions remain, however, about whether either method reliably reduces brain serotonin, and about other effects including changes in brain nitrous oxide, cerebrovascular changes, reduced BDNF and amino acid imbalances that may be produced by the manipulations and might explain observed effects independent of possible changes in serotonin activity [ 49 ].

One meta-analysis and one systematic review fulfilled inclusion criteria (see Table  1 ). Data from studies involving volunteers mostly showed no effect, including a meta-analysis of parallel group studies [ 50 ]. In a small meta-analysis of within-subject studies involving 75 people with a positive family history, a minor effect was found, with people given the active depletion showing a larger decrease in mood than those who had a sham procedure [ 50 ]. Across both reviews, studies involving people diagnosed with depression showed slightly greater mood reduction following tryptophan depletion than sham treatment overall, but most participants had taken or were taking antidepressants and participant numbers were small [ 50 , 51 ].

Since these research syntheses were conducted more than 10 years ago, we searched for a systematic sample of ten recently published studies (Table  2 ). Eight studies conducted with healthy volunteers showed no effects of tryptophan depletion on mood, including the only two parallel group studies. One study presented effects in people with and without a family history of depression, and no differences were apparent in either group [ 52 ]. Two cross-over studies involving people with depression and current or recent use of antidepressants showed no convincing effects of a depletion drink [ 53 , 54 ], although one study is reported as positive mainly due to finding an improvement in mood in the group given the sham drink [ 54 ].

SERT gene and gene-stress interactions

A possible link between depression and the repeat length polymorphism in the promoter region of the SERT gene (5-HTTLPR), specifically the presence of the short repeats version, which causes lower SERT mRNA expression, has been proposed [ 55 ]. Interestingly, lower levels of SERT would produce higher levels of synaptic serotonin. However, more recently, this hypothesis has been superseded by a focus on the interaction effect between this polymorphism, depression and stress, with the idea that the short version of the polymorphism may only give rise to depression in the presence of stressful life events [ 55 , 56 ]. Unlike other areas of serotonin research, numerous systematic reviews and meta-analyses of genetic studies have been conducted, and most recently a very large analysis based on a sample from two genetic databanks. Details of the five most recent studies that have addressed the association between the SERT gene and depression, and the interaction effect are detailed in Table  1 .

Although some earlier meta-analyses of case-control studies showed a statistically significant association between the 5-HTTLPR and depression in some ethnic groups [ 57 , 58 ], two recent large, high quality studies did not find an association between the SERT gene polymorphism and depression [ 27 , 32 ]. These two studies consist of  by far the largest and most comprehensive study to date [ 32 ] and a high-quality meta-analysis that involved a consistent re-analysis of primary data across all conducted studies, including previously unpublished data, and other comprehensive quality checks [ 27 , 59 ] (see Table  1 ).

Similarly, early studies based on tens of thousands of participants suggested a statistically significant interaction between the SERT gene, forms of stress or maltreatment and depression [ 60 , 61 , 62 ], with a small odds ratio in the only study that reported this (1.18, 95% CI 1.09 to 1.28) [ 62 ]. However, the two recent large, high-quality studies did not find an interaction between the SERT gene and stress in depression (Border et al [ 32 ] and Culverhouse et al.) [ 27 ] (see Table  1 ).

Overall results

Table  3 presents the modified GRADE ratings for each study and the overall rating of the strength of evidence in each area. Areas of research that provided moderate or high certainty of evidence such as the studies of plasma serotonin and metabolites and the genetic and gene-stress interaction studies all showed no association between markers of serotonin activity and depression. Some other areas suggested findings consistent with increased serotonin activity, but evidence was of very low certainty, mainly due to small sample sizes and possible residual confounding by current or past antidepressant use. One area - the tryptophan depletion studies - showed very low certainty evidence of lowered serotonin activity or availability in a subgroup of volunteers with a family history of depression. This evidence was considered very low certainty as it derived from a subgroup of within-subject studies, numbers were small, and there was no information on medication use, which may have influenced results. Subsequent research has not confirmed an effect with numerous negative studies in volunteers.

Our comprehensive review of the major strands of research on serotonin shows there is no convincing evidence that depression is associated with, or caused by, lower serotonin concentrations or activity. Most studies found no evidence of reduced serotonin activity in people with depression compared to people without, and methods to reduce serotonin availability using tryptophan depletion do not consistently lower mood in volunteers. High quality, well-powered genetic studies effectively exclude an association between genotypes related to the serotonin system and depression, including a proposed interaction with stress. Weak evidence from some studies of serotonin 5-HT 1A receptors and levels of SERT points towards a possible association between increased serotonin activity and depression. However, these results are likely to be influenced by prior use of antidepressants and its effects on the serotonin system [ 30 , 31 ]. The effects of tryptophan depletion in some cross-over studies involving people with depression may also be mediated by antidepressants, although these are not consistently found [ 63 ].

The chemical imbalance theory of depression is still put forward by professionals [ 17 ], and the serotonin theory, in particular, has formed the basis of a considerable research effort over the last few decades [ 14 ]. The general public widely believes that depression has been convincingly demonstrated to be the result of serotonin or other chemical abnormalities [ 15 , 16 ], and this belief shapes how people understand their moods, leading to a pessimistic outlook on the outcome of depression and negative expectancies about the possibility of self-regulation of mood [ 64 , 65 , 66 ]. The idea that depression is the result of a chemical imbalance also influences decisions about whether to take or continue antidepressant medication and may discourage people from discontinuing treatment, potentially leading to lifelong dependence on these drugs [ 67 , 68 ].

As with all research synthesis, the findings of this umbrella review are dependent on the quality of the included studies, and susceptible to their limitations. Most of the included studies were rated as low quality on the AMSTAR-2, but the GRADE approach suggested some findings were reasonably robust. Most of the non-genetic studies did not reliably exclude the potential effects of previous antidepressant use and were based on relatively small numbers of participants. The genetic studies, in particular, illustrate the importance of methodological rigour and sample size. Whereas some earlier, lower quality, mostly smaller studies produced marginally positive findings, these were not confirmed in better-conducted, larger and more recent studies [ 27 , 32 ]. The identification of depression and assessment of confounders and interaction effects were limited by the data available in the original studies on which the included reviews and meta-analyses were based. Common methods such as the categorisation of continuous measures and application of linear models to non-linear data may have led to over-estimation or under-estimation of effects [ 69 , 70 ], including the interaction between stress and the SERT gene. The latest systematic review of tryptophan depletion studies was conducted in 2007, and there has been considerable research produced since then. Hence, we provided a snapshot of the most recent evidence at the time of writing, but this area requires an up to date, comprehensive data synthesis. However, the recent studies were consistent with the earlier meta-analysis with little evidence for an effect of tryptophan depletion on mood.

Although umbrella reviews typically restrict themselves to systematic reviews and meta-analyses, we aimed to provide the most comprehensive possible overview. Therefore, we chose to include meta-analyses that did not involve a systematic review and a large genetic association study on the premise that these studies contribute important data on the question of whether the serotonin hypothesis of depression is supported. As a result, the AMSTAR-2 quality rating scale, designed to evaluate the quality of conventional systematic reviews, was not easily applicable to all studies and had to be modified or replaced in some cases.

One study in this review found that antidepressant use was associated with a reduction of plasma serotonin [ 26 ], and it is possible that the evidence for reductions in SERT density and 5-HT 1A receptors in some of the included imaging study reviews may reflect compensatory adaptations to serotonin-lowering effects of prior antidepressant use. Authors of one meta-analysis also highlighted evidence of 5-HIAA levels being reduced after long-term antidepressant treatment [ 71 ]. These findings suggest that in the long-term antidepressants might produce compensatory changes [ 72 ] that are opposite to their acute effects [ 73 , 74 ]. Lowered serotonin availability has also been demonstrated in animal studies following prolonged antidepressant administration [ 75 ]. Further research is required to clarify the effects of different drugs on neurochemical systems, including the serotonin system, especially during and after long-term use, as well as the physical and psychological consequences of such effects.

This review suggests that the huge research effort based on the serotonin hypothesis has not produced convincing evidence of a biochemical basis to depression. This is consistent with research on many other biological markers [ 21 ]. We suggest it is time to acknowledge that the serotonin theory of depression is not empirically substantiated.

Data availability

All extracted data is available in the paper and supplementary materials. Further information about the decision-making for each rating for categories of the AMSTAR-2 and STREGA are available on request.

Coppen A. The biochemistry of affective disorders. Br J Psychiatry. 1967;113:1237–64.

Article   PubMed   Google Scholar  

American Psychiatric Association. What Is Psychiatry? 2021. https://www.psychiatry.org/patients-families/what-is-psychiatry-menu .

GlaxoSmithKline. Paxil XR. 2009. www.Paxilcr.com (site no longer available). Last accessed 27th Jan 2009.

Eli Lilly. Prozac - How it works. 2006. www.prozac.com/how_prozac/how_it_works.jsp?reqNavId=2.2 . (site no longer available). Last accessed 10th Feb 2006.

Healy D. Serotonin and depression. BMJ: Br Med J. 2015;350:h1771.

Article   Google Scholar  

Pies R. Psychiatry’s New Brain-Mind and the Legend of the “Chemical Imbalance.” 2011. https://www.psychiatrictimes.com/view/psychiatrys-new-brain-mind-and-legend-chemical-imbalance . Accessed March 2, 2021.

Geddes JR, Andreasen NC, Goodwin GM. New Oxford Textbook of Psychiatry. Oxford, UK: Oxford University Press; 2020.

Book   Google Scholar  

Sadock BJ, Sadock VA, Ruiz P. Kaplan & Sadock’s Comprehensive Textbook of Psychiatry. 10th Editi. Lippincott Williams & Wilkins (LWW); 2017.

Cowen PJ, Browning M. What has serotonin to do with depression? World Psychiatry. 2015;14:158–60.

Article   PubMed   PubMed Central   Google Scholar  

Harmer CJ, Duman RS, Cowen PJ. How do antidepressants work? New perspectives for refining future treatment approaches. Lancet Psychiatry. 2017;4:409–18.

Yohn CN, Gergues MM, Samuels BA. The role of 5-HT receptors in depression. Mol Brain. 2017;10:28.

Hahn A, Haeusler D, Kraus C, Höflich AS, Kranz GS, Baldinger P, et al. Attenuated serotonin transporter association between dorsal raphe and ventral striatum in major depression. Hum Brain Mapp. 2014;35:3857–66.

Amidfar M, Colic L, Kim MWAY-K. Biomarkers of major depression related to serotonin receptors. Curr Psychiatry Rev. 2018;14:239–44.

Albert PR, Benkelfat C, Descarries L. The neurobiology of depression—revisiting the serotonin hypothesis. I. Cellular and molecular mechanisms. Philos Trans R Soc Lond B Biol Sci. 2012;367:2378–81.

Pilkington PD, Reavley NJ, Jorm AF. The Australian public’s beliefs about the causes of depression: associated factors and changes over 16 years. J Affect Disord. 2013;150:356–62.

Pescosolido BA, Martin JK, Long JS, Medina TR, Phelan JC, Link BG. A disease like any other? A decade of change in public reactions to schizophrenia, depression, and alcohol dependence. Am J Psychiatry. 2010;167:1321–30.

Read J, Renton J, Harrop C, Geekie J, Dowrick C. A survey of UK general practitioners about depression, antidepressants and withdrawal: implementing the 2019 Public Health England report. Therapeutic Advances in. Psychopharmacology. 2020;10:204512532095012.

Google Scholar  

Demasi M, Gøtzsche PC. Presentation of benefits and harms of antidepressants on websites: A cross-sectional study. Int J Risk Saf Med. 2020;31:53–65.

Jakobsen JC, Gluud C, Kirsch I. Should antidepressants be used for major depressive disorder? BMJ Evidence-Based. Medicine. 2020;25:130–130.

Moncrieff J, Cohen D. Do antidepressants cure or create abnormal brain states? PLoS Med. 2006;3:e240.

Kennis M, Gerritsen L, van Dalen M, Williams A, Cuijpers P, Bockting C. Prospective biomarkers of major depressive disorder: a systematic review and meta-analysis. Mol Psychiatry. 2020;25:321–38.

Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097.

Fusar-Poli P, Radua J. Ten simple rules for conducting umbrella reviews. Evid Based Ment Health. 2018;21:95–100.

Pollock M, Fernandes RM, Becker LA, Pieper D, Hartling L. Chapter V: Overviews of Reviews. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.2,. version 6.Cochrane; 2021.

Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008.

Huang T, Balasubramanian R, Yao Y, Clish CB, Shadyab AH, Liu B, et al. Associations of depression status with plasma levels of candidate lipid and amino acid metabolites: a meta-analysis of individual data from three independent samples of US postmenopausal women. Mol Psychiatry. 2020;2020. https://doi.org/10.1038/s41380-020-00870-9 .

Culverhouse RC, Saccone NL, Horton AC, Ma Y, Anstey KJ, Banaschewski T, et al. Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression. Mol Psychiatry. 2018;23:133–42.

Little J, Higgins JPT, Ioannidis JPA, Moher D, Gagnon F, von Elm E, et al. STrengthening the REporting of Genetic Association Studies (STREGA)— An Extension of the STROBE Statement. PLoS Med. 2009;6:e1000022.

Guyatt GH, Oxman AD, Kunz R, Vist GE, Falck-Ytter Y, Schünemann HJ. What is quality of evidence and why is it important to clinicians? BMJ. 2008;336:995–8.

Yoon HS, Hattori K, Ogawa S, Sasayama D, Ota M, Teraishi T, et al. Relationships of cerebrospinal fluid monoamine metabolite levels with clinical variables in major depressive disorder. J Clin Psychiatry. 2017;78:e947–56.

Kugaya A, Seneca NM, Snyder PJ, Williams SA, Malison RT, Baldwin RM, et al. Changes in human in vivo serotonin and dopamine transporter availabilities during chronic antidepressant administration. Neuropsychopharmacology. 2003;28:413–20.

Border R, Johnson EC, Evans LM, Smolen A, Berley N, Sullivan PF, et al. No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. Am J Psychiatry. 2019;176:376–87.

Ogawa S, Tsuchimine S, Kunugi H. Cerebrospinal fluid monoamine metabolite concentrations in depressive disorder: A meta-analysis of historic evidence. J Psychiatr Res. 2018;105:137–46.

Nautiyal KM, Hen R. Serotonin receptors in depression: from A to B. F1000Res. 2017;6:123.

Rojas PS, Neira D, Muñoz M, Lavandero S, Fiedler JL. Serotonin (5‐HT) regulates neurite outgrowth through 5‐HT1A and 5‐HT7 receptors in cultured hippocampal neurons. J Neurosci Res. 2014;92:1000–9.

Kaufman J, DeLorenzo C, Choudhury S, Parsey RV. The 5-HT1A receptor in Major Depressive Disorder. Eur Neuropsychopharmacol. 2016;26:397–410.

Nikolaus S, Müller H-W, Hautzel H. Different patterns of 5-HT receptor and transporter dysfunction in neuropsychiatric disorders – a comparative analysis of in vivo imaging findings. Rev Neurosci. 2016;27:27–59.

Wang L, Zhou C, Zhu D, Wang X, Fang L, Zhong J, et al. Serotonin-1A receptor alterations in depression: A meta-analysis of molecular imaging studies. BMC Psychiatry. 2016;16:1–9.

Kambeitz JP, Howes OD. The serotonin transporter in depression: Meta-analysis of in vivo and post mortem findings and implications for understanding and treating depression. J Affect Disord. 2015;186:358–66.

Meyer JH. Imaging the serotonin transporter during major depressive disorder and antidepressant treatment. J Psychiatry Neurosci. 2007;32:86–102.

PubMed   PubMed Central   Google Scholar  

Mathews TA, Fedele DE, Coppelli FM, Avila AM, Murphy DL, Andrews AM. Gene dose-dependent alterations in extraneuronal serotonin but not dopamine in mice with reduced serotonin transporter expression. J Neurosci Methods. 2004;140:169–81.

Shen H-W, Hagino Y, Kobayashi H, Shinohara-Tanaka K, Ikeda K, Yamamoto H, et al. Regional differences in extracellular dopamine and serotonin assessed by in vivo microdialysis in mice lacking dopamine and/or serotonin transporters. Neuropsychopharmacology. 2004;29:1790–9.

Hagino Y, Takamatsu Y, Yamamoto H, Iwamura T, Murphy DL, Uhl GR, et al. Effects of MDMA on extracellular dopamine and serotonin levels in mice lacking dopamine and/or serotonin transporters. Curr Neuropharmacol. 2011;9:91–5.

Zhou Z, Zhen J, Karpowich NK, Law CJ, Reith MEA, Wang D-N. Antidepressant specificity of serotonin transporter suggested by three LeuT-SSRI structures. Nat Struct Mol Biol. 2009;16:652–7.

Gryglewski G, Lanzenberger R, Kranz GS, Cumming P. Meta-analysis of molecular imaging of serotonin transporters in major depression. J Cereb Blood Flow Metab. 2014;34:1096–103.

Benmansour S, Owens WA, Cecchi M, Morilak DA, Frazer A. Serotonin clearance in vivo is altered to a greater extent by antidepressant-induced downregulation of the serotonin transporter than by acute blockade of this transporter. J Neurosci. 2002;22:6766–72.

Benmansour S, Cecchi M, Morilak DA, Gerhardt GA, Javors MA, Gould GG, et al. Effects of chronic antidepressant treatments on serotonin transporter function, density, and mRNA level. J Neurosci. 1999;19:10494–501.

Horschitz S, Hummerich R, Schloss P. Down-regulation of the rat serotonin transporter upon exposure to a selective serotonin reuptake inhibitor. Neuroreport. 2001;12:2181–4.

Young SN. Acute tryptophan depletion in humans: a review of theoretical, practical and ethical aspects. J Psychiatry Neurosci. 2013;38:294–305.

Ruhe HG, Mason NS, Schene AH. Mood is indirectly related to serotonin, norepinephrine and dopamine levels in humans: a meta-analysis of monoamine depletion studies. Mol Psychiatry. 2007;12:331–59.

Fusar-Poli P, Allen P, McGuire P, Placentino A, Cortesi M, Perez J. Neuroimaging and electrophysiological studies of the effects of acute tryptophan depletion: A systematic review of the literature. Psychopharmacology. 2006;188:131–43.

Hogenelst K, Schoevers RA, Kema IP, Sweep FCGJ, aan het Rot M. Empathic accuracy and oxytocin after tryptophan depletion in adults at risk for depression. Psychopharmacology. 2016;233:111–20.

Weinstein JJ, Rogers BP, Taylor WD, Boyd BD, Cowan RL, Shelton KM, et al. Effects of acute tryptophan depletion on raphé functional connectivity in depression. Psychiatry Res. 2015;234:164–71.

Moreno FA, Erickson RP, Garriock HA, Gelernter J, Mintz J, Oas-Terpstra J, et al. Association study of genotype by depressive response during tryptophan depletion in subjects recovered from major depression. Mol. Neuropsychiatry. 2015;1:165–74.

Munafò MR. The serotonin transporter gene and depression. Depress Anxiety. 2012;29:915–7.

Caspi A, Sugden K, Moffitt TE, Taylor A, Craig IW, Harrington H, et al. Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene. Science 2003;301:386–9.

Article   ADS   PubMed   Google Scholar  

Kiyohara C, Yoshimasu K. Association between major depressive disorder and a functional polymorphism of the 5-hydroxytryptamine (serotonin) transporter gene: A meta-analysis. Psychiatr Genet. 2010;20:49–58.

Oo KZ, Aung YK, Jenkins MA, Win AK. Associations of 5HTTLPR polymorphism with major depressive disorder and alcohol dependence: A systematic review and meta-analysis. Aust N. Z J Psychiatry. 2016;50:842–57.

Culverhouse RC, Bowes L, Breslau N, Nurnberger JI, Burmeister M, Fergusson DM, et al. Protocol for a collaborative meta-analysis of 5-HTTLPR, stress, and depression. BMC Psychiatry. 2013;13:1–12.

Karg K, Burmeister M, Shedden K, Sen S. The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited. Arch Gen Psychiatry. 2011;68:444.

Sharpley CF, Palanisamy SKA, Glyde NS, Dillingham PW, Agnew LL. An update on the interaction between the serotonin transporter promoter variant (5-HTTLPR), stress and depression, plus an exploration of non-confirming findings. Behav Brain Res. 2014;273:89–105.

Bleys D, Luyten P, Soenens B, Claes S. Gene-environment interactions between stress and 5-HTTLPR in depression: A meta-analytic update. J Affect Disord. 2018;226:339–45.

Delgado PL. Monoamine depletion studies: implications for antidepressant discontinuation syndrome. J Clin Psychiatry. 2006;67:22–26.

PubMed   Google Scholar  

Kemp JJ, Lickel JJ, Deacon BJ. Effects of a chemical imbalance causal explanation on individuals’ perceptions of their depressive symptoms. Behav Res Ther. 2014;56:47–52.

Lebowitz MS, Ahn W-K, Nolen-Hoeksema S. Fixable or fate? Perceptions of the biology of depression. J Consult Clin Psychol. 2013;81:518.

Zimmermann M, Papa A. Causal explanations of depression and treatment credibility in adults with untreated depression: Examining attribution theory. Psychol Psychother. 2020;93:537–54.

Maund E, Dewar-Haggart R, Williams S, Bowers H, Geraghty AWA, Leydon G, et al. Barriers and facilitators to discontinuing antidepressant use: A systematic review and thematic synthesis. J Affect Disord. 2019;245:38–62.

Eveleigh R, Speckens A, van Weel C, Oude Voshaar R, Lucassen P. Patients’ attitudes to discontinuing not-indicated long-term antidepressant use: barriers and facilitators. Therapeutic Advances in. Psychopharmacology. 2019;9:204512531987234.

Harrell FE Jr. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer, Cham; 2015.

Schafer JL, Kang J. Average causal effects from nonrandomized studies: a practical guide and simulated example. Psychol Methods. 2008;13:279–313.

Pech J, Forman J, Kessing LV, Knorr U. Poor evidence for putative abnormalities in cerebrospinal fluid neurotransmitters in patients with depression versus healthy non-psychiatric individuals: A systematic review and meta-analyses of 23 studies. J Affect Disord. 2018;240:6–16.

Fava GA. May antidepressant drugs worsen the conditions they are supposed to treat? The clinical foundations of the oppositional model of tolerance. Therapeutic Adv Psychopharmacol. 2020;10:2045125320970325.

Kitaichi Y, Inoue T, Nakagawa S, Boku S, Kakuta A, Izumi T, et al. Sertraline increases extracellular levels not only of serotonin, but also of dopamine in the nucleus accumbens and striatum of rats. Eur J Pharm. 2010;647:90–6.

Gartside SE, Umbers V, Hajós M, Sharp T. Interaction between a selective 5‐HT1Areceptor antagonist and an SSRI in vivo: effects on 5‐HT cell firing and extracellular 5‐HT. Br J Pharmacol. 1995;115:1064–70.

Bosker FJ, Tanke MAC, Jongsma ME, Cremers TIFH, Jagtman E, Pietersen CY, et al. Biochemical and behavioral effects of long-term citalopram administration and discontinuation in rats: role of serotonin synthesis. Neurochem Int. 2010;57:948–57.

Download references

There was no specific funding for this review. MAH is supported by a Clinical Research Fellowship from North East London NHS Foundation Trust (NELFT). This funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Author information

Authors and affiliations.

Division of Psychiatry, University College London, London, UK

Joanna Moncrieff & Mark A. Horowitz

Research and Development Department, Goodmayes Hospital, North East London NHS Foundation Trust, Essex, UK

Faculty of Education, Health and Human Sciences, University of Greenwich, London, UK

Ruth E. Cooper

Psychiatry-UK, Cornwall, UK

Tom Stockmann

Department of Dynamic and Clinical Psychology, and Health Studies, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy

Simone Amendola

Department of Applied Psychology, Zurich University of Applied Sciences, Zurich, Switzerland

Michael P. Hengartner

You can also search for this author in PubMed   Google Scholar

Contributions

JM conceived the idea for the study. JM, MAH, MPH, TS and SA designed the study. JM, MAH, MPH, TS, and SA screened articles and abstracted data. JM drafted the first version of the manuscript. JM, MAH, MPH, TS, SA, and REC contributed to the manuscript’s revision and interpretation of findings. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Corresponding author

Correspondence to Joanna Moncrieff .

Ethics declarations

Competing interests.

All authors have completed the Unified Competing Interest form at http://www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author). SA declares no conflicts of interest. MAH reports being co-founder of a company in April 2022, aiming to help people safely stop antidepressants in Canada. MPH reports royalties from Palgrave Macmillan, London, UK for his book published in December, 2021, called “Evidence-biased Antidepressant Prescription.” JM receives royalties for books about psychiatric drugs, reports grants from the National Institute of Health Research outside the submitted work, that she is co-chairperson of the Critical Psychiatry Network (an informal group of psychiatrists) and a board member of the unfunded organisation, the Council for Evidence-based Psychiatry. Both are unpaid positions. TS is co-chairperson of the Critical Psychiatry Network. RC is an unpaid board member of the International Institute for Psychiatric Drug Withdrawal.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary tables, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Moncrieff, J., Cooper, R.E., Stockmann, T. et al. The serotonin theory of depression: a systematic umbrella review of the evidence. Mol Psychiatry 28 , 3243–3256 (2023). https://doi.org/10.1038/s41380-022-01661-0

Download citation

Received : 21 June 2021

Revised : 31 May 2022

Accepted : 07 June 2022

Published : 20 July 2022

Issue Date : August 2023

DOI : https://doi.org/10.1038/s41380-022-01661-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

A constructivist grounded theory study on mental health recovery from a lived experience perspective in singapore.

  • Jonathan Han Loong Kuek
  • Toby Raeburn
  • Timothy Wand

Community Mental Health Journal (2024)

Electrochemical and biosensor techniques to monitor neurotransmitter changes with depression

  • Kelly E. Dunham
  • B. Jill Venton

Analytical and Bioanalytical Chemistry (2024)

Personal growth initiative and depression: the mediating role of need for cognition and affect

Current Psychology (2024)

Immune-related transcriptomic and epigenetic reconfiguration in BV2 cells after lipopolysaccharide exposure: an in vitro omics integrative study

  • Arturo Marroquín-Rivera
  • Chenqi Zhao
  • Benoit Labonté

Inflammation Research (2024)

Hermeneutic Suspicion in Action: Agency Beyond Causality

  • Jonas Tellefsen Hejlesen

Human Arenas (2024)

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

research paper about stress and depression

ORIGINAL RESEARCH article

Perceived academic stress and depression: the mediation role of mobile phone addiction and sleep quality.

\nXin Zhang&#x;

  • 1 Department of Social Medicine, School of Public Health, Health Management College, Harbin Medical University, Harbin, China
  • 2 Institute of Food Safety and School Health, Heilongjiang Center for Disease Control and Prevention, Harbin, China
  • 3 Department of Educational Administration, Ningbo College of Health Sciences, Ningbo, China
  • 4 Department of Elderly Healthcare and Management, School of Health Services and Management, Ningbo College of Health Sciences, Ningbo, China

Background: Although academic stress is a well-known risk factor for students' depression, little is known about the possible psychological mechanisms underlying this association. In this study, we investigated the prevalence of depression and sleep disturbance among Chinese students, examined the relationship between perceived academic stress and depression, considered if mobile phone addiction and sleep quality is a mediator of this relationship, and tested if mobile phone addiction and sleep quality together play a serial mediating role in the influence of perceived academic stress on depression.

Method: A cross-sectional survey was conducted among students from September to December 2018 in Heilongjiang Province, China. The final analysis included 5,109 students. Mobile phone addiction, sleep quality, and depressive symptoms were assessed using the Mobile Phone Addiction Index, Pittsburgh Sleep Quality Index, and Center for Epidemiologic Studies-Depression scales, respectively. The serial mediation model was used to analyse the relationship between perceived academic stress, mobile phone addiction, sleep quality, and depression.

Results: Among all participants, the prevalence of depressive symptoms and sleep disturbance was 28.69 and 27.95%, respectively. High school students showed the highest scores of perceived academic stress (2.68 ± 1.06), and the highest prevalence of depressive symptoms (33.14%) and sleep disturbance (36.47%). The serial mediation model indicated that perceived academic stress was a significant predictor of depression (B = 0.10, SE = 0.02, 95% CI = 0.06 – 0.13). Additionally, mobile phone addiction (B = 0.08, 95% boot CI = 0.06–0.11) and sleep quality (B = 0.27, 95% boot CI = 0.22–0.33) played a mediating role between perceived academic stress and depression. Mobile phone addiction and sleep quality together played a serial mediating role in the influence of perceived academic stress on depression (B = 0.11, 95% boot CI = 0.08–0.14). Furthermore, the indirect effect (i.e., the mediating effect of mobile phone addiction and sleep quality) was significant and accounted for 64.01% of the total effect.

Conclusions: Our research results underscore the need for stakeholders—including family members, educators, and policy makers—to take preventative intervention measures to address depression among Chinese students, especially high school students.

- Perceived academic stress significantly predicts depression.

- Sleep quality mediates perceived academic stress and depression.

- Mobile phone addiction mediates perceived academic stress and depression.

- Mobile phone addiction and sleep quality together play a serially mediating role in the influence of PAS on depression.

Introduction

Depression (major depressive disorder) is a widespread chronic medical illness that can influence mood, thoughts, and physical health ( 1 ), and is a severe problem faced by students worldwide. A meta-analysis that included 183 studies from 43 countries shows that the overall pooled crude prevalence of depression was 27.2% among medical students ( 2 ). Previous studies demonstrated that the prevalence of depression was 51.3, 38.3, 28.4, and 30.6% among Indian students ( 3 ), Japanese adolescents ( 4 ), Chinese university students ( 5 ), and Cameroon medical students ( 6 ), respectively. It is important to evaluate the prevalence of depressive symptoms and explore the effect mechanism of depressive symptoms to protect students from the harmful effects of depression. Studies related to students' depressive symptoms often focus on a particular group of students, such as medical ( 2 ), college ( 7 ), and university students ( 8 ), and scant research exists about depressive symptoms among students at different levels of education. Many risk factors have been associated with depression, including being female ( 9 , 10 ), life stressors ( 9 , 10 ), physical and mental factors, social media addiction ( 11 ), and parental factors, including parental psychopathology and parenting attachment ( 12 ). Stress has been shown to be one of the most important risk factors of depression, and numerous studies have demonstrated that stress plays an important role in the emergence of depression ( 13 – 15 ). For example, Torres-Berrío et al. supposed that depression is caused by a combination of genetic predisposition and life events ( 16 ). Stress often leads to adverse consequences—such as depression and anxiety ( 17 – 19 ), mobile phone addiction (MPA) ( 20 , 21 ), poor sleep quality (PSQ) ( 22 , 23 ), changes in legal drug consumption ( 24 ), cardiovascular disease ( 25 ), and worsens the outcomes of many medical illnesses ( 26 ), potentially even leading to suicide ( 27 , 28 ). Additionally, various physical and mental factors influence the prevalence of depressive symptoms, such as PSQ ( 29 ), bodily pain ( 30 ), and poor cognitive and physical functioning ( 31 ). Scholars have noted that there is a remarkable association between alterations in sleep patterns and depression ( 32 ). Furthermore, in the internet age, studies show that individuals who experience depressive symptoms often suffer from social media addictions, such as Facebook ( 33 , 34 ), mobile phone ( 35 ), and internet addictions ( 8 ). For instance, Ivanova found that MPA was positively related to both depression and loneliness in Ukrainian students ( 36 ).

In China, the school environment and parental practices contribute to the extraordinarily high expectations of students' academic performance ( 37 ). Chinese students experience high levels of academic stress throughout their academic careers, including numerous, intense examinations—such as end-of-term tests and the standardized senior high school and university entrance examinations—and a heavy homework burden ( 37 ). Scholars have demonstrated that Chinese students experience sleep deprivation owing to this culture of academic achievement. A study of 9,392 Chinese students in primary education through university levels showed that 35.6% of participants slept <7 h a day ( 38 ). In addition to the threat of academic stress and sleep deprivation, MPA is a risk factor affecting Chinese students' physical and mental health. Mobile phones have become an integral part of students' quotidian lives—Meng's survey from December 2016 to January 2017 found that 100% of the college students had mobile phones ( 39 )—and the prevalence of problematic mobile phone use has been found to be 28.2% among Chinese college students ( 40 ). Our study explored the correlations between perceived academic stress (PAS), MPA, sleep quality, and depression among Chinese students in middle school through college levels. Based on previous literature, our study proposed research hypotheses, and tested hypothesis by using survey data on Chinese students. To our knowledge, this was the first study to investigate relations between these variables among Chinese students by using the serial mediation model.

Literature Review and Research Hypotheses

Academic stress.

Academic concerns are the most important sources of chronic and sporadic stress for young people in both Western and Asian countries ( 41 ). Academic stress is defined as a student's psychological state resulting from continuous social and self-imposed pressure in a school environment that depletes the student's psychological reserves ( 42 , 43 ). Students experience academic stress throughout their secondary school ( 41 ), high school ( 44 ), and university ( 45 , 46 ), educational careers. Studies have shown that academic stress has been positively associated with depression ( 41 ), PSQ ( 24 , 47 ), and MPA ( 48 ) among students. Jayanthi observed that, compared to adolescents who do not experience academic stress, adolescents who experienced academic stress were 2.4 times more likely to have depressive symptoms ( 41 ). Other studies have found that there is a relationship between high academic stress and PSQ ( 47 , 49 ). However, scholars have not adequately addressed the adverse consequences (e.g., depression, PSQ, and MPA) of Chinese students' academic stress. Hence, we propose the following hypotheses:

H1 : PAS is positively associated with depression.

H2 : PAS is positively associated with MPA.

H3 : PAS is positively associated with PSQ.

MPA is one of the most common behavioral (i.e., non-drug) addictions ( 48 ), and is accompanied by negative effects, such as PSQ ( 50 ), depression ( 35 ), and impaired academic performance ( 51 ). The positive relationship between MPA and PSQ has been proved in previous studies, including a longitudinal study conducted among Korean adolescents ( 52 ) and a one-year prospective study among Chinese college students ( 50 ). Zhang found that among Chinese university students, there is a significant positive relationship between smartphone addiction and bedtime procrastination, which is one of the indicators of PSQ ( 53 ). Hence, we propose the following hypothesis:

H4 : MPA is positively associated with PSQ.

Similarly, the positive relationship between MPA and depression has been proved in previous studies, including a cross-sectional study conducted among Saudi university students ( 35 ), a cross-sectional study among Ukrainian college students ( 36 ), and a systematic review of relations between problematic smartphone use, anxiety and depression psychopathology ( 54 ). Furthermore, another study based on three cohorts of Korean children and adolescents confirmed the bidirectional relationship between MPA and depression ( 55 ). Hence, we propose the following hypothesis:

H5 : MPA is positively associated with depression.

Researchers have documented that stress is associated with MPA, and that MPA is associated with depression. For example, according to Wan et al., smartphone addictions are significantly positively associated with both depression and stress among Malaysian public university students ( 56 ). However, it is unclear if MPA mediates the relationship between PAS and depression. Hence, we propose the following hypothesis:

H6 : MPA mediates the relationship between PAS and depression.

Sleep Quality

Sleep disturbance has complex associations with depression (major depressive disorder) ( 31 ), and is a common physical symptom of depression. Numerous studies have confirmed the remarkable association between PSQ and depression ( 29 , 57 , 58 ). For example, Okun et al. found that PSQ is positively related to depression symptoms in postpartum women ( 29 ). Hence, we propose the following hypothesis:

H7 : PSQ is positively associated with depression.

Scholars have also demonstrated that there are relationships between stress, PSQ, and depression. A prospective birth cohort study showed that PSQ is associated with stress and depression symptoms among Chinese pregnant women ( 58 ). Zhang et al. found that perceived stress is associated with sleep quality and depressive symptoms among Chinese nursing students ( 59 ). However, it has not been documented if sleep quality mediates the relationship between PAS and depression among Chinese students. Hence, we propose the following hypothesis:

H8: Sleep quality mediates the relationship between PAS and depression.

Mobile Phone Addiction and Sleep Quality and the Relationship Between Perceived Academic Stress and Depression

Scholars have posited that there are significant associations between MPA, depression levels, and sleep quality. Demirci found that there were positive correlations between MPA, depression levels, and sleep quality ( 60 ). The results of Kaya's multivariate regression analysis showed a relationship between smartphone usage, PSQ, and depression in university students ( 57 ). A recent meta-analysis also found that there are positive correlations between MPA, depression, and sleep quality ( 61 ). Another literature review and case study found that depressive symptoms are associated with screen time-induced poor sleep, digital device night use, and mobile phone dependency ( 62 ). Although these studies explored the correlations between MPA, sleep quality, and depression among students, several scholars have added academic stress into the relationship—for example, a review found that sleep disturbance, anxiety, stress, and depression have been associated with problematic mobile phone use ( 63 ). There still exist gaps in the literature on how PAS influences depression. First, few scholars have focused on PAS, MPA, sleep quality, and depression among Chinese students. Second, the underlying mediating mechanisms that account for this association have been disregarded partly. Based on H6 and H8, it remains unclear if MPA and sleep quality serially mediate the relationship between PAS and depression. Therefore, we propose the following hypothesis:

H9: MPA and sleep quality serially mediate the relationship between PAS and depression.

Study Objectives

In this study, our primary aim was to investigate the prevalence of depression and sleep disturbance among Chinese students. Our secondary aim was to test if there were relationships between PAS, MPA, sleep quality, and depression. First, we tested if there was a relationship between PAS and depression among Chinese students (H1: PAS is positively associated with depression). Second, we tested if MPA was a mediator of the relationship between PAS and depression (H2: PAS is positively associated with MPA, H5: MPA is positively associated with depression, and H6: MPA mediates the relationship between PAS and depression). Third, we tested if sleep quality was a mediator of the relationship between PAS and MPA (H3: PAS is positively associated with PSQ, H7: PSQ is positively associated with depression, and H8: Sleep quality mediates the relationship between PAS and depression). Finally, we also tested if MPA and sleep quality together played a serial mediating role in the influence of PAS on depression (H4: MPA is positively associated with PSQ and H9: MPA and sleep quality serially mediate the relationship between PAS and depression).

Data were collected from a cross-sectional questionnaire survey that was conducted from September to December 2018 in Heilongjiang Province, China, by the Heilongjiang Center for Disease Control and Prevention. A multistage cluster sampling method was used. In the first stage, three cities of Heilongjiang province were randomly selected by economic characteristics. In the second stage, one urban district and one rural township were chosen at random. In the third stage, two middle schools were randomly selected in each urban district and rural township; Since nine-year compulsory education was implemented in China, high school education is not included in the nine-year compulsory education, high schools are more in urban districts than in rural townships, two high schools and one high school were randomly selected in urban district and rural township, respectively; Since vocational high schools and universities are scarce in rural townships, one vocational high school and one college were randomly selected from the urban district. In the fourth stage, two classes were randomly selected from each grade of middle school, high school, vocational high school, and from grades 1, 2, and 3 in college. Since senior students may have been looking for a job or working as an intern, some of them were not on campus, they were not been investigated. Finally, four middle schools, three high schools, one vocational high school, and one college were randomly selected within each city (Harbin, Jiamusi, and Jixi) of Heilongjiang Province. Data were collected through a self-administered questionnaire distributed in class. Students completed the survey within 1 h, while a well-trained member of the research group supervised. All the students were informed of the purpose of the study and assured that their identities would remain confidential. Students and their parents provided written informed assent to participate in the study.

Participants

Finally, we recruited 6,480 students in our investigation; 6,430 (99.23%) valid questionnaires were analyzed after excluding those with incomplete information. Participants were included in the sample if they had one constant internet-accessible mobile phone, which is similar with previous studies ( 64 – 68 ). A total of 5,109 (79.46%) participants reported having one constant internet-accessible mobile phone at the time of the survey. The final sample comprised 1,904 middle school students from grades 1, 2, and 3, respectively; 1,859 high school students from grades 1, 2, and 3, respectively; 660 vocational high school students from grades 1, 2, and 3, respectively; and 686 college students from grades 1, 2 and 3, respectively. Of these participants, there were 2,422 (47.41%) boys and 2,687 (52.59%) girls; on average, the mean age of participants was 15.53 years, with a standard deviation of 2.22, ranging from 11 to 25 years. Approval was obtained from the Medical Research Ethics Committee of Harbin Medical University and the principals of the participating schools.

Perceived Academic Stress

Consistent with previous studies ( 69 – 71 ), PAS was measured using one self-report item “How much academic stress did you feel in the study during the past month?” using a 5-point Likert scale where 1 = “No,” 2 = “relatively low,” 3 = “average/general,” 4 =“relatively high” and 5 = “extremely heavy,” with a higher score indicating more PAS.

Center for Epidemiologic Studies-Depression Scale. The 20-item CES-D developed by Radloff ( 72 ) is a self-report measure that has been widely used to assess depressive symptoms in different populations ( 73 ). The reliability and validity of the CES-D have been tested among Chinese populations ( 74 ). The CES-D, when used in Chinese adolescents and university students, has shown good reliability ( 75 – 78 ), as well as good validity ( 77 , 78 ). There are four components of CES-D, namely somatic and retarded activity, depressed affect, positive affect, and interpersonal relationships. Among the 20 items, four (items 4, 8, 12, and 16) are reversed scores. All items are evaluated on a 4-point Likert scale in relation to their incidence during the previous week, and are scored from 0 to 3 (0 = not at all, 1 = a little, 2 = some, 3 = a lot); total possible scores thus range from 0 to 60, with higher scores indicating greater number of symptoms ( 79 ).

For the original CES-D scale, a total score of 16 or greater is considered as indicative of subthreshold depression ( 72 ). Many studies have evaluated the diagnostic accuracy of the CES-D to detect depression among the general population and proposed a variety of cut-off scores, such as a cut-off score of 21 for Chinese patients with type 2 diabetes ( 80 ), and a cut-off score of 22 for the older Chinese population ( 81 ). However, the cut-off score of 16 has been widely used for Chinese adolescents and university students ( 7 , 76 , 82 – 84 ). Therefore, the same cut-off score has been used in our study too. Students with CES-D scores between 16 and 21 were defined as “mildly depressed,” between 21 and 24 as “moderately depressed,” and ≥ 25 as “severely depressed” ( 83 ). The CFA on the four-factor model showed a good model fit, with χ 2 = 16.54, df = 1, P < 0.000, RMSEA = 0.06, SRMR = 0.01, CFI = 0.99, TLI = 0.98. Additionally, the Cronbach's alpha coefficient was 0.84 for the total scale, all four dimensions had acceptable reliability with Cronbach's alpha coefficient of 0.70, 0.83, 0.78, and 0.62.

Mobile Phone Use Situation and Mobile Phone Addiction

Mobile phone use situation was assessed by three items. First, “How many hours do you use your mobile phone every day?” to which participants answered with one of four options: “less than a half hour,” “a half hour to one hour,” “one to two hours,” or “more than two hours.” Second, “How long have you had a mobile phone?” to which participants answered “ <1 year,” “1–2 years,” “2–3 years,” or “more than 3 years.” Third, “How much do you spend on mobile phone charges every month?” to which participants answered “less 30 yuan,” “30–50 yuan,” “50–100 yuan,” or “more than 100 yuan.”

The Mobile Phone Addiction Index (MPAI) was used in our study ( 85 ). Participants rated the 17 items on a 5-point Likert scale ranging from 1 (not at all) to 5 (always). Higher scores indicated greater addiction to mobile phones ( 86 ). There are four components of MPAI, namely inability to control craving, feeling of anxiety and being lost, withdrawal or escape, and productivity loss. The Confirmatory Factor Analysis (CFA) on the four-factor model showed a good model fit, with χ 2 = 6.44, df = 1, p < 0.05, RMSEA = 0.03, SRMR = 0.004, CFI = 0.99, TLI = 0.99. Additionally, the Cronbach's alpha coefficient was 0.90 for the total scale. All four dimensions had satisfactory reliability with Cronbach's alpha coefficient of 0.76, 0.81, 0.85, and 0.75.

The Pittsburgh Sleep Quality Index (PSQI) was used in our study ( 87 ). PSQI scale contains 19 items covering seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each component was scored from 0 (no difficulty) to 3 (severe difficulty). The total score was calculated from the seven component scores, ranging from 0 to 21. A score of more than 5 implied poor sleep ( 87 ). The CFA on the seven-factor model showed a good model fit, with χ 2 = 79.49, df = 11, P < 0.000, RMSEA = 0.04, SRMR = 0.02, CFI = 0.99, TLI = 0.98. Additionally, Cronbach's alpha coefficient was 0.624 for the PSQI scale in our study.

Data Analyses

SPSS version 19.0. and Mplus 7.0 were used to analyse data in our study. Descriptive analyses were first conducted of participants' characteristics, participants' mobile phone use and the prevalence of sleep disturbance, MPA, and depression. We tested the reliability and validity of the MPAI scale, PSQI scale and CES-D scale by examining their Cronbach's alpha coefficient and performing a CFA. Spearman's correlation analysis was performed to examine the general relationships among the four variables—PAS, MPA, sleep quality, and depression. A structural equation model (SEM) was built to examine hypotheses. We tested the mediating role of MPA and sleep quality; the constructed serial mediation model included three latent variables (MPA, sleep quality and depression) and one manifest variable (PAS), PAS was the independent variable, depression was the dependent variable, and MPA and sleep quality were the mediating variables ( 88 ). The bootstrapping analyses used 5,000 samples at the 95% confidence interval (CI) to indicate significance.

To determine whether the model fits the data well, multiple indices were tested, including (1) the model χ 2 and its p value, in which non-significance is desirable for good fit. With increasing sample size and a fixed degree of freedom, the χ 2 value increases. It is difficult to get a nonsignificant chi-square (indicative of good fit) when sample sizes are over 200 ( 89 ). This can lead to a problem where plausible models might be rejected. Because this statistic is sensitive to the sample size, inspection of the other fit indices is recommended ( 90 ). (2) The root mean square error of approximation (RMSEA) in which values ranging from 0.05 to 0.08 represent adequate fit, and values <0.05 indicate good fit. (3) The standardized root mean square residual (SRMR) in which values are ≤0.08 indicate good fit. (4) The comparative fit index (CFI), in which values range from 0.90 to 0.95 indicate an adequate fit and values ≥0.95 indicate a good fit, and (5) the Tacker-Lewis index (TLI) in which values >0.90 indicate a good fit.

Descriptive Statistics

The mean scores of PAS were 2.61 ± 1.03, 2.68 ± 1.06, 2.13 ± 0.98, and 2.29 ±0.96 for middle school students, high school students, vocational high school students, and college school students, respectively.

Among the participants, 45.55% used their mobile phone more than 2 h daily; 39.5% of the participants had a mobile phone for more than 3 years; 53.24% of the participants spent more than or equal to 30 yuan on mobile phone charges every month ( Table 1 ). The mean MPAI score of all the participants was 30.62 ± 11.92.

www.frontiersin.org

Table 1 . Participants' mobile phone use.

The prevalence of depressive symptoms was 28.69% ( n = 1,466) with a mean CES-D score of 12.52 ± 8.86. Prevalence of depression at a mild level (CES-D ≥ 16 and CES-D < 21), moderate level (CES-D ≥ 21 and CES-D < 25), and severe level (CES-D ≥ 25) was 12.62, 6.95, and 9.12%, respectively. The prevalence of depressive symptoms among high school students (33.14%) was the highest, while the prevalence of depressive symptoms among college students (21.43%) was the lowest ( Table 2 ).

www.frontiersin.org

Table 2 . Depression classifications of participants.

The prevalence of sleep disturbance was 27.95% ( n = 1,428) with a mean global PSQI score of 4.29 ± 2.59. The prevalence of sleep disturbance among high school students (36.47%) was the highest, while the prevalence of sleep disturbance among middle school students (20.75%) was the lowest. The average sleep time and sleep latency were 7.40 ± 1.28 h and 15.81 ± 12.48 min, respectively. Among the participants, 14.50% reported that they had bad or very bad sleep quality; 36.29% reported that their sleep latency was more than 15 min; 50.89% reported that they slept ≤7 h a day; 12.62% reported that their sleep efficiency was ≤85%; 67.59% reported that they experienced sleep disturbances; 2.90% of them reported that they used sleep medication; and 78.80% reported that they had daytime dysfunction ( Table 3 ).

www.frontiersin.org

Table 3 . Prevalence of sleep disturbance for participants at different education levels.

Means, Standard Deviation (SD) and correlations of the main variables in the mediation model are shown in Table 4 . The results, indicating that the variables were significantly and positively correlated, provide initial support for the hypotheses of this study, and act as a foundation of the serial mediation model.

www.frontiersin.org

Table 4 . Means, SD, Pearson's correlation coefficient of variables.

Test for Serial Mediation Model

SEM was used to provide the fit indexes of the serial mediation model. A model was constructed with MPA (M1) as a mediator and sleep quality (M2) as another mediator. In this model, PAS was set as the predictor (X) and depression as the outcome (Y). Results of the serial mediation model indicated that the constructed model exhibited a satisfactory fit with the data: χ 2 = 1,196.50, df = 95, P < 0.000, SRMR = 0.04, RMSEA = 0.05, CFI = 0.95, and TLI = 0.94.

First, PAS was positively associated with depression (B = 0.10, SE = 0.02, 95% CI = 0.06–0.13). Higher levels of PAS were related to higher levels of depression, and thus H1 was supported. Second, PAS positively predicted MPA (B = 0.18, SE = 0.02, 95% CI = 0.15–0.21). Higher levels of PAS were related to higher levels of MPA, and thus H2 was supported. Third, PAS was positively associated with PSQ (B = 0.23, SE = 0.02, 95% CI = 0.19–0.26). Higher levels of PAS were related to poorer sleep quality, and thus H3 was supported. Fourth, MPA was positively associated with PSQ (B = 0.51, SE = 0.02, 95% CI = 0.47–0.54). Higher levels of MPA were related to poorer sleep quality, and thus H4 was supported. Fifth, MPA was positively associated with depression (B = 0.17, SE = 0.02, 95% CI = 0.13–0.22). Higher levels of MPA were related to higher levels of depression, and thus H5 was supported. Last, PSQ was positively associated with depression (B = 0.44, SE = 0.03, 95% CI = 0.39–0.49). PSQ was related to higher levels of depression, and thus H7 was supported.

Total, Direct, and Indirect Effects

Table 5 shows all possible indirect effects of the mediation model. First, the indirect effect of PAS on depression through MPA was significant (B = 0.08, 95% boot CI = 0.06–0.11), and thus H6 was supported. Second, the indirect effect of PAS on depression through sleep quality was significant (B = 0.27, 95% boot CI = 0.22–0.33), and thus H8 was supported. Third, the indirect effect of PAS on depression through MPA and sleep quality was also significant (B = 0.11, 95% boot CI = 0.08–0.14), and thus H9 was also supported. The total indirect effect was B = 0.46, 95% boot CI = 0.40–0.53, and the mediating effect of MPA and sleep quality were significant ( P < 0.001), accounting for 64.01% (total indirect effect/total effect) of the total effect. The indirect effect related to sleep quality accounted for 82.61% of the total indirect effect, that is, (indirect effect 2 + indirect effect 3)/total indirect effect ( Table 5 ).

www.frontiersin.org

Table 5 . Total, direct, and indirect effects.

Although academic stress is a well-known risk factor for depression in students, little is known about the possible psychological mechanisms underlying this association, or how MPA and PSQ—which also are risk factors of depression—operate to have an impact on it. The main aim of our study was to test if there is a relationship between PAS and depression and if MPA and sleep quality together play a serial mediating role in the influence of PAS on depression among Chinese students. To the best of our knowledge, this was the first study to investigate the relationship between the variables using SEM. As expected, the serial mediation model showed that PAS was a significant predictor of depression. MPA and sleep quality played a mediating role between PAS and depression. Furthermore, MPA and sleep quality together played a serial mediating role in the influence of PAS on depression. In our study, the indirect effect (i.e., the mediating effect of MPA and sleep quality) was significant and accounted for 64.01% of the total effect. Thus, apart from the direct effect of PAS on depression, the indirect effect of PAS on depression should be emphasized. Our findings provide significant insights into the risk factors for depressive symptoms in students.

Depression Among Students

According to studies that have focused on depression among Chinese students, the prevalence of depression varies from 22.0 to 68.5% ( 5 , 91 – 95 ). In our study, the prevalence of depressive symptoms was 28.69%. The differences across these studies may have resulted from temporal or regional disparities or variations in depression definitions and assessment methods. Depressive symptoms are related to many negative consequences, such as increased suicide risk among students ( 96 ) and increased college withdrawal rates ( 97 ). Controlling depressive symptoms among students can both protect human capital value from the societal perspective and maintain students' physical and mental health from the individual perspective. In our study, the most stressed, depressed, and sleep-deprived students were high school students. Thus, Chinese high school students' physical and mental health requires attention. In China, high school students are admitted to colleges and universities based on gaokao , the standardized National College Entrance Examination ( 98 ). These admission decisions are extremely important, as they impact high school students' future educational opportunities, career paths, and life experiences. Our research results prove that Chinese students experience the most stressful and competitive academic environment of their academic careers when they are in high school.

Mediating Role of Mobile Phone Addiction

Chinese students spend considerable time on mobile phones−45.55% of the participants spent more than 2 h daily on their mobile devices. 39.5% of participants had had a mobile phone for more than 3 years, while the mean age of participants was 15.53 years. Using the mediation model, we illustrated the mediating role of MPA in line with our hypothesis. As H6 predicted, MPA played a role in the path from PAS to depression. MPA could partially explain the association between PAS and depression among Chinese students—hence, MPA was not only an outcome of PAS, but also a catalyst of depression. First, we found that high levels of PAS were associated with high levels of MPA. This finding is consistent with previous research results ( 48 ) and suggests that PAS may be a significant trigger for students' negative behaviors—such as MPA. Scholars have posited that young people's digital distraction activities—including playing computer games and online surfing—may be interpreted as a way to avoid problems, reality, and stress ( 99 , 100 ). High levels of PAS were associated with high levels of MPA, which may be due to students' use of mobile phones to escape from academic stress. Second, we found that high levels of MPA were associated with high levels of depression, which is in line with existing research results ( 35 , 36 , 101 ). Students who experience MPA may neglect real-world social engagement ( 102 ) resulting in academic underperformance ( 103 ), clinical health symptoms ( 68 ), which are related to negative emotions—such as depression. Our findings add to the existing research that suggests that when students are facing academic stress, they may be addicted to their mobile phones to escape from academic stress, and thus the negative consequences of MPA may lead to depression in students.

Mediating Role of Sleep Quality

As H8 predicted, sleep quality is not only an outcome of academic pressure—it is also a catalyst of depression. Moreover, the indirect effect related to sleep quality accounted for 82.61% of the total indirect effect. Thus, compared to MPA, sleep quality played a more important role in the path from PAS to depression. We found that higher levels of PAS were associated with poorer sleep quality. This finding is consistent with previous research findings ( 24 , 47 ). For example, Waqas et al. demonstrated that perceived stress is a significant predictor of PSQ ( 47 ). In China, students exist in a prolonged competitive learning environment and experience unrelenting academic stress. To achieve better academic performance and meet the extraordinarily high expectations of parents and educators, Chinese students have heavy homework burdens and learning burdens, resulting in sleep deprivation. Furthermore, academic stress decreases sleep quality. According to Almojali et al., students who are not suffering from academic stress are less likely to experience PSQ ( 104 ). Previous studies have proved that sleep deficiency and sleep health problems are common among Chinese students ( 105 ). Our research results may explain why higher levels of PAS were related to poorer sleep quality.

We also found that high levels of PSQ were associated with high levels of depression, which is consistent with prior research findings ( 31 , 50 , 57 ). Scholars have proved that PSQ is related to multiple negative consequences that may lead to depression—including daytime dysfunction, poor academic performance, and fatigue ( 106 , 107 ). Our findings add to the existing research that suggests that sleep quality is a mediator between PAS and depression among students, which means that higher levels of PAS were related to poorer sleep quality—such as sleep deficiency and daytime dysfunction—which was related to higher levels of depression.

Serial Mediating Effect of Mobile Phone Addiction and Sleep Quality

As per H9, MPA and sleep quality together play a serial mediating role in the influence of PAS on depression. The results of our study showed that higher levels of PAS were related to higher levels of MPA, which was associated with poorer sleep quality, which was associated with higher levels of depression. Numerous studies have documented that there is a positive relationship between MPA and PSQ ( 50 , 52 ). For example, Kang et al. found that there were bidirectional longitudinal relationships between MPA and PSQ ( 50 ). Scholars have posited that the more screen time young people use, the less sleep time they have ( 108 ). Moreover, young people often use their mobile phone in the bedroom—bedtime mobile phone use is related to higher insomnia scores and increased fatigue ( 109 ), and both insomnia and fatigue are related to depression ( 110 , 111 ). This may explain why MPA and PSQ together play a serial mediating role in the influence of PAS on depression. Our findings suggests that Chinese students are likely to distract themselves from PAS by using their mobile phones, and thus shortening their sleep duration, decreasing their sleep quality, leading to PSQ, and resulting in depressive symptoms.

Measures to Reduce Depressive Symptoms Among Chinese Students

To reduce depressive symptoms among students, their PAS should be managed. Given the multiple, negative consequences (MPA, PSQ, and depression) of PAS, stakeholders—family members, educators (including teachers, school administrators, and school health professionals), and policy makers—should take preventative measures to help students manage and relieve academic stress, such as provide counseling services ( 112 ), foster their psychological resilience ( 113 ), and increase social support ( 19 ) to improve their overall well-being. Second, students' sleep quality should be ensured to reduce depressive symptoms. Stakeholders should actively promote counseling and intervention for students experiencing sleep disturbances. Third, given that higher levels of MPA are associated with poorer sleep quality and higher levels of depression, stakeholders should develop mitigating strategies to manage mobile phone use to ensure students' sleep quality and to relieve their depressive symptoms. Rational and normative mobile phone use should be advocated and classroom management strategies enforced to ensure that students use their mobile phones at restricted times and places for positive purposes, such as online learning. Fourth, regular psychological assessment of depression, MPA, and PSQ will help stakeholders detect and manage students' health problems. Last, parents and family members, educators, and policy makers should encourage students to exercise more to alleviate MPA ( 114 ), improve sleep quality ( 115 ), and reduce depression ( 116 ).

Limitations

This study has several limitations. First, although we conducted our research based on previous studies, due to the cross-sectional design of our study, we could not confirm causal relationships among the study variables. Second, the study period was September to December 2018, which was more than 2 years ago. However, we believe that the results of our study are valuable for understanding the mechanisms of how PAS influences students' depression through MPA and sleep quality, and our study can provide a basis for future research. Third, the study measured the participants' perceived academic stress using a single item, which may not have captured various other relevant stressors, such as parental learning expectations. Future studies should use a multiple-item scale to assess the participants' perceived academic stress. Forth, this study was limited to middle school, high school, vocational high school, and college students. Future research on Chinese students at all education levels from primary school to postgraduate levels is necessary. Fifth, perceived academic stress can increase during stressful conditions ( 117 ), such as during exams or major change in life (e.g., from high-school student to freshman). While our study was conducted in 27 schools in three cities, it is impossible that we conducted the survey when the participants had no examinations or changes. Future studies can control for stressful academic conditions in the analyses to enhance their accuracy. Last, gender, age, and other factors are important influencing factors of PAS, MPA, sleep quality, and depression. Since the main aim of this study was to test if there was a relationship between PAS and depression and if MPA and sleep quality together play a serial mediating role in the influence of PAS on depression, the aforementioned factors were not considered in this study. Future studies should consider these factors and test the relationships between PAS, MPA, sleep quality, depression, and other health indicators.

Conclusions

Our study's results showed that Chinese students face the risk of depression and sleep disturbance, and the most stressed, depressed, and sleep-disturbed students are those in high school. Second, the results of the serial mediation model indicated that PAS predicted depression, and MPA and sleep quality played a mediating role between PAS and depression. Furthermore, MPA and sleep quality together play a serial mediating role in the influence of PAS on depression. Our study extends the understanding of how PAS is associated with depression among Chinese students. Considering the harmful effects of depression, stakeholders—including parents and family members, educators, and policy makers—should take preventative measures to alleviate Chinese students' depression and depressive symptoms.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Requests to access these datasets should be directed to wuqunhong@163.com .

Author Contributions

XZ, FG, ZK, and QW: conceptualization. XZ, HZ, JW, and HL: formal analysis. FG, JZ, JL, JY, HZ, and BL: investigation. XZ, FG, ZK, and BL: data curation. XZ, FG, and ZK: writing—original draft preparation. QW and BL: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

This research was funded by QW of The National Key Social Science Fund of China (Grant No.19AZD013).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors would like to express our appreciation to all of the individuals for their involvement in the study, including each of students and teachers for their support during the data collection.

Abbreviations

PAS, Perceived Academic Stress; MPA, Mobile Phone Addiction; MPAI, Mobile Phone Addiction Index; CES-D, Center for Epidemiologic Studies-Depression; PSQ, Poor Sleep Quality; PSQI, Pittsburgh Sleep Quality Index; SEM, Structural Equation Model.

1. Cui R. Editorial: a systematic review of depression. Curr Neuropharmacol. (2015) 13:480. doi: 10.2174/1570159X1304150831123535

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Rotenstein LS, Ramos MA, Torre M, Segal JB, Peluso MJ, Guille C, et al. Prevalence of depression, depressive symptoms, and suicidal ideation among medical students: a systematic review and meta-analysis. JAMA. (2016) 316:2214–36. doi: 10.1001/jama.2016.17324

3. Iqbal S, Gupta S, Venkatarao E. Stress, anxiety and depression among medical undergraduate students and their socio-demographic correlates. Indian J Med Res. (2015) 141:354–7. doi: 10.4103/0971-5916.156571

4. Nishida T, Tamura H, Sakakibara H. The association of smartphone use and depression in Japanese adolescents. Psychiatry Res. (2019) 273:523–7. doi: 10.1016/j.psychres.2019.01.074

5. Gao L, Xie Y, Jia C, Wang W. Prevalence of depression among Chinese university students: a systematic review and meta-analysis. Sci Rep. (2020) 10:15897. doi: 10.1038/s41598-020-72998-1

6. Ngasa SN, Sama CB, Dzekem BS, Nforchu KN, Tindong M, Aroke D, et al. Prevalence and factors associated with depression among medical students in Cameroon: a cross-sectional study. BMC Psychiatry. (2017) 17:216. doi: 10.1186/s12888-017-1382-3

7. Xu Y, Chi X, Chen S, Qi J, Zhang P, Yang Y. Prevalence and correlates of depression among college nursing students in China. Nurse Educ Today. (2014) 34:e7–12. doi: 10.1016/j.nedt.2013.10.017

8. Seki T, Hamazaki K, Natori T, Inadera H. Relationship between internet addiction and depression among Japanese university students. J Affect Disord. (2019) 256:668–72. doi: 10.1016/j.jad.2019.06.055

9. Sajjadi H, Mohaqeqi Kamal SH, Rafiey H, Vameghi M, Forouzan AS, Rezaei M, et al. systematic review of the prevalence and risk factors of depression among iranian adolescents. Glob J Health Sci. (2013) 5:16–27. doi: 10.5539/gjhs.v5n3p16

10. Hammen C. Risk factors for depression: an autobiographical review. Annu Rev Clin Psychol. (2018) 14:1–28. doi: 10.1146/annurev-clinpsy-050817-084811

11. Shensa A, Escobar-Viera CG, Sidani JE, Bowman ND, Marshal MP. Primack BA. Problematic social media use and depressive symptoms among US young adults: a nationally-representative study. Soc Sci Med. (2017) 182:150–7. doi: 10.1016/j.socscimed.2017.03.061

12. Essau CA, Sasagawa S. Parental psychopathology and parenting style/attachment as risk factors of depression. In Dobson KS, Dozois DJA, editors. Risk Factors in Depression. San Diego, CA: Elsevier Ltd (2008). p. 343–61.

PubMed Abstract | Google Scholar

13. Bianchi R, Schonfeld IS, Laurent E. Is burnout a depressive disorder? A reexamination with special focus on atypical depression. Int J Stress Manag. (2014) 21:307–24. doi: 10.1037/a0037906

CrossRef Full Text | Google Scholar

14. Hammen. Constance. Stress and depression. Ann Rev Clin Psychol. (2005) 1:293–319. doi: 10.1146/annurev.clinpsy.1.102803.143938

15. Pr Aa GH. Can stress cause depression? Prog Neuropsychopharmacol Biol Psychiatry. (2004) 6:5–22. doi: 10.1080/15622970510030018

16. Torres-Berrío A, Issler O, Parise EM, Nestler EJ. Unraveling the epigenetic landscape of depression: focus on early life stress. Dialogues Clin Neurosci. (2019) 21:341–57. doi: 10.31887/DCNS.2019.21.4/enestler

17. Fawzy M, Hamed SA. Prevalence of psychological stress, depression and anxiety among medical students in Egypt. Psychiatry Res. (2017) 255:186–94. doi: 10.1016/j.psychres.2017.05.027

18. Regehr C, Glancy D, Pitts A. Interventions to reduce stress in university students: a review and meta-analysis. J Affect Disord. (2013) 148:1–11. doi: 10.1016/j.jad.2012.11.026

19. Friedlander LJ, Reid GJ, Shupak N, Cribbie R. Social support, self-esteem, and stress as predictors of adjustment to university among first-year undergraduates. J Coll Stud Dev. (2007) 48:259–74. doi: 10.1353/csd.2007.0024

20. Gao T, Li J, Zhang H, Gao J, Kong Y, Hu Y, et al. The influence of alexithymia on mobile phone addiction: the role of depression, anxiety and stress. J Affect Disord. (2018) 225:761–6. doi: 10.1016/j.jad.2017.08.020

21. Cho HY, Kim DJ, Park JW. Stress and adult smartphone addiction: Mediation by self-control, neuroticism, and extraversion. Stress Health. (2017) 33:624–30. doi: 10.1002/smi.2749

22. Alsaggaf MA, Wali SO, Merdad RA, Merdad LA. Sleep quantity, quality, and insomnia symptoms of medical students during clinical years. Relationship with stress and academic performance. Saudi Med J. (2016) 37:173–82. doi: 10.15537/smj.2016.2.14288

23. Hu Y, Visser M, Kaiser S. Perceived stress and sleep quality in midlife and later: controlling for genetic and environmental influences. Behav Sleep Med. (2020) 18:537–49. doi: 10.1080/15402002.2019.1629443

24. Zunhammer M, Eichhammer P, Busch V. Sleep quality during exam stress: the role of alcohol, caffeine and nicotine. PLoS ONE. (2014) 9:e109490. doi: 10.1371/journal.pone.0109490

25. Zhang Y, Chen Y, Ma L. Depression and cardiovascular disease in elderly: Current understanding. J Clin Neurosci. (2018) 47:1–5. doi: 10.1016/j.jocn.2017.09.022

26. Alexopoulos GS. Depression in the elderly. Lancet. (2005) 365:1961–70. doi: 10.1016/S0140-6736(05)66665-2

27. Gill SK, Muñoz RF, Leykin Y. The Influence of perceived stress and depression on suicide-related beliefs in caucasian and indian adults. Crisis. (2018) 39:127–36. doi: 10.1027/0227-5910/a000492

28. Hawton K, Casañas I. Comabella C, Haw C, Saunders K. Risk factors for suicide in individuals with depression: a systematic review. J Affect Disord. (2013) 147:17–28. doi: 10.1016/j.jad.2013.01.004

29. Okun ML, Mancuso RA, Hobel CJ, Schetter CD, Coussons-Read M. Poor sleep quality increases symptoms of depression and anxiety in postpartum women. J Behav Med. (2018) 41:703–10. doi: 10.1007/s10865-018-9950-7

30. Conway A, Sheridan J, Maddicks-Law J, Fulbrook P, Ski CF, Thompson DR, et al. Depression and pain in heart transplant recipients: an observational study. Biol Res Nurs. (2017) 19:71–6. doi: 10.1177/1099800416666717

31. Nyer M, Farabaugh A, Fehling K, Soskin D, Holt D, Papakostas GI, et al. Relationship between sleep disturbance and depression, anxiety, and functioning in college students. Depress Anxiety. (2013) 30:873–80. doi: 10.1002/da.22064

32. Tsuno N, Besset A, Ritchie K. Sleep and depression. J Clin Psychiatry. (2005) 66:1254–69. doi: 10.4088/JCP.v66n1008

33. Yoon S, Kleinman M, Mertz J, Brannick M. Is social network site usage related to depression? A meta-analysis of Facebook-depression relations. J Affect Disord. (2019) 248:65–72. doi: 10.1016/j.jad.2019.01.026

34. Mamun MAA, Griffiths MD. The association between Facebook addiction and depression: a pilot survey study among bangladeshi students. Psychiatry Res. (2019) 271:628–33. doi: 10.1016/j.psychres.2018.12.039

35. El-Sayed Desouky D, Abu-Zaid H. Mobile phone use pattern and addiction in relation to depression and anxiety. East Mediterr Health J. (2020) 26:692–9. doi: 10.26719/emhj.20.043

36. Ivanova A, Gorbaniuk O, Błachnio A, Przepiórka A, Mraka N, Polishchuk V, et al. Mobile phone addiction, phubbing, and depression among men and women: a moderated mediation analysis. Psychiatr Q. (2020) 91:655–68. doi: 10.1007/s11126-020-09723-8

37. Qi S, Qin Z, Wang N, Tse LA, Qiao H, Xu F. Association of academic performance, general health with health-related quality of life in primary and high school students in China. Health Qual Life Outcomes. (2020) 18:339. doi: 10.1186/s12955-020-01590-y

38. Liu B, Gao F, Zhang J, Zhou H, Sun N, Li L, et al. Sleep quality of students from elementary school to university: a cross-sectional study. Nat Sci Sleep. (2020) 12:855–64. doi: 10.2147/NSS.S266493

39. Meng J, Wang F, Chen R, Hua H, Yang Q, Yang D, et al. Association between the pattern of mobile phone use and sleep quality in Northeast China college students. Sleep Breath. (2021) 25:2259–67. doi: 10.1007/s11325-021-02295-2

40. Tao S, Wu X, Zhang Y, Zhang S, Tong S, Tao F. Effects of sleep quality on the association between problematic mobile phone use and mental health symptoms in chinese college students. Int J Environ Res Public Health. (2017) 14:185. doi: 10.3390/ijerph14020185

41. Jayanthi P, Thirunavukarasu M, Rajkumar R. Academic stress and depression among adolescents: a cross-sectional study. Indian Pediatr. (2015) 52:217–9. doi: 10.1007/s13312-015-0609-y

42. Misra R, Mckean M, West S, Russo T. Academic stress of college students: comparison of student and faculty perceptions. Coll Stud J. (2000) 34:236–45. doi: 10.1037/1072-5245.11.2.132

43. Neseliler S, Tannenbaum B, Zacchia M, Larcher K, Coulter K, Lamarche M, et al. Academic stress and personality interact to increase the neural response to high-calorie food cues. Appetite. (2017) 116:306–14. doi: 10.1016/j.appet.2017.05.016

44. Gurung M, Chansatitporn N, Chamroonsawasdi K, Lapvongwatana P. Academic stress among high school students in a rural area of nepal: a descriptive cross-sectional study. JNMA J Nepal Med Assoc. (2020) 58:306–9. doi: 10.31729/jnma.4978

45. Trigueros R, Padilla A, Aguilar-Parra JM, Lirola MJ, García-Luengo AV, Rocamora-Pérez P, et al. The influence of teachers on motivation and academic stress and their effect on the learning strategies of university students. Int J Environ Res Public Health. (2020) 17:9089. doi: 10.3390/ijerph17239089

46. Trigueros R, Padilla AM, Aguilar-Parra JM, Rocamora P, Morales-Gázquez MJ, López-Liria R. The influence of emotional intelligence on resilience, test anxiety, academic stress and the mediterranean diet. A study with university students. Int J Environ Res Public Health. (2020) 17:2071. doi: 10.3390/ijerph17062071

47. Waqas A, Khan S, Sharif W, Khalid U, Ali A. Association of academic stress with sleeping difficulties in medical students of a Pakistani medical school: a cross sectional survey. PeerJ. (2015) 3:e840. doi: 10.7717/peerj.840

48. Gligor ?, Mozo? I. Indicators of smartphone addiction and stress score in university students. Wien Klin Wochenschr. (2019) 131:120–5. doi: 10.1007/s00508-018-1373-5

49. Chen TY, Chou YC, Tzeng NS, Chang HA, Kuo SC, Pan PY, et al. Effects of a selective educational system on fatigue, sleep problems, daytime sleepiness, and depression among senior high school adolescents in Taiwan. Neuropsychiatr Dis Treat. (2015) 11:741–50. doi: 10.2147/NDT.S77179

50. Kang Y, Liu S, Yang L, Xu B, Lin L, Xie L, et al. Testing the bidirectional associations of mobile phone addiction behaviors with mental distress, sleep disturbances, and sleep patterns: a one-year prospective study among Chinese College Students. Front Psychiatry. (2020) 11:634. doi: 10.3389/fpsyt.2020.00634

51. Liu X, Luo Y, Liu ZZ, Yang Y, Liu J, Jia CX. Prolonged mobile phone use is associated with poor academic performance in adolescents. Cyberpsychol Behav Soc Netw. (2020) 23:303–11. doi: 10.1089/cyber.2019.0591

52. Lee JE, Jang SI, Ju YJ, Kim W, Lee HJ, Park EC. Relationship between mobile phone addiction and the incidence of poor and short sleep among Korean Adolescents: a Longitudinal Study of the Korean Children & Youth Panel Survey. J Korean Med Sci. (2017) 32:1166–72. doi: 10.3346/jkms.2017.32.7.1166

53. Zhang MX, Wu AMS. Effects of smartphone addiction on sleep quality among Chinese university students: the mediating role of self-regulation and bedtime procrastination. Addict Behav. (2020) 111:106552. doi: 10.1016/j.addbeh.2020.106552

54. Elhai JD, Dvorak RD, Levine JC, Hall BJ. Problematic smartphone use: a conceptual overview and systematic review of relations with anxiety and depression psychopathology. J Affect Disord. (2017) 207:251–9. doi: 10.1016/j.jad.2016.08.030

55. Park SY, Yang S, Shin CS, Jang H, Park SY. Long-term symptoms of mobile phone use on mobile phone addiction and depression among Korean Adolescents. Int J Environ Res Public Health. (2019) 16:3584. doi: 10.3390/ijerph16193584

56. Wan Ismail WS, Sim ST, Tan KA, Bahar N, Ibrahim N, Mahadevan R, et al. The relations of internet and smartphone addictions to depression, anxiety, stress, and suicidality among public university students in Klang Valley, Malaysia. Perspect Psychiatr Care. (2020) 56:949–55. doi: 10.1111/ppc.12517

57. Kaya F, Bostanci Daştan N, Durar E. Smart phone usage, sleep quality and depression in university students. Int J Soc Psychiatry. (2021) 67:407–14. doi: 10.1177/0020764020960207

58. Gao M, Hu J, Yang L, Ding N, Wei X, Li L, et al. Association of sleep quality during pregnancy with stress and depression: a prospective birth cohort study in China. BMC Pregnancy Childbirth. (2019) 19:444. doi: 10.1186/s12884-019-2583-1

59. Zhang Y, Peters A, Chen G. Perceived stress mediates the associations between sleep quality and symptoms of anxiety and depression among college nursing students. Int J Nurs Educ Scholarsh. (2018) 15:20170020. doi: 10.1515/ijnes-2017-0020

60. Demirci K, Akgönül M, Akpinar A. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. J Behav Addict. (2015) 4:85–92. doi: 10.1556/2006.4.2015.010

61. Li Y, Li G, Liu L, Wu H. Correlations between mobile phone addiction and anxiety, depression, impulsivity, and poor sleep quality among college students: a systematic review and meta-analysis. J Behav Addict. (2020) 9:551–71. doi: 10.1556/2006.2020.00057

62. Lissak G. Adverse physiological and psychological effects of screen time on children and adolescents: literature review and case study. Environ Res. (2018) 164:149–57. doi: 10.1016/j.envres.2018.01.015

63. De-Sola Gutiérrez J, Rodríguez de. Fonseca F, Rubio G. Cell-phone addiction: a review. Front Psychiatry. (2016) 7:175. doi: 10.3389/fpsyt.2016.00175

64. Zhang G, Yang X, Tu X, Ding N, Lau JTF. Prospective relationships between mobile phone dependence and mental health status among Chinese undergraduate students with college adjustment as a mediator. J Affect Disord. (2020) 260:498–505. doi: 10.1016/j.jad.2019.09.047

65. Hong W, Liu RD, Ding Y, Sheng X, Zhen R. Mobile phone addiction and cognitive failures in daily life: The mediating roles of sleep duration and quality and the moderating role of trait self-regulation. Addict Behav. (2020) 107:106383. doi: 10.1016/j.addbeh.2020.106383

66. Leung and Louis. Linking psychological attributes to addiction and improper use of the mobile phone among adolescents in Hong Kong. J Child Media. (2008) 2:93–113. doi: 10.1080/17482790802078565

67. Foerster M, Roser K, Schoeni A, Röösli M. Problematic mobile phone use in adolescents: derivation of a short scale MPPUS-10. Int J Public Health. (2015) 60:277–86. doi: 10.1007/s00038-015-0660-4

68. Xie X, Dong Y, Wang J. Sleep quality as a mediator of problematic smartphone use and clinical health symptoms. J Behav Addict. (2018) 7:466–72. doi: 10.1556/2006.7.2018.40

69. Chen Y, Liu X, Yan N, Jia W, Fan Y, Yan H, et al. Higher academic stress was associated with increased risk of overweight and obesity among college students in China. Int J Environ Res Public Health. (2020) 17:5559. doi: 10.3390/ijerph17155559

70. Wang H, Zhou X, Lu C, Wu J, Deng X, Hong L. Problematic internet use in high school students in Guangdong Province, China. PLoS ONE. (2011) 6:e19660. doi: 10.1371/journal.pone.0019660

71. Fu Y, Ren W, Liang Z. Perceived academic stress and depressive symptoms among Chinese adolescents: a moderated mediation analysis of overweight status. J Affect Disord. (2022) 296:224–32. doi: 10.1016/j.jad.2021.09.060

72. Radloff SL. The CES-D scale a self-report depression scale for research in the general population. Appl Psychol Meas. (1977) 1:385–401. doi: 10.1177/014662167700100306

73. Dou D, Shek DTL, Zhu X, Zhao L. Dimensionality of the Chinese CES-D: Is It Stable across Gender, Time, and Samples? Int J Environ Res Public Health. (2021) 18:11818. doi: 10.3390/ijerph182211818

74. Rankin SH, Galbraith ME, Johnson S. Reliability and validity data for a Chinese translation of the Center for Epidemiological Studies-Depression. Psychol Rep. (1993) 73:1291–8. doi: 10.2466/pr0.1993.73.3f.1291

75. Zhou M, Zhang G, Rozelle S, Kenny K, Xue H. Depressive symptoms of Chinese Children: prevalence and correlated factors among subgroups. Int J Environ Res Public Health. (2018) 15:283. doi: 10.3390/ijerph15020283

76. Lin J, Guo T, Becker B, Yu Q, Chen ST, Brendon S, et al. Depression is associated with moderate-intensity physical activity among college students during the COVID-19 Pandemic: differs by activity level, gender and gender role. Psychol Res Behav Manag. (2020) 13:1123–34. doi: 10.2147/PRBM.S277435

77. Jiang L, Wang Y, Zhang Y, Li R, Wu H, Li C, et al. The reliability and validity of the center for epidemiologic studies depression scale (CES-D) for Chinese University Students. Front Psychiatry. (2019) 10:315. doi: 10.3389/fpsyt.2019.00315

78. Chen Z, Yang X, Li X. Psychometric features of CES-D in Chinese adolescents. Chin J Clin Psychol. (2009) 17:443–5.

Google Scholar

79. Li HC, Chung OK, Ho KY. Center for epidemiologic studies depression scale for children: psychometric testing of the Chinese version. J Adv Nurs. (2010) 66:2582–91. doi: 10.1111/j.1365-2648.2010.05440.x

80. Zhang Y, Ting RZ, Lam MH, Lam SP, Yeung RO, Nan H, et al. Measuring depression with CES-D in Chinese patients with type 2 diabetes: the validity and its comparison to PHQ-9. BMC Psychiatry. (2015) 15:198. doi: 10.1186/s12888-015-0580-0

PubMed Abstract | CrossRef Full Text

81. Cheng ST, Chan AC. The center for epidemiologic studies depression scale in older Chinese: thresholds for long and short forms. Int J Geriatr Psychiatry. (2005) 20:465–70. doi: 10.1002/gps.1314

82. Chi X, Huang L, Wang J, Zhang P. The Prevalence and socio-demographic correlates of depressive symptoms in early adolescents in china: differences in only child and non-only child groups. Int J Environ Res Public Health. (2020) 17:438. doi: 10.3390/ijerph17020438

83. Song Y, Huang Y, Liu D, Kwan JS, Zhang F, Sham PC, et al. Depression in college: depressive symptoms and personality factors in Beijing and Hong Kong college freshmen. Compr Psychiatry. (2008) 49:496–502. doi: 10.1016/j.comppsych.2008.02.005

84. Yang L, Jia CX, Qin P. Reliability and validity of the Center for Epidemiologic Studies Depression Scale (CES-D) among suicide attempters and comparison residents in rural China. BMC Psychiatry. (2015) 15:76. doi: 10.1186/s12888-015-0458-1

85. Leung L, Konijn E, Tanis M, Utz S, Linden A. Leisure Boredom, Sensation Seeking, Self-esteem, Addiction Symptoms and Patterns of Mobile Phone Use: Mediated Interpersonal Communication. 1st ed. New York, NY: Routledge (2007).

86. Mei S, Xu G, Gao T, Ren H, Li J. The relationship between college students' alexithymia and mobile phone addiction: testing mediation and moderation effects. BMC Psychiatry. (2018) 18:329. doi: 10.1186/s12888-018-1891-8

87. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. (1989) 28:193–213. doi: 10.1016/0165-1781(89)90047-4

88. Muthen LK, Muthen BO. Mplus User's Guide. 7. Los Angeles, CA: Muthen & Muthen. (2012).

89. Usp N, Winter SEM. Some clarifications and recommendations on fit indices. Struct Equ Model. (2012) 523:1–4.

90. Schermelleh-Engel K, Moosbrugger H, Müller H. Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures. Mpr Online. (2003) 8:23–74. Available online at: https://www.researchgate.net/publication/251060246

91. Lun KW, Chan CK, Ip PK, Ma SY, Tsai WW, Wong CS, et al. Depression and anxiety among university students in Hong Kong. Hong Kong Med J. (2018) 24:466–72. doi: 10.12809/hkmj176915

92. Shao R, He P, Ling B, Tan L, Xu L, Hou Y, et al. Prevalence of depression and anxiety and correlations between depression, anxiety, family functioning, social support and coping styles among Chinese medical students. BMC Psychol. (2020) 8:38. doi: 10.1186/s40359-020-00402-8

93. Xiao H, Shu W, Li M, Li Z, Tao F, Wu X, et al. Social distancing among medical students during the 2019 coronavirus disease pandemic in China: disease awareness, anxiety disorder, depression, and behavioral activities. Int J Environ Res Public Health. (2020) 17:5047. doi: 10.3390/ijerph17145047

94. Zhang C, Ye M, Fu Y, Yang M, Luo F, Yuan J, et al. The psychological impact of the COVID-19 pandemic on teenagers in China. J Adolesc Health. (2020) 67:747–55. doi: 10.1016/j.jadohealth.2020.08.026

95. Guo S, Kaminga AC, Xiong J. Depression and coping styles of college students in China During COVID-19 pandemic: a systemic review and meta-analysis. Front Public Health. (2021) 9:613321. doi: 10.3389/fpubh.2021.613321

96. Chang EC, Chang OD, Lucas AG, Li M, Beavan CB, Eisner RS, et al. Depression, loneliness, and suicide risk among latino college students: a test of a psychosocial interaction model. Soc Work. (2019) 64:51–60. doi: 10.1093/sw/swy052

97. Cuijpers P, Cristea IA, Ebert DD, Koot HM, Auerbach RP, Bruffaerts R, et al. A metaanalysis. Depress Anxiety. (2016) 33:400–14. doi: 10.1002/da.22461

98. Wu H, Li S, Zheng J, Guo J. Medical students' motivation and academic performance: the mediating roles of self-efficacy and learning engagement. Med Educ Online. (2020) 25:1742964. doi: 10.1080/10872981.2020.1742964

99. Zagalaz-Sánchez ML, Cachón-Zagalaz J, Sánchez-Zafra M, Lara-Sánchez A. Mini review of the use of the mobile phone and its repercussion in the deficit of physical activity. Front Psychol. (2019) 10:1307. doi: 10.3389/fpsyg.2019.01307

100. Wang P, Wang J, Yan Y, Si Y, Zhan X, Tian Y. Relationship between loneliness and depression among chinese junior high school students: the serial mediating roles of internet gaming disorder, social network use, and generalized pathological internet use. Front Psychol. (2021) 11:529665. doi: 10.3389/fpsyg.2020.529665

101. Wang P, Liu S, Zhao M, Yang X, Zhang G, Chu X, et al. How is problematic smartphone use related to adolescent depression? a moderated mediation analysis. Child Youth Serv Rev. (2019) 104:104384. doi: 10.1016/j.childyouth.2019.104384

102. Chen BA, Xc B, Kh A. Mobile phone addiction and school performance among Chinese adolescents from low-income families: a moderated mediation model. Child Youth Serv Rev. (2020) 118:105406. doi: 10.1016/j.childyouth.2020.105406

103. Rashid JA, Aziz, AA, Rahman AA. Saaid SA, & Ahmad, Z. The Influence of Mobile Phone Addiction on Academic Performance Among Teenagers. J Komun Malays J Commun. (2020) 36:408–24. doi: 10.17576/JKMJC-2020-3603-25

CrossRef Full Text

104. Almojali AI, Almalki SA, Alothman AS, Masuadi EM, Alaqeel MK. The prevalence and association of stress with sleep quality among medical students. J Epidemiol Glob Health. (2017) 7:169–74. doi: 10.1016/j.jegh.2017.04.005

105. Lin YQ, Zhuang WJ, Zheng FH, Zeng ZN, Wu YX, Wu SY, et al. Weekday and weekend sleep deprivation are associated with recurrent nightmare in adolescents: a cross-sectional study. Sleep Med. (2020) 76:36–42. doi: 10.1016/j.sleep.2020.09.016

106. Shim EJ, Noh HL, Yoon J, Mun HS, Hahm BJ. A longitudinal analysis of the relationships among daytime dysfunction, fatigue, and depression in college students. J Am Coll Health. (2019) 67:51–8. doi: 10.1080/07448481.2018.1462819

107. Çelik N, Ceylan B, Ünsal A, Çagan Ö. Depression in health college students: relationship factors and sleep quality. Psychol Health Med. (2019) 24:625–30. doi: 10.1080/13548506.2018.1546881

108. Wu X, Tao S, Zhang Y, Zhang S, Tao F. Low physical activity and high screen time can increase the risks of mental health problems and poor sleep quality among Chinese college students. PLoS ONE. (2015) 10:e0119607. doi: 10.1371/journal.pone.0119607

109. Exelmans L, Van den Bulck J. Bedtime mobile phone use and sleep in adults. Soc Sci Med. (2016) 148:93–101. doi: 10.1016/j.socscimed.2015.11.037

110. Baglioni C, Battagliese G, Feige B, Spiegelhalder K, Nissen C, Voderholzer U, et al. Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. J Affect Disord. (2011) 135:10–9. doi: 10.1016/j.jad.2011.01.011

111. Corfield EC, Martin NG, Nyholt DR. Co-occurrence and symptomatology of fatigue and depression. Compr Psychiatry. (2016) 71:1–10. doi: 10.1016/j.comppsych.2016.08.004

112. Liu J, Nie J, Wang Y. Effects of group counseling programs, cognitive behavioral therapy, and sports intervention on internet addiction in East Asia: a systematic review and meta-analysis. Int J Environ Res Public Health. (2017) 14:1470. doi: 10.3390/ijerph14121470

113. Lee WWS. Relationships among grit, academic performance, perceived academic failure, and stress in associate degree students. J Adolesc. (2017) 60:148–52. doi: 10.1016/j.adolescence.2017.08.006

114. Liu S, Xiao T, Yang L, Loprinzi PD. Exercise as an alternative approach for treating smartphone addiction: a systematic review and meta-analysis of random controlled trials. Int J Environ Res Public Health. (2019) 16:3912. doi: 10.3390/ijerph16203912

115. Ghrouz AK, Noohu MM, Dilshad Manzar M, Warren Spence D, BaHammam AS, Pandi-Perumal SR. Physical activity and sleep quality in relation to mental health among college students. Sleep Breath. (2019) 23:627–34. doi: 10.1007/s11325-019-01780-z

116. Paolucci EM, Loukov D, Bowdish DME, Heisz JJ. Exercise reduces depression and inflammation but intensity matters. Biol Psychol. (2018) 133:79–84. doi: 10.1016/j.biopsycho.2018.01.015

117. Balmus IM, Robea M, Ciobica A Timofte D Perceived stress and gastrointestinal habits in college. Students. Acta Endocrinol (Buchar). (2019) 15:274–5. doi: 10.4183/aeb.2019.274

Keywords: perceived academic stress, mobile phone addiction (MPA), sleep quality, depression, depressive symptoms, Chinese students

Citation: Zhang X, Gao F, Kang Z, Zhou H, Zhang J, Li J, Yan J, Wang J, Liu H, Wu Q and Liu B (2022) Perceived Academic Stress and Depression: The Mediation Role of Mobile Phone Addiction and Sleep Quality. Front. Public Health 10:760387. doi: 10.3389/fpubh.2022.760387

Received: 18 August 2021; Accepted: 07 January 2022; Published: 25 January 2022.

Reviewed by:

Copyright © 2022 Zhang, Gao, Kang, Zhou, Zhang, Li, Yan, Wang, Liu, Wu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Qunhong Wu, wuqunhong@163.com ; Baohua Liu, liubaohuawoshi@163.com

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Advertisement

Advertisement

Major Depressive Disorder: Advances in Neuroscience Research and Translational Applications

  • Open access
  • Published: 13 February 2021
  • Volume 37 , pages 863–880, ( 2021 )

Cite this article

You have full access to this open access article

  • Zezhi Li 1 , 2 ,
  • Meihua Ruan 3 ,
  • Jun Chen 1 , 5 &
  • Yiru Fang   ORCID: orcid.org/0000-0002-8748-9085 1 , 4 , 5  

42k Accesses

95 Citations

16 Altmetric

Explore all metrics

A Correction to this article was published on 17 May 2021

This article has been updated

Major depressive disorder (MDD), also referred to as depression, is one of the most common psychiatric disorders with a high economic burden. The etiology of depression is still not clear, but it is generally believed that MDD is a multifactorial disease caused by the interaction of social, psychological, and biological aspects. Therefore, there is no exact pathological theory that can independently explain its pathogenesis, involving genetics, neurobiology, and neuroimaging. At present, there are many treatment measures for patients with depression, including drug therapy, psychotherapy, and neuromodulation technology. In recent years, great progress has been made in the development of new antidepressants, some of which have been applied in the clinic. This article mainly reviews the research progress, pathogenesis, and treatment of MDD.

Similar content being viewed by others

research paper about stress and depression

Molecular Mechanisms of Psilocybin and Implications for the Treatment of Depression

Susan Ling, Felicia Ceban, … Roger S. McIntyre

research paper about stress and depression

Bipolar Disorder and Suicide: a Review

Jacob N. Miller & Donald W. Black

research paper about stress and depression

Animal models of major depression: drawbacks and challenges

Barbara Planchez, Alexandre Surget & Catherine Belzung

Avoid common mistakes on your manuscript.

Major depressive disorder (MDD) also referred to as depression, is one of the most severe and common psychiatric disorders across the world. It is characterized by persistent sadness, loss of interest or pleasure, low energy, worse appetite and sleep, and even suicide, disrupting daily activities and psychosocial functions. Depression has an extreme global economic burden and has been listed as the third largest cause of disease burden by the World Health Organization since 2008, and is expected to rank the first by 2030 [ 1 , 2 ]. In 2016, the Global Burden of Diseases, Injuries, and Risk Factors Study demonstrated that depression caused 34.1 million of the total years lived with disability (YLDs), ranking as the fifth largest cause of YLD [ 3 ]. Therefore, the research progress and the clinical application of new discoveries or new technologies are imminent. In this review, we mainly discuss the current situation of research, developments in pathogenesis, and the management of depression.

Current Situation of Research on Depression

Analysis of published papers.

In the past decade, the total number of papers on depression published worldwide has increased year by year as shown in Fig. 1 A. Searching the Web of Science database, we found a total of 43,863 papers published in the field of depression from 2009 to 2019 (search strategy: TI = (depression$) or ts = ("major depressive disorder$")) and py = (2009 – 2019), Articles). The top 10 countries that published papers on the topic of depression are shown in Fig. 1 B. Among them, researchers in the USA published the most papers, followed by China. Compared with the USA, the gap in the total number of papers published in China is gradually narrowing (Fig. 1 C), but the quality gap reflected by the index (the total number of citations and the number of citations per paper) is still large, and is lower than the global average (Fig. 1 D). As shown in Fig. 1 E, the hot research topics in depression are as follows: depression management in primary care, interventions to prevent depression, the pathogenesis of depression, comorbidity of depression and other diseases, the risks of depression, neuroimaging studies of depression, and antidepressant treatment.

figure 1

Analysis of published papers around the world from 2009 to 2019 in depressive disorder. A The total number of papers [from a search of the Web of Science database (search strategy: TI = (depression$) or ts = ("major depressive disorder$")) and py = (2009 – 2019), Articles)]. B The top 10 countries publishing on the topic. C Comparison of papers in China and the USA. D Citations for the top 10 countries and comparison with the global average. E Hot topics.

Analysis of Patented Technology Application

There were 16,228 patent applications in the field of depression between 2009 and 2019, according to the Derwent Innovation Patent database. The annual number and trend of these patents are shown in Fig. 2 A. The top 10 countries applying for patents related to depression are shown in Fig. 2 B. The USA ranks first in the number of depression-related patent applications, followed by China. The largest number of patents related to depression is the development of antidepressants, and drugs for neurodegenerative diseases such as dementia comorbid with depression. The top 10 technological areas of patents related to depression are shown in Fig. 2 C, and the trend in these areas have been stable over the past decade (Fig. 2 D).

figure 2

Analysis of patented technology applications from 2009 to 2019 in the field of depressive disorder. A Annual numbers and trends of patents (the Derwent Innovation patent database). B The top 10 countries/regions applying for patents. C The top 10 technological areas of patents. D The trend of patent assignees. E Global hot topic areas of patents.

Analysis of technical hotspots based on keyword clustering was conducted from the Derwent Innovation database using the "ThemeScape" tool. This demonstrated that the hot topic areas are as follows (Fig. 2 E): (1) improvement for formulation and the efficiency of hydrobromide, as well as optimization of the dosage; intervention for depression comorbid with AD, diabetes, and others; (3) development of alkyl drugs; (4) development of pharmaceutical acceptable salts as antidepressants; (5) innovation of the preparation of antidepressants; (6) development of novel antidepressants based on neurotransmitters; (7) development of compositions based on nicotinic acetylcholine receptors; and (8) intervention for depression with traditional Chinese medicine.

Analysis of Clinical Trial

There are 6,516 clinical trials in the field of depression in the ClinicalTrials.gov database, and among them, 1,737 valid trials include the ongoing recruitment of subjects, upcoming recruitment of subjects, and ongoing clinical trials. These clinical trials are mainly distributed in the USA (802 trials), Canada (155), China (114), France (93), Germany (66), UK (62), Spain (58), Denmark (41), Sweden (39), and Switzerland (23). The indications for clinical trials include various types of depression, such as minor depression, depression, severe depression, perinatal depression, postpartum depression, and depression comorbid with other psychiatric disorders or physical diseases, such as schizophrenia, epilepsy, stroke, cancer, diabetes, cardiovascular disease, and Parkinson's disease.

Based on the database of the Chinese Clinical Trial Registry website, a total of 143 clinical trials for depression have been carried out in China. According to the type of research, they are mainly interventional and observational studies, as well as a small number of related factor studies, epidemiological studies, and diagnostic trials. The research content involves postpartum, perinatal, senile, and other age groups with clinical diagnosis (imaging diagnosis) and intervention studies (drugs, acupuncture, electrical stimulation, transcranial magnetic stimulation). It also includes intervention studies on depression comorbid with coronary heart disease, diabetes, and heart failure.

New Medicine Development

According to the Cortellis database, 828 antidepressants were under development by the end of 2019, but only 292 of these are effective and active (Fig. 3 A). Large number of them have been discontinued or made no progress, indicating that the development of new drugs in the field of depression is extremely urgent.

figure 3

New medicine development from 2009 to 2019 in depressive disorder. A Development status of new candidate drugs. B Top target-based actions.

From the perspective of target-based actions, the most common new drugs are NMDA receptor antagonists, followed by 5-HT targets, as well as dopamine receptor agonists, opioid receptor antagonists and agonists, AMPA receptor modulators, glucocorticoid receptor antagonists, NK1 receptor antagonists, and serotonin transporter inhibitors (Fig. 3 B).

Epidemiology of Depression

The prevalence of depression varies greatly across cultures and countries. Previous surveys have demonstrated that the 12-month prevalence of depression was 0.3% in the Czech Republic, 10% in the USA, 4.5% in Mexico, and 5.2% in West Germany, and the lifetime prevalence of depression was 1.0% in the Czech Republic, 16.9% in the USA, 8.3% in Canada, and 9.0% in Chile [ 4 , 5 ]. A recent meta-analysis including 30 Countries showed that lifetime and 12-month prevalence depression were 10.8% and 7.2%, respectively [ 6 ]. In China, the lifetime prevalence of depression ranged from 1.6% to 5.5% [ 7 , 8 , 9 ]. An epidemiological study demonstrated that depression was the most common mood disorder with a life prevalence of 3.4% and a 12-month prevalence of 2.1% in China [ 10 ].

Some studies have also reported the prevalence in specific populations. The National Comorbidity Survey-Adolescent Supplement (NCS-A) survey in the USA showed that the lifetime and 12-month prevalence of depression in adolescents aged 13 to 18 were 11.0% and 7.5%, respectively [ 11 ]. A recent meta-analysis demonstrated that lifetime prevalence and 12-month prevalence were 2.8% and 2.3%, respectively, among the elderly population in China [ 12 ].

Neurobiological Pathogenesis of Depressive Disorder

The early hypothesis of monoamines in the pathophysiology of depression has been accepted by the scientific community. The evidence that monoamine oxidase inhibitors and tricyclic antidepressants promote monoamine neurotransmission supports this theory of depression [ 13 ]. So far, selective serotonin reuptake inhibitors and norepinephrine reuptake inhibitors are still the first-line antidepressants. However, there remain 1/3 to 2/3 of depressed patients who do not respond satisfactorily to initial antidepressant treatment, and even as many as 15%–40% do not respond to several pharmacological medicines [ 14 , 15 ]. Therefore, the underlying pathogenesis of depression is far beyond the simple monoamine mechanism.

Other hypotheses of depression have gradually received increasing attention because of biomarkers for depression and the effects pharmacological treatments, such as the stress-responsive hypothalamic pituitary adrenal (HPA) axis, neuroendocrine systems, the neurotrophic family of growth factors, and neuroinflammation.

Stress-Responsive HPA Axis

Stress is causative or a contributing factor to depression. Particularly, long-term or chronic stress can lead to dysfunction of the HPA axis and promote the secretion of hormones, including cortisol, adrenocorticotropic hormone, corticotropin-releasing hormone, arginine vasopressin, and vasopressin. About 40%–60% of patients with depression display a disturbed HPA axis, including hypercortisolemia, decreased rhythmicity, and elevated cortisol levels [ 16 , 17 ]. Mounting evidence has shown that stress-induced abnormality of the HPA axis is associated with depression and cognitive impairment, which is due to the increased secretion of cortisol and the insufficient inhibition of glucocorticoid receptor regulatory feedback [ 18 , 19 ]. In addition, it has been reported that the increase in cortisol levels is related to the severity of depression, especially in melancholic depression [ 20 , 21 ]. Further, patients with depression whose HPA axis was not normalized after treatment had a worse clinical response and prognosis [ 22 , 23 ]. Despite the above promising insights, unfortunately previous studies have shown that treatments regulating the HPA axis, such as glucocorticoid receptor antagonists, do not attenuate the symptoms of depressed patients [ 24 , 25 ].

Glutamate Signaling Pathway

Glutamate is the main excitatory neurotransmitter released by synapses in the brain; it is involved in synaptic plasticity, cognitive processes, and reward and emotional processes. Stress can induce presynaptic glutamate secretion by neurons and glutamate strongly binds to ionotropic glutamate receptors (iGluRs) including N-methyl-D-aspartate receptors (NMDARs) and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptors (AMPARs) [ 26 ] on the postsynaptic membrane to activate downstream signal pathways [ 27 ]. Accumulating evidence has suggested that the glutamate system is associated with the incidence of depression. Early studies have shown increased levels of glutamate in the peripheral blood, cerebrospinal fluid, and brain of depressed patients [ 28 , 29 ], as well as NMDAR subunit disturbance in the brain [ 30 , 31 ]. Blocking the function of NMDARs has an antidepressant effect and protects hippocampal neurons from morphological abnormalities induced by stress, while antidepressants reduce glutamate secretion and NMDARs [ 32 ]. Most importantly, NMDAR antagonists such as ketamine have been reported to have profound and rapid antidepressant effects on both animal models and the core symptoms of depressive patients [ 33 ]. On the other hand, ketamine can also increase the AMPAR pathway in hippocampal neurons by up-regulating the AMPA glutamate receptor 1 subunit [ 34 ]. Further, the AMPAR pathway may be involved in the mechanism of antidepressant effects. For example, preclinical studies have indicated that AMPAR antagonists might attenuate lithium-induced depressive behavior by increasing the levels of glutamate receptors 1 and 2 in the mouse hippocampus [ 35 ].

Gamma-Aminobutyric Acid (GABA)

Contrary to glutamate, GABA is the main inhibitory neurotransmitter. Although GABA neurons account for only a small proportion compared to glutamate, inhibitory neurotransmission is essential for brain function by balancing excitatory transmission [ 36 ]. Number of studies have shown that patients with depression have neurotransmission or functional defects of GABA [ 37 , 38 ]. Schür et al ., conducted a meta-analysis of magnetic resonance spectroscopy studies, which showed that the brain GABA level in depressive patients was lower than that in healthy controls, but no difference was found in depressive patients in remission [ 39 ]. Several postmortem studies have shown decreased levels of the GABA synthase glutamic acid decarboxylase in the prefrontal cortex of patients with depression [ 40 , 41 ]. It has been suggested that a functional imbalance of the GABA and glutamate systems contributes to the pathophysiology of depression, and activation of the GABA system might induce antidepressant activity, by which GABA A  receptor mediators α2/α3 are considered potential antidepressant candidates [ 42 , 43 ]. Genetic mouse models, such as the GABA A receptor mutant mouse and conditional the Gad1-knockout mouse (GABA in hippocampus and cerebral cortex decreased by 50%) and optogenetic methods have verified that depression-like behavior is induced by changing the level of GABA [ 44 , 45 ].

Neurotrophin Family

The neurotrophin family plays a key role in neuroplasticity and neurogenesis. The neurotrophic hypothesis of depression postulates that a deficit of neurotrophic support leads to neuronal atrophy, the reduction of neurogenesis, and the destruction of glia support, while antidepressants attenuate or reverse these pathophysiological processes [ 46 ]. Among them, the most widely accepted hypothesis involves brain-derived neurotrophic factor (BDNF). This was initially triggered by evidence that stress reduces the BDNF levels in the animal brain, while antidepressants rescue or attenuate this reduction [ 47 , 48 ], and agents involved in the BDNF system have been reported to exert antidepressant-like effects [ 49 , 50 ]. In addition, mounting studies have reported that the BDNF level is decreased in the peripheral blood and at post-mortem in depressive patients, and some have reported that antidepressant treatment normalizes it [ 51 , 52 ]. Furthermore, some evidence also showed that the interaction of BDNF and its receptor gene is associated with treatment-resistant depression [ 15 ].

Recent studies reported that depressed patients have a lower level of the pro-domain of BDNF (BDNF pro-peptide) than controls. This is located presynaptically and promotes long-term depression in the hippocampus, suggesting that it is a promising synaptic regulator [ 53 ].

Neuroinflammation

The immune-inflammation hypothesis has attracted much attention, suggesting that the interactions between inflammatory pathways and neural circuits and neurotransmitters are involved in the pathogenesis and pathophysiological processes of depression. Early evidence found that patients with autoimmune or infectious diseases are more likely to develop depression than the general population [ 54 ]. In addition, individuals without depression may display depressive symptoms after treatment with cytokines or cytokine inducers, while antidepressants relieve these symptoms [ 55 , 56 ]. There is a complex interaction between the peripheral and central immune systems. Previous evidence suggested that peripheral inflammation/infection may spread to the central nervous system in some way and cause a neuroimmune response [ 55 , 57 ]: (1) Some cytokines produced in the peripheral immune response, such as IL-6 and IL-1 β, can leak into the brain through the blood-brain barrier (BBB). (2) Cytokines entering the central nervous system act directly on astrocytes, small stromal cells, and neurons. (3) Some peripheral immune cells can cross the BBB through specific transporters, such as monocytes. (4) Cytokines and chemokines in the circulation activate the central nervous system by regulating the surface receptors of astrocytes and endothelial cells at the BBB. (5) As an intermediary pathway, the immune inflammatory response transmits peripheral danger signals to the center, amplifies the signals, and shows the external phenotype of depressive behavior associated with stress/trauma/infection. (6) Cytokines and chemokines may act directly on neurons, change their plasticity and promote depression-like behavior.

Patients with depression show the core feature of the immune-inflammatory response, that is, increased concentrations of pro-inflammatory cytokines and their receptors, chemokines, and soluble adhesion molecules in peripheral blood and cerebrospinal fluid [ 58 , 59 , 60 ]. Peripheral immune-inflammatory response markers not only change the immune activation state in the brain that affects explicit behavior, but also can be used as an evaluation index or biological index of antidepressant therapy [ 61 , 62 ]. Li et al . showed that the level of TNF-α in patients with depression prior to treatment was higher than that in healthy controls. After treatment with venlafaxine, the level of TNF-α in patients with depression decreased significantly, and the level of TNF-α in the effective group decreased more [ 63 ]. A recent meta-analysis of 1,517 patients found that antidepressants significantly reduced peripheral IL-6, TNF-α, IL-10, and CCL-2, suggesting that antidepressants reduce markers of peripheral inflammatory factors [ 64 ]. Recently, Syed et al . also confirmed that untreated patients with depression had higher levels of inflammatory markers and increased levels of anti-inflammatory cytokines after antidepressant treatment, while increased levels of pro-inflammatory cytokines were found in non-responders [ 62 ]. Clinical studies have also found that anti-inflammatory cytokines, such as monoclonal antibodies and other cytokine inhibitors, may play an antidepressant role by blocking cytokines. The imbalance of pro-inflammatory and anti-inflammatory cytokines may be involved in the pathophysiological process of depression.

In addition, a recent study showed that microglia contribute to neuronal plasticity and neuroimmune interaction that are involved in the pathophysiology of depression [ 65 ]. When activated microglia promote inflammation, especially the excessive production of pro-inflammatory factors and cytotoxins in the central nervous system, depression-like behavior can gradually develop [ 65 , 66 ]. However, microglia change polarization as two types under different inflammatory states, regulating the balance of pro- and anti-inflammatory factors. These two types are M1 and M2 microglia; the former produces large number of pro-inflammatory cytokines after activation, and the latter produces anti-inflammatory cytokines. An imbalance of M1/M2 polarization of microglia may contribute to the pathophysiology of depression [ 67 ].

Microbiome-Gut-Brain Axis

The microbiota-gut-brain axis has recently gained more attention because of its ability to regulate brain activity. Many studies have shown that the microbiota-gut-brain axis plays an important role in regulating mood, behavior, and neuronal transmission in the brain [ 68 , 69 ]. It is well established that comorbidity of depression and gastrointestinal diseases is common [ 70 , 71 ]. Some antidepressants can attenuate the symptoms of patients with irritable bowel syndrome and eating disorders [ 72 ]. It has been reported that gut microbiome alterations are associated with depressive-like behaviors [ 73 , 74 ], and brain function [ 75 ]. Early animal studies have shown that stress can lead to long-term changes in the diversity and composition of intestinal microflora, and is accompanied by depressive behavior [ 76 , 77 ]. Interestingly, some evidence indicates that rodents exhibit depressive behavior after fecal transplants from patients with depression [ 74 ]. On the other hand, some probiotics attenuated depressive-like behavior in animal studies, [ 78 ] and had antidepressant effects on patients with depression in several double-blind, placebo-controlled clinical trials [ 79 , 80 ].

The potential mechanism may be that gut microbiota can interact with the brain through a variety of pathways or systems, including the HPA axis, and the neuroendocrine, autonomic, and neuroimmune systems [ 81 ]. For example, recent evidence demonstrated that gut microbiota can affect the levels of neurotransmitters in the gut and brain, including serotonin, dopamine, noradrenalin, glutamate, and GABA [ 82 ]. In addition, recent studies showed that changes in gut microbiota can also impair the gut barrier and promote higher levels of peripheral inflammatory cytokines [ 83 , 84 ]. Although recent research in this area has made significant progress, more clinical trials are needed to determine whether probiotics have any effect on the treatment of depression and what the potential underlying mechanisms are.

Other Systems and Pathways

There is no doubt that several other systems or pathways are also involved in the pathophysiology of depression, such as oxidant-antioxidant imbalance [ 85 ], mitochondrial dysfunction [ 86 , 87 ], and circadian rhythm-related genes [ 88 ], especially their critical interactions ( e.g. interaction between the HPA and mitochondrial metabolism [ 89 , 90 ], and the reciprocal interaction between oxidative stress and inflammation [ 2 , 85 ]). The pathogenesis of depression is complex and all the hypotheses should be integrated to consider the many interactions between various systems and pathways.

Advances in Various Kinds of Research on Depressive Disorder

Genetic, molecular, and neuroimaging studies continue to increase our understanding of the neurobiological basis of depression. However, it is still not clear to what extent the results of neurobiological studies can help improve the clinical and functional prognosis of patients. Therefore, over the past 10 years, the neurobiological study of depression has become an important measure to understand the pathophysiological mechanism and guide the treatment of depression.

Genetic Studies

Previous twin and adoption studies have indicated that depression has relatively low rate of heritability at 37% [ 91 ]. In addition, environmental factors such as stressful events are also involved in the pathogenesis of depression. Furthermore, complex psychiatric disorders, especially depression, are considered to be polygenic effects that interact with environmental factors [ 13 ]. Therefore, reliable identification of single causative genes for depression has proved to be challenging. The first genome-wide association studies (GWAS) for depression was published in 2009, and included 1,738 patients and 1,802 controls [ 92 , 93 ]. Although many subsequent GWASs have determined susceptible genes in the past decade, the impact of individual genes is so small that few results can be replicated [ 94 , 95 ]. So far, it is widely accepted that specific single genetic mutations may play minor and marginal roles in complex polygenic depression. Another major recognition in GWASs over the past decade is that prevalent candidate genes are usually not associated with depression. Further, the inconsistent results may also be due to the heterogeneity and polygenic nature of genetic and non-genetic risk factors for depression as well as the heterogeneity of depression subtypes [ 95 , 96 ]. Therefore, to date, the quality of research has been improved in two aspects: (1) the sample size has been maximized by combining the data of different evaluation models; and (2) more homogenous subtypes of depression have been selected to reduce phenotypic heterogeneity [ 97 ]. Levinson et al . pointed out that more than 75,000 to 100,000 cases should be considered to detect multiple depression associations [ 95 ]. Subsequently, several recent GWASs with larger sample sizes have been conducted. For example, Okbay et al . identified two loci associated with depression and replicated them in separate depression samples [ 98 ]. Wray et al . also found 44 risk loci associated with depression based on 135,458 cases and 344,901 controls [ 99 ]. A recent GWAS of 807,553 individuals with depression reported that 102 independent variants were associated with depression; these were involved in synaptic structure and neural transmission, and were verified in a further 1,507,153 individuals [ 100 ]. However, even with enough samples, GWASs still face severe challenges. A GWAS only marks the region of the genome and is not directly related to the potential biological function. In addition, a genetic association with the indicative phenotype of depression may only be part of many pathogenic pathways, or due to the indirect influence of intermediate traits in the causal pathway on the final result [ 101 ].

Given the diversity of findings, epigenetic factors are now being investigated. Recent studies indicated that epigenetic mechanisms may be the potential causes of "loss of heritability" in GWASs of depression. Over the past decade, a promising discovery has been that the effects of genetic information can be directly influenced by environment factors, and several specific genes are activated by environmental aspects. This process is described as interactions between genes and the environment, which is identified by the epigenetic mechanism. Environmental stressors cause alterations in gene expression in the brain, which may cause abnormal neuronal plasticity in areas related to the pathogenesis of the disease. Epigenetic events alter the structure of chromatin, thereby regulating gene expression involved in neuronal plasticity, stress behavior, depressive behavior, and antidepressant responses, including DNA methylation, histone acetylation, and the role of non-coding RNA. These new mechanisms of trans-generational transmission of epigenetic markers are considered a supplement to orthodox genetic heredity, providing the possibility for the discovery of new treatments for depression [ 102 , 103 ]. Recent studies imply that life experiences, including stress and enrichment, may affect cellular and molecular signaling pathways in sperm and influence the behavioral and physiological phenotypes of offspring in gender-specific patterns, which may also play an important role in the development of depression [ 103 ].

Brain Imaging and Neuroimaging Studies

Neuroimaging, including magnetic resonance imaging (MRI) and molecular imaging, provides a non-invasive technique for determining the underlying etiology and individualized treatment for depression. MRI can provide important data on brain structure, function, networks, and metabolism in patients with depression; it includes structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging, and magnetic resonance spectroscopy.

Previous sMRI studies have found damaged gray matter in depression-associated brain areas, including the frontal lobe, anterior cingulate gyrus, hippocampus, putamen, thalamus, and amygdala. sMRI focuses on the thickness of gray matter and brain morphology [ 104 , 105 ]. A recent meta-analysis of 2,702 elderly patients with depression and 11,165 controls demonstrated that the volumes of the whole brain and hippocampus of patients with depression were lower than those of the control group [ 106 ]. Some evidence also showed that the hippocampal volume in depressive patients was lower than that of controls, and increased after treatment with antidepressants [ 107 ] and electroconvulsive therapy (ECT) [ 108 ], suggesting that the hippocampal volume plays a critical role in the development, treatment response, and clinical prognosis of depression. A recent study also reported that ECT increased the volume of the right hippocampus, amygdala, and putamen in patients with treatment-resistant depression [ 109 ]. In addition, postmortem research supported the MRI study showing that dentate gyrus volume was decreased in drug-naive patients with depression compared to healthy controls, and was potentially reversed by treatment with antidepressants [ 110 ].

Diffusion tensor imaging detects the microstructure of the white matter, which has been reported impaired in patients with depression [ 111 ]. A recent meta-analysis that included first-episode and drug-naïve depressive patients showed that the decrease in fractional anisotropy was negatively associated with illness duration and clinical severity [ 112 ].

fMRI, including resting-state and task-based fMRI, can divide the brain into self-related regions, such as the anterior cingulate cortex, posterior cingulate cortex, medial prefrontal cortex, precuneus, and dorsomedial thalamus. Many previous studies have shown the disturbance of several brain areas and intrinsic neural networks in patients with depression which could be rescued by antidepressants [ 113 , 114 , 115 , 116 ]. Further, some evidence also showed an association between brain network dysfunction and the clinical correlates of patients with depression, including clinical symptoms [ 117 ] and the response to antidepressants [ 118 , 119 ], ECT [ 120 , 121 ], and repetitive transcranial magnetic stimulation [ 122 ].

It is worth noting that brain imaging provides new insights into the large-scale brain circuits that underlie the pathophysiology of depressive disorder. In such studies, large-scale circuits are often referred to as “networks”. There is evidence that a variety of circuits are involved in the mechanisms of depressive disorder, including disruption of the default mode, salience, affective, reward, attention, and cognitive control circuits [ 123 ]. Over the past decade, the study of intra-circuit and inter-circuit connectivity dysfunctions in depression has escalated, in part due to advances in precision imaging and analysis techniques [ 124 ]. Circuit dysfunction is a potential biomarker to guide psychopharmacological treatment. For example, Williams et al . found that hyper-activation of the amygdala is associated with a negative phenotype that can predict the response to antidepressants [ 125 ]. Hou et al . showed that the baseline characteristics of the reward circuit predict early antidepressant responses [ 126 ].

Molecular imaging studies, including single photon emission computed tomography and positron emission tomography, focus on metabolic aspects such as amino-acids, neurotransmitters, glucose, and lipids at the cellular level in patients with depression. A recent meta-analysis examined glucose metabolism and found that glucose uptake dysfunction in different brain regions predicts the treatment response [ 127 ].

The most important and promising studies were conducted by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, which investigated the human brain across 43 countries. The ENIGMA-MDD Working Group was launched in 2012 to detect the structural and functional changes associated with MDD reliably and replicate them in various samples around the world [ 128 ]. So far, the ENIGMA-MDD Working Group has collected data from 4,372 MDD patients and 9,788 healthy controls across 14 countries, including 45 cohorts [ 128 ]. Their findings to date are shown in Table 1 [ 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 ].

Objective Index for Diagnosis of MDD

To date, the clinical diagnosis of depression is subjectively based on interviews according to diagnostic criteria ( e.g. International Classification of Diseases and Diagnostic and Statistical Manual diagnostic systems) and the severity of clinical symptoms are assessed by questionnaires, although patients may experience considerable differences in symptoms and subtypes [ 138 ]. Meanwhile, biomarkers including genetics, epigenetics, peripheral gene and protein expression, and neuroimaging markers may provide a promising supplement for the development of the objective diagnosis of MDD, [ 139 , 140 , 141 ]. However, the development of reliable diagnosis for MDD using biomarkers is still difficult and elusive, and all methods based on a single marker are insufficiently specific and sensitive for clinical use [ 142 ]. Papakostas et al . showed that a multi-assay, serum-based test including nine peripheral biomarkers (soluble tumor necrosis factor alpha receptor type II, resistin, prolactin, myeloperoxidase, epidermal growth factor, BDNF, alpha1 antitrypsin, apolipoprotein CIII, brain-derived neurotrophic factor, and cortisol) yielded a specificity of 81.3% and a sensitivity of 91.7% [ 142 ]. However, the sample size was relatively small and no other studies have yet validated their results. Therefore, further studies are needed to identify biomarker models that integrate all biological variables and clinical features to improve the specificity and sensitivity of diagnosis for MDD.

Management of Depression

The treatment strategies for depression consist of pharmacological treatment and non-pharmacological treatments including psychotherapy, ECT [ 98 ], and transcranial magnetic stimulation. As psychotherapy has been shown to have effects on depression including attenuating depressive symptoms and improving the quality of life [ 143 , 144 ]; several practice guidelines are increasingly recommending psychotherapy as a monotherapy or in combination with antidepressants [ 145 , 146 ].

Current Antidepressant Treatment

Antidepressants approved by the US Food and Drug Administration (FDA) are shown in Table 2 . Due to the relatively limited understanding of the etiology and pathophysiology of depression, almost all the previous antidepressants were discovered by accident a few decades ago. Although most antidepressants are usually safe and effective, there are still some limitations, including delayed efficacy (usually 2 weeks) and side-effects that affect the treatment compliance [ 147 ]. In addition, <50% of all patients with depression show complete remission through optimized treatment, including trials of multiple drugs with and without simultaneous psychotherapy. In the past few decades, most antidepressant discoveries focused on finding faster, safer, and more selective serotonin or norepinephrine receptor targets. In addition, there is an urgent need to develop new approaches to obtain more effective, safer, and faster antidepressants. In 2019, the FDA approved two new antidepressants: Esketamine for refractory depression and Bresanolone for postpartum depression. Esmolamine, a derivative of the anesthetic drug ketamine, was approved by the FDA for the treatment of refractory depression, based on a large number of preliminary clinical studies [ 148 ]. For example, several randomized controlled trials and meta-analysis studies showed the efficacy and safety of Esketamine in depression or treatment-resistant depression [ 26 , 149 , 150 ]. Although both are groundbreaking new interventions for these debilitating diseases and both are approved for use only under medical supervision, there are still concerns about potential misuse and problems in the evaluation of mental disorders [ 151 ].

To date, although several potential drugs have not yet been approved by the FDA, they are key milestones in the development of antidepressants that may be modified and used clinically in the future, such as compounds containing dextromethorphan (a non-selective NMDAR antago–nist), sarcosine (N-methylglycine, a glycine reuptake inhibitor), AMPAR modulators, and mGluR modulators [ 152 ].

Neuromodulation Therapy

Neuromodulation therapy acts through magnetic pulse, micro-current, or neural feedback technology within the treatment dose, acting on the central or peripheral nervous system to regulate the excitatory/inhibitory activity to reduce or attenuate the symptoms of the disease.

ECT is one of most effective treatments for depression, with the implementation of safer equipment and advancement of techniques such as modified ECT [ 153 ]. Mounting evidence from randomized controlled trial (RCT) and meta-analysis studies has shown that rTMS can treat depressive patients with safety [ 154 ]. Other promising treatments for depression have emerged, such as transcranial direct current stimulation (tDCS) [ 155 ], transcranial alternating current stimulation (tACS)[ 156 ], vagal nerve stimulation [ 157 ], deep brain stimulation [ 158 ] , and light therapy [ 159 ], but some of them are still experimental to some extent and have not been widely used. For example, compared to tDCS, tACS displays less sensory experience and adverse reactions with weak electrical current in a sine-wave pattern, but the evidence for the efficacy of tACS in the treatment of depression is still limited [ 160 ]. Alexander et al . recently demonstrated that there was no difference in efficacy among different treatments (sham, 10-Hz and 40-Hz tACS). However, only the 10-Hz tACS group had more responders than the sham and 40-Hz tACS groups at week 2 [ 156 ]. Further RCT studies are needed to verify the efficacy of tACS. In addition, the mechanism of the effect of neuromodulation therapy on depression needs to be further investigated.

Precision Medicine for Depression

Optimizing the treatment strategy is an effective way to improve the therapeutic effect on depression. However, each individual with depression may react very differently to different treatments. Therefore, this raises the question of personalized treatment, that is, which patients are suitable for which treatment. Over the past decade, psychiatrists and psychologists have focused on individual biomarkers and clinical characteristics to predict the efficiency of antidepressants and psychotherapies, including genetics, peripheral protein expression, electrophysiology, neuroimaging, neurocognitive performance, developmental trauma, and personality [ 161 ]. For example, Bradley et al . recently conducted a 12-week RCT, which demonstrated that the response rate and remission rates of the pharmacogenetic guidance group were significantly higher than those of the non-pharmacogenetic guidance group [ 162 ].

Subsequently, Greden et al . conducted an 8-week RCT of Genomics Used to Improve Depression Decisions (GUIDED) on 1,167 MDD patients and demonstrated that although there was no difference in symptom improvement between the pharmacogenomics-guided and non- pharmacogenomics-guided groups, the response rate and remission rate of the pharmacogenomics-guided group increased significantly [ 163 ].

A recent meta-analysis has shown that the baseline default mode network connectivity in patients with depression can predict the clinical responses to treatments including cognitive behavioral therapy, pharmacotherapy, ECT, rTMS, and transcutaneous vagus nerve stimulation [ 164 ]. However, so far, the biomarkers that predict treatment response at the individual level have not been well applied in the clinic, and there is still a lot of work to be conducted in the future.

Future Perspectives

Although considerable progress has been made in the study of depression during a past decade, the heterogeneity of the disease, the effectiveness of treatment, and the gap in translational medicine are critical challenges. The main dilemma is that our understanding of the etiology and pathophysiology of depression is inadequate, so our understanding of depression is not deep enough to develop more effective treatment. Animal models still cannot fully simulate this heterogeneous and complex mental disorder. Therefore, how to effectively match the indicators measured in animals with those measured in genetic research or the development of new antidepressants is another important challenge.

Change history

17 may 2021.

A Correction to this paper has been published: https://doi.org/10.1007/s12264-021-00694-9

Dadi AF, Miller ER, Bisetegn TA, Mwanri L. Global burden of antenatal depression and its association with adverse birth outcomes: an umbrella review. BMC Public Health 2020, 20: 173

Article   PubMed   PubMed Central   Google Scholar  

Zhu S, Zhao L, Fan Y, Lv Q, Wu K, Lang X. Interaction between TNF-alpha and oxidative stress status in first-episode drug-naive schizophrenia. Psychoneuroendocrinology 2020, 114: 104595

Article   CAS   PubMed   Google Scholar  

Disease GBD, Injury I, Prevalence C. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017, 390: 1211–1259

Article   Google Scholar  

Andrade L, Caraveo-Anduaga JJ, Berglund P, Bijl RV, De Graaf R, Vollebergh W, et al. The epidemiology of major depressive episodes: results from the International Consortium of Psychiatric Epidemiology (ICPE) Surveys. Int J Methods Psychiatr Res 2003, 12: 3–21

Article   PubMed   Google Scholar  

Kessler RC, Bromet EJ. The epidemiology of depression across cultures. Annu Rev Public Health 2013, 34: 119–138

Lim GY, Tam WW, Lu Y, Ho CS, Zhang MW, Ho RC. Prevalence of depression in the community from 30 countries between 1994 and 2014. Sci Rep 2018, 8: 2861

Liu J, Yan F, Ma X, Guo HL, Tang YL, Rakofsky JJ, et al. Prevalence of major depressive disorder and socio-demographic correlates: Results of a representative household epidemiological survey in Beijing, China. J Affect Disord 2015, 179: 74–81

Zhang YS, Rao WW, Cui LJ, Li JF, Li L, Ng CH, et al. Prevalence of major depressive disorder and its socio-demographic correlates in the general adult population in Hebei province, China. J Affect Disord 2019, 252: 92–98

Ma X, Xiang YT, Cai ZJ, Li SR, Xiang YQ, Guo HL, et al. Prevalence and socio-demographic correlates of major depressive episode in rural and urban areas of Beijing, China. J Affect Disord 2009, 115: 323–330

Huang Y, Wang Y, Wang H, Liu Z, Yu X, Yan J, et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study. Lancet Psychiatry 2019, 6: 211–224

Avenevoli S, Swendsen J, He JP, Burstein M, Merikangas KR. Major depression in the national comorbidity survey-adolescent supplement: prevalence, correlates, and treatment. J Am Acad Child Adolesc Psychiatry 2015, 54(37–44): e32

Google Scholar  

Wang F, Zhang QE, Zhang L, Ng CH, Ungvari GS, Yuan Z, et al. Prevalence of major depressive disorder in older adults in China: A systematic review and meta-analysis. J Affect Disord 2018, 241: 297–304

Krishnan V, Nestler EJ. The molecular neurobiology of depression. Nature 2008, 455: 894–902

Article   CAS   PubMed   PubMed Central   Google Scholar  

Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW, Warden D, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry 2006, 163: 1905–1917

Li Z, Zhang Y, Wang Z, Chen J, Fan J, Guan Y, et al. The role of BDNF, NTRK2 gene and their interaction in development of treatment-resistant depression: data from multicenter, prospective, longitudinal clinic practice. J Psychiatr Res 2013, 47: 8–14

Murphy BE. Steroids and depression. J Steroid Biochem Mol Biol 1991, 38: 537–559

Pariante CM, Lightman SL. The HPA axis in major depression: classical theories and new developments. Trends Neurosci 2008, 31: 464–468

Keller J, Gomez R, Williams G, Lembke A, Lazzeroni L, Murphy GM Jr, et al. HPA axis in major depression: cortisol, clinical symptomatology and genetic variation predict cognition. Mol Psychiatry 2017, 22: 527–536

Gomez RG, Fleming SH, Keller J, Flores B, Kenna H, DeBattista C, et al. The neuropsychological profile of psychotic major depression and its relation to cortisol. Biol Psychiatry 2006, 60: 472–478

Lamers F, Vogelzangs N, Merikangas KR, de Jonge P, Beekman AT, Penninx BW. Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Mol Psychiatry 2013, 18: 692–699

Nandam LS, Brazel M, Zhou M, Jhaveri DJ. Cortisol and major depressive disorder-translating findings from humans to animal models and back. Front Psychiatry 2019, 10: 974

Owashi T, Otsubo T, Oshima A, Nakagome K, Higuchi T, Kamijima K. Longitudinal neuroendocrine changes assessed by dexamethasone/CRH and growth hormone releasing hormone tests in psychotic depression. Psychoneuroendocrinology 2008, 33: 152–161

Mickey BJ, Ginsburg Y, Sitzmann AF, Grayhack C, Sen S, Kirschbaum C, et al. Cortisol trajectory, melancholia, and response to electroconvulsive therapy. J Psychiatr Res 2018, 103: 46–53

Stetler C, Miller GE. Depression and hypothalamic-pituitary-adrenal activation: a quantitative summary of four decades of research. Psychosom Med 2011, 73: 114–126

Aubry JM. CRF system and mood disorders. J Chem Neuroanat 2013, 54: 20–24

Correia-Melo FS, Leal GC, Vieira F, Jesus-Nunes AP, Mello RP, Magnavita G, et al. Efficacy and safety of adjunctive therapy using esketamine or racemic ketamine for adult treatment-resistant depression: A randomized, double-blind, non-inferiority study. J Affect Disord 2020, 264: 527–534

Duman RS, Voleti B. Signaling pathways underlying the pathophysiology and treatment of depression: novel mechanisms for rapid-acting agents. Trends Neurosci 2012, 35: 47–56

Sanacora G, Zarate CA, Krystal JH, Manji HK. Targeting the glutamatergic system to develop novel, improved therapeutics for mood disorders. Nat Rev Drug Discov 2008, 7: 426–437

Hashimoto K. The role of glutamate on the action of antidepressants. Prog Neuropsychopharmacol Biol Psychiatry 2011, 35: 1558–1568

Gray AL, Hyde TM, Deep-Soboslay A, Kleinman JE, Sodhi MS. Sex differences in glutamate receptor gene expression in major depression and suicide. Mol Psychiatry 2015, 20: 1057–1068

Chandley MJ, Szebeni A, Szebeni K, Crawford JD, Stockmeier CA, Turecki G, et al. Elevated gene expression of glutamate receptors in noradrenergic neurons from the locus coeruleus in major depression. Int J Neuropsychopharmacol 2014, 17: 1569–1578

Musazzi L, Treccani G, Mallei A, Popoli M. The action of antidepressants on the glutamate system: regulation of glutamate release and glutamate receptors. Biol Psychiatry 2013, 73: 1180–1188

Kadriu B, Musazzi L, Henter ID, Graves M, Popoli M, Zarate CA Jr. Glutamatergic Neurotransmission: Pathway to Developing Novel Rapid-Acting Antidepressant Treatments. Int J Neuropsychopharmacol 2019, 22: 119–135

Beurel E, Grieco SF, Amadei C, Downey K, Jope RS. Ketamine-induced inhibition of glycogen synthase kinase-3 contributes to the augmentation of alpha-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA) receptor signaling. Bipolar Disord 2016, 18: 473–480

Gould TD, O’Donnell KC, Dow ER, Du J, Chen G, Manji HK. Involvement of AMPA receptors in the antidepressant-like effects of lithium in the mouse tail suspension test and forced swim test. Neuropharmacology 2008, 54: 577–587

Duman RS, Sanacora G, Krystal JH. Altered Connectivity in Depression: GABA and Glutamate Neurotransmitter Deficits and Reversal by Novel Treatments. Neuron 2019, 102: 75–90

Ghosal S, Hare B, Duman RS. Prefrontal Cortex GABAergic Deficits and Circuit Dysfunction in the Pathophysiology and Treatment of Chronic Stress and Depression. Curr Opin Behav Sci 2017, 14: 1–8

Fee C, Banasr M, Sibille E. Somatostatin-positive gamma-aminobutyric acid interneuron deficits in depression: Cortical microcircuit and therapeutic perspectives. Biol Psychiatry 2017, 82: 549–559

Schur RR, Draisma LW, Wijnen JP, Boks MP, Koevoets MG, Joels M, et al. Brain GABA levels across psychiatric disorders: A systematic literature review and meta-analysis of (1) H-MRS studies. Hum Brain Mapp 2016, 37: 3337–3352

Guilloux JP, Douillard-Guilloux G, Kota R, Wang X, Gardier AM, Martinowich K, et al. Molecular evidence for BDNF- and GABA-related dysfunctions in the amygdala of female subjects with major depression. Mol Psychiatry 2012, 17: 1130–1142

Karolewicz B, Maciag D, O’Dwyer G, Stockmeier CA, Feyissa AM, Rajkowska G. Reduced level of glutamic acid decarboxylase-67 kDa in the prefrontal cortex in major depression. Int J Neuropsychopharmacol 2010, 13: 411–420

Lener MS, Niciu MJ, Ballard ED, Park M, Park LT, Nugent AC, et al. Glutamate and gamma-aminobutyric acid systems in the pathophysiology of major depression and antidepressant response to ketamine. Biol Psychiatry 2017, 81: 886–897

Chen X, van Gerven J, Cohen A, Jacobs G. Human pharmacology of positive GABA-A subtype-selective receptor modulators for the treatment of anxiety. Acta Pharmacol Sin 2019, 40: 571–582

Ren Z, Pribiag H, Jefferson SJ, Shorey M, Fuchs T, Stellwagen D, et al. Bidirectional homeostatic regulation of a depression-related brain state by gamma-aminobutyric acidergic deficits and ketamine treatment. Biol Psychiatry 2016, 80: 457–468

Kolata SM, Nakao K, Jeevakumar V, Farmer-Alroth EL, Fujita Y, Bartley AF, et al. Neuropsychiatric phenotypes produced by GABA reduction in mouse cortex and hippocampus. Neuropsychopharmacology 2018, 43: 1445–1456

Duman RS, Li N. A neurotrophic hypothesis of depression: role of synaptogenesis in the actions of NMDA receptor antagonists. Philos Trans R Soc Lond B Biol Sci 2012, 367: 2475–2484

Albert PR, Benkelfat C, Descarries L. The neurobiology of depression—revisiting the serotonin hypothesis. I. Cellular and molecular mechanisms. Philos Trans R Soc Lond B Biol Sci 2012, 367: 2378–2381

Li K, Shen S, Ji YT, Li XY, Zhang LS, Wang XD. Melatonin augments the effects of fluoxetine on depression-like behavior and hippocampal BDNF-TrkB signaling. Neurosci Bull 2018, 34: 303–311

Zhang JJ, Gao TT, Wang Y, Wang JL, Guan W, Wang YJ, et al. Andrographolide exerts significant antidepressant-like effects involving the hippocampal BDNF system in mice. Int J Neuropsychopharmacol 2019, 22: 585–600

Wang JQ, Mao L. The ERK pathway: Molecular mechanisms and treatment of depression. Mol Neurobiol 2019, 56: 6197–6205

Chiou YJ, Huang TL. Serum brain-derived neurotrophic factors in taiwanese patients with drug-naive first-episode major depressive disorder: Effects of antidepressants. Int J Neuropsychopharmacol 2017, 20: 213–218

CAS   PubMed   Google Scholar  

Youssef MM, Underwood MD, Huang YY, Hsiung SC, Liu Y, Simpson NR, et al. Association of BDNF Val66Met polymorphism and brain BDNF levels with major depression and suicide. Int J Neuropsychopharmacol 2018, 21: 528–538

Kojima M, Matsui K, Mizui T. BDNF pro-peptide: physiological mechanisms and implications for depression. Cell Tissue Res 2019, 377: 73–79

Jeon SW, Kim YK. Inflammation-induced depression: Its pathophysiology and therapeutic implications. J Neuroimmunol 2017, 313: 92–98

Miller AH, Raison CL. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol 2016, 16: 22–34

Zhao G, Liu X. Neuroimmune advance in depressive disorder. Adv Exp Med Biol 2019, 1180: 85–98

Li Z, Wang Z, Zhang C, Chen J, Su Y, Huang J, et al. Reduced ENA78 levels as novel biomarker for major depressive disorder and venlafaxine efficiency: Result from a prospective longitudinal study. Psychoneuroendocrinology 2017, 81: 113–121

Ambrosio G, Kaufmann FN, Manosso L, Platt N, Ghisleni G, Rodrigues ALS, et al. Depression and peripheral inflammatory profile of patients with obesity. Psychoneuroendocrinology 2018, 91: 132–141

Mao R, Zhang C, Chen J, Zhao G, Zhou R, Wang F, et al. Different levels of pro- and anti-inflammatory cytokines in patients with unipolar and bipolar depression. J Affect Disord 2018, 237: 65–72

Enache D, Pariante CM, Mondelli V. Markers of central inflammation in major depressive disorder: A systematic review and meta-analysis of studies examining cerebrospinal fluid, positron emission tomography and post-mortem brain tissue. Brain Behav Immun 2019, 81: 24–40

Haroon E, Daguanno AW, Woolwine BJ, Goldsmith DR, Baer WM, Wommack EC, et al. Antidepressant treatment resistance is associated with increased inflammatory markers in patients with major depressive disorder. Psychoneuroendocrinology 2018, 95: 43–49

Syed SA, Beurel E, Loewenstein DA, Lowell JA, Craighead WE, Dunlop BW, et al. Defective inflammatory pathways in never-treated depressed patients are associated with poor treatment response. Neuron 2018, 99(914–924): e913

Li Z, Qi D, Chen J, Zhang C, Yi Z, Yuan C, et al. Venlafaxine inhibits the upregulation of plasma tumor necrosis factor-alpha (TNF-alpha) in the Chinese patients with major depressive disorder: a prospective longitudinal study. Psychoneuroendocrinology 2013, 38: 107–114

Kohler CA, Freitas TH, Stubbs B, Maes M, Solmi M, Veronese N, et al. Peripheral alterations in cytokine and chemokine levels after antidepressant drug treatment for major depressive disorder: systematic review and meta-analysis. Mol Neurobiol 2018, 55: 4195–4206

Innes S, Pariante CM, Borsini A. Microglial-driven changes in synaptic plasticity: A possible role in major depressive disorder. Psychoneuroendocrinology 2019, 102: 236–247

Sochocka M, Diniz BS, Leszek J. Inflammatory response in the CNS: Friend or foe? Mol Neurobiol 2017, 54: 8071–8089

Zhang L, Zhang J, You Z. Switching of the microglial activation phenotype is a possible treatment for depression disorder. Front Cell Neurosci 2018, 12: 306

Sandhu KV, Sherwin E, Schellekens H, Stanton C, Dinan TG, Cryan JF. Feeding the microbiota-gut-brain axis: diet, microbiome, and neuropsychiatry. Transl Res 2017, 179: 223–244

Gonzalez-Arancibia C, Urrutia-Pinones J, Illanes-Gonzalez J, Martinez-Pinto J, Sotomayor-Zarate R, Julio-Pieper M, et al. Do your gut microbes affect your brain dopamine? Psychopharmacology (Berl) 2019, 236: 1611–1622

Article   CAS   Google Scholar  

Kennedy PJ, Cryan JF, Dinan TG, Clarke G. Irritable bowel syndrome: a microbiome-gut-brain axis disorder? World J Gastroenterol 2014, 20: 14105–14125

Whitehead WE, Palsson O, Jones KR. Systematic review of the comorbidity of irritable bowel syndrome with other disorders: what are the causes and implications? Gastroenterology 2002, 122: 1140–1156

Ruepert L, Quartero AO, de Wit NJ, van der Heijden GJ, Rubin G, Muris JW. Bulking agents, antispasmodics and antidepressants for the treatment of irritable bowel syndrome. Cochrane Database Syst Rev 2011: CD003460.

Marin IA, Goertz JE, Ren T, Rich SS, Onengut-Gumuscu S, Farber E, et al. Microbiota alteration is associated with the development of stress-induced despair behavior. Sci Rep 2017, 7: 43859

Zheng P, Zeng B, Zhou C, Liu M, Fang Z, Xu X, et al. Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host’s metabolism. Mol Psychiatry 2016, 21: 786–796

Curtis K, Stewart CJ, Robinson M, Molfese DL, Gosnell SN, Kosten TR, et al. Insular resting state functional connectivity is associated with gut microbiota diversity. Eur J Neurosci 2019, 50: 2446–2452

O’Mahony SM, Marchesi JR, Scully P, Codling C, Ceolho AM, Quigley EM, et al. Early life stress alters behavior, immunity, and microbiota in rats: implications for irritable bowel syndrome and psychiatric illnesses. Biol Psychiatry 2009, 65: 263–267

Garcia-Rodenas CL, Bergonzelli GE, Nutten S, Schumann A, Cherbut C, Turini M, et al. Nutritional approach to restore impaired intestinal barrier function and growth after neonatal stress in rats. J Pediatr Gastroenterol Nutr 2006, 43: 16–24

Hao Z, Wang W, Guo R, Liu H. Faecalibacterium prausnitzii (ATCC 27766) has preventive and therapeutic effects on chronic unpredictable mild stress-induced depression-like and anxiety-like behavior in rats. Psychoneuroendocrinology 2019, 104: 132–142

Messaoudi M, Violle N, Bisson JF, Desor D, Javelot H, Rougeot C. Beneficial psychological effects of a probiotic formulation (Lactobacillus helveticus R0052 and Bifidobacterium longum R0175) in healthy human volunteers. Gut Microbes 2011, 2: 256–261

Rudzki L, Ostrowska L, Pawlak D, Malus A, Pawlak K, Waszkiewicz N, et al. Probiotic Lactobacillus Plantarum 299v decreases kynurenine concentration and improves cognitive functions in patients with major depression: A double-blind, randomized, placebo controlled study. Psychoneuroendocrinology 2019, 100: 213–222

Foster JA, McVey Neufeld KA. Gut-brain axis: how the microbiome influences anxiety and depression. Trends Neurosci 2013, 36: 305–312

Mittal R, Debs LH, Patel AP, Nguyen D, Patel K, O’Connor G, et al. Neurotransmitters: The critical modulators regulating gut-brain axis. J Cell Physiol 2017, 232: 2359–2372

Diviccaro S, Giatti S, Borgo F, Barcella M, Borghi E, Trejo JL, et al. Treatment of male rats with finasteride, an inhibitor of 5alpha-reductase enzyme, induces long-lasting effects on depressive-like behavior, hippocampal neurogenesis, neuroinflammation and gut microbiota composition. Psychoneuroendocrinology 2019, 99: 206–215

Kiecolt-Glaser JK, Wilson SJ, Bailey ML, Andridge R, Peng J, Jaremka LM, et al. Marital distress, depression, and a leaky gut: Translocation of bacterial endotoxin as a pathway to inflammation. Psychoneuroendocrinology 2018, 98: 52–60

Lindqvist D, Dhabhar FS, James SJ, Hough CM, Jain FA, Bersani FS, et al. Oxidative stress, inflammation and treatment response in major depression. Psychoneuroendocrinology 2017, 76: 197–205

Czarny P, Wigner P, Galecki P, Sliwinski T. The interplay between inflammation, oxidative stress, DNA damage, DNA repair and mitochondrial dysfunction in depression. Prog Neuropsychopharmacol Biol Psychiatry 2018, 80: 309–321

Xie X, Yu C, Zhou J, Xiao Q, Shen Q, Xiong Z, et al. Nicotinamide mononucleotide ameliorates the depression-like behaviors and is associated with attenuating the disruption of mitochondrial bioenergetics in depressed mice. J Affect Disord 2020, 263: 166–174

Wang XL, Yuan K, Zhang W, Li SX, Gao GF, Lu L. Regulation of circadian genes by the MAPK pathway: Implications for rapid antidepressant action. Neurosci Bull 2020, 36: 66–76

Xie X, Shen Q, Yu C, Xiao Q, Zhou J, Xiong Z, et al. Depression-like behaviors are accompanied by disrupted mitochondrial energy metabolism in chronic corticosterone-induced mice. J Steroid Biochem Mol Biol 2020, 200: 105607

Zhang LF, Shi L, Liu H, Meng FT, Liu YJ, Wu HM, et al. Increased hippocampal tau phosphorylation and axonal mitochondrial transport in a mouse model of chronic stress. Int J Neuropsychopharmacol 2012, 15: 337–348

Flint J, Kendler KS. The genetics of major depression. Neuron 2014, 81: 1214

Sullivan PF, de Geus EJ, Willemsen G, James MR, Smit JH, Zandbelt T, et al. Genome-wide association for major depressive disorder: a possible role for the presynaptic protein piccolo. Mol Psychiatry 2009, 14: 359–375

Dunn EC, Brown RC, Dai Y, Rosand J, Nugent NR, Amstadter AB, et al. Genetic determinants of depression: recent findings and future directions. Harv Rev Psychiatry 2015, 23: 1–18

Major Depressive Disorder Working Group of the Psychiatric GC, Ripke S, Wray NR, Lewis CM, Hamilton SP, Weissman MM, et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry 2013, 18: 497–511.

Levinson DF, Mostafavi S, Milaneschi Y, Rivera M, Ripke S, Wray NR, et al. Genetic studies of major depressive disorder: why are there no genome-wide association study findings and what can we do about it? Biol Psychiatry 2014, 76: 510–512

Sullivan PF, Agrawal A, Bulik CM, Andreassen OA, Borglum AD, Breen G, et al. Psychiatric genomics: An update and an agenda. Am J Psychiatry 2018, 175: 15–27

Schwabe I, Milaneschi Y, Gerring Z, Sullivan PF, Schulte E, Suppli NP, et al. Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies. Psychol Med 2019, 49: 2646–2656

Okbay A, Baselmans BM, De Neve JE, Turley P, Nivard MG, Fontana MA, et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet 2016, 48: 624–633

Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 2018, 50: 668–681

Howard DM, Adams MJ, Clarke TK, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci 2019, 22: 343–352

Ormel J, Hartman CA, Snieder H. The genetics of depression: successful genome-wide association studies introduce new challenges. Transl Psychiatry 2019, 9: 114

Uchida S, Yamagata H, Seki T, Watanabe Y. Epigenetic mechanisms of major depression: Targeting neuronal plasticity. Psychiatry Clin Neurosci 2018, 72: 212–227

Yeshurun S, Hannan AJ. Transgenerational epigenetic influences of paternal environmental exposures on brain function and predisposition to psychiatric disorders. Mol Psychiatry 2019, 24: 536–548

van Eijndhoven P, van Wingen G, Katzenbauer M, Groen W, Tepest R, Fernandez G, et al. Paralimbic cortical thickness in first-episode depression: evidence for trait-related differences in mood regulation. Am J Psychiatry 2013, 170: 1477–1486

Peng W, Chen Z, Yin L, Jia Z, Gong Q. Essential brain structural alterations in major depressive disorder: A voxel-wise meta-analysis on first episode, medication-naive patients. J Affect Disord 2016, 199: 114–123

Geerlings MI, Gerritsen L. Late-life depression, hippocampal volumes, and hypothalamic-pituitary-adrenal axis regulation: A systematic review and meta-analysis. Biol Psychiatry 2017, 82: 339–350

Maller JJ, Broadhouse K, Rush AJ, Gordon E, Koslow S, Grieve SM. Increased hippocampal tail volume predicts depression status and remission to anti-depressant medications in major depression. Mol Psychiatry 2018, 23: 1737–1744

Joshi SH, Espinoza RT, Pirnia T, Shi J, Wang Y, Ayers B, et al. Structural plasticity of the hippocampus and amygdala induced by electroconvulsive therapy in major depression. Biol Psychiatry 2016, 79: 282–292

Gryglewski G, Baldinger-Melich P, Seiger R, Godbersen GM, Michenthaler P, Klobl M, et al. Structural changes in amygdala nuclei, hippocampal subfields and cortical thickness following electroconvulsive therapy in treatment-resistant depression: longitudinal analysis. Br J Psychiatry 2019, 214: 159–167

Boldrini M, Santiago AN, Hen R, Dwork AJ, Rosoklija GB, Tamir H, et al. Hippocampal granule neuron number and dentate gyrus volume in antidepressant-treated and untreated major depression. Neuropsychopharmacology 2013, 38: 1068–1077

Liang S, Wang Q, Kong X, Deng W, Yang X, Li X, et al. White matter abnormalities in major depression biotypes identified by diffusion tensor imaging. Neurosci Bull 2019, 35: 867–876

Chen G, Guo Y, Zhu H, Kuang W, Bi F, Ai H, et al. Intrinsic disruption of white matter microarchitecture in first-episode, drug-naive major depressive disorder: A voxel-based meta-analysis of diffusion tensor imaging. Prog Neuropsychopharmacol Biol Psychiatry 2017, 76: 179–187

Brakowski J, Spinelli S, Dorig N, Bosch OG, Manoliu A, Holtforth MG, et al. Resting state brain network function in major depression - Depression symptomatology, antidepressant treatment effects, future research. J Psychiatr Res 2017, 92: 147–159

Dutta A, McKie S, Downey D, Thomas E, Juhasz G, Arnone D, et al. Regional default mode network connectivity in major depressive disorder: modulation by acute intravenous citalopram. Transl Psychiatry 2019, 9: 116

Meyer BM, Rabl U, Huemer J, Bartova L, Kalcher K, Provenzano J, et al. Prefrontal networks dynamically related to recovery from major depressive disorder: a longitudinal pharmacological fMRI study. Transl Psychiatry 2019, 9: 64

Muller VI, Cieslik EC, Serbanescu I, Laird AR, Fox PT, Eickhoff SB. Altered brain activity in unipolar depression revisited: Meta-analyses of neuroimaging studies. JAMA Psychiatry 2017, 74: 47–55

Connolly CG, Ho TC, Blom EH, LeWinn KZ, Sacchet MD, Tymofiyeva O, et al. Resting-state functional connectivity of the amygdala and longitudinal changes in depression severity in adolescent depression. J Affect Disord 2017, 207: 86–94

Godlewska BR, Browning M, Norbury R, Igoumenou A, Cowen PJ, Harmer CJ. Predicting treatment response in depression: The role of anterior cingulate cortex. Int J Neuropsychopharmacol 2018, 21: 988–996

Goldstein-Piekarski AN, Staveland BR, Ball TM, Yesavage J, Korgaonkar MS, Williams LM. Intrinsic functional connectivity predicts remission on antidepressants: a randomized controlled trial to identify clinically applicable imaging biomarkers. Transl Psychiatry 2018, 8: 57

Leaver AM, Vasavada M, Joshi SH, Wade B, Woods RP, Espinoza R, et al. Mechanisms of antidepressant response to electroconvulsive therapy studied with perfusion magnetic resonance imaging. Biol Psychiatry 2019, 85: 466–476

Miskowiak KW, Macoveanu J, Jorgensen MB, Stottrup MM, Ott CV, Jensen HM, et al. Neural response after a single ECT session during retrieval of emotional self-referent words in depression: A randomized, sham-controlled fMRI study. Int J Neuropsychopharmacol 2018, 21: 226–235

Du L, Liu H, Du W, Chao F, Zhang L, Wang K, et al. Stimulated left DLPFC-nucleus accumbens functional connectivity predicts the anti-depression and anti-anxiety effects of rTMS for depression. Transl Psychiatry 2018, 7: 3

Williams LM. Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: a theoretical review of the evidence and future directions for clinical translation. Depress Anxiety 2017, 34: 9–24

Williams LM. Precision psychiatry: a neural circuit taxonomy for depression and anxiety. Lancet Psychiatry 2016, 3: 472–480

Williams LM, Korgaonkar MS, Song YC, Paton R, Eagles S, Goldstein-Piekarski A, et al. Amygdala reactivity to emotional faces in the prediction of general and medication-specific responses to antidepressant treatment in the randomized iSPOT-D trial. Neuropsychopharmacology 2015, 40: 2398–2408

Hou Z, Gong L, Zhi M, Yin Y, Zhang Y, Xie C, et al. Distinctive pretreatment features of bilateral nucleus accumbens networks predict early response to antidepressants in major depressive disorder. Brain Imaging Behav 2018, 12: 1042–1052

De Crescenzo F, Ciliberto M, Menghini D, Treglia G, Ebmeier KP, Janiri L. Is (18)F-FDG-PET suitable to predict clinical response to the treatment of geriatric depression? A systematic review of PET studies. Aging Ment Health 2017, 21: 889–894

Schmaal L, Pozzi E, T CH, van Velzen LS, Veer IM, Opel N, et al. ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing. Transl Psychiatry 2020, 10: 172.

Schmaal L, Veltman DJ, van Erp TG, Samann PG, Frodl T, Jahanshad N, et al. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group. Mol Psychiatry 2016, 21: 806–812

Schmaal L, Hibar DP, Samann PG, Hall GB, Baune BT, Jahanshad N, et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol Psychiatry 2017, 22: 900–909

Frodl T, Janowitz D, Schmaal L, Tozzi L, Dobrowolny H, Stein DJ, et al. Childhood adversity impacts on brain subcortical structures relevant to depression. J Psychiatr Res 2017, 86: 58–65

Renteria ME, Schmaal L, Hibar DP, Couvy-Duchesne B, Strike LT, Mills NT, et al. Subcortical brain structure and suicidal behaviour in major depressive disorder: a meta-analysis from the ENIGMA-MDD working group. Transl Psychiatry 2017, 7: e1116

Tozzi L, Garczarek L, Janowitz D, Stein DJ, Wittfeld K, Dobrowolny H, et al. Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohort. Psychol Med 2020, 50: 1020–1031

de Kovel CGF, Aftanas L, Aleman A, Alexander-Bloch AF, Baune BT, Brack I, et al. No alterations of brain structural asymmetry in major depressive disorder: An ENIGMA consortium analysis. Am J Psychiatry 2019, 176: 1039–1049

Ho TC, Gutman B, Pozzi E, Grabe HJ, Hosten N, Wittfeld K, et al. Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group. Hum Brain Mapp 2020.

Han LKM, Dinga R, Hahn T, Ching CRK, Eyler LT, Aftanas L, et al. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group. Mol Psychiatry 2020.

van Velzen LS, Kelly S, Isaev D, Aleman A, Aftanas LI, Bauer J, et al. White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group. Mol Psychiatry 2020, 25: 1511–1525

Lv X, Si T, Wang G, Wang H, Liu Q, Hu C, et al. The establishment of the objective diagnostic markers and personalized medical intervention in patients with major depressive disorder: rationale and protocol. BMC Psychiatry 2016, 16: 240

Fabbri C, Hosak L, Mossner R, Giegling I, Mandelli L, Bellivier F, et al. Consensus paper of the WFSBP Task Force on Genetics: Genetics, epigenetics and gene expression markers of major depressive disorder and antidepressant response. World J Biol Psychiatry 2017, 18: 5–28

Li Z, Zhang C, Fan J, Yuan C, Huang J, Chen J, et al. Brain-derived neurotrophic factor levels and bipolar disorder in patients in their first depressive episode: 3-year prospective longitudinal study. Br J Psychiatry 2014, 205: 29–35

Gong Q, He Y. Depression, neuroimaging and connectomics: a selective overview. Biol Psychiatry 2015, 77: 223–235

Papakostas GI, Shelton RC, Kinrys G, Henry ME, Bakow BR, Lipkin SH, et al. Assessment of a multi-assay, serum-based biological diagnostic test for major depressive disorder: a pilot and replication study. Mol Psychiatry 2013, 18: 332–339

Kolovos S, Kleiboer A, Cuijpers P. Effect of psychotherapy for depression on quality of life: meta-analysis. Br J Psychiatry 2016, 209: 460–468

Park LT, Zarate CA Jr. Depression in the primary care setting. N Engl J Med 2019, 380: 559–568

Health N C C F M . Depression: The treatment and management of depression in adults (updated edition) 2010.

Qaseem A, Barry MJ, Kansagara D. Clinical Guidelines Committee of the American College of P. Nonpharmacologic versus pharmacologic treatment of adult patients with major depressive disorder: A clinical practice guideline from the american college of physicians. Ann Intern Med 2016, 164: 350–359

Dodd S, Mitchell PB, Bauer M, Yatham L, Young AH, Kennedy SH, et al. Monitoring for antidepressant-associated adverse events in the treatment of patients with major depressive disorder: An international consensus statement. World J Biol Psychiatry 2018, 19: 330–348

Cristea IA, Naudet F. US Food and Drug Administration approval of esketamine and brexanolone. Lancet Psychiatry 2019, 6: 975–977

Zheng W, Cai DB, Xiang YQ, Zheng W, Jiang WL, Sim K, et al. Adjunctive intranasal esketamine for major depressive disorder: A systematic review of randomized double-blind controlled-placebo studies. J Affect Disord 2020, 265: 63–70

Popova V, Daly EJ, Trivedi M, Cooper K, Lane R, Lim P, et al. Efficacy and safety of flexibly dosed esketamine nasal spray combined with a newly initiated oral antidepressant in treatment-resistant depression: A Randomized Double-Blind Active-Controlled Study. Am J Psychiatry 2019, 176: 428–438

Turner EH. Esketamine for treatment-resistant depression: seven concerns about efficacy and FDA approval. Lancet Psychiatry 2019, 6: 977–979

Murrough JW, Abdallah CG, Mathew SJ. Targeting glutamate signalling in depression: progress and prospects. Nat Rev Drug Discov 2017, 16: 472–486

Gill SP, Kellner CH. Clinical practice recommendations for continuation and maintenance electroconvulsive therapy for depression: Outcomes from a review of the evidence and a consensus workshop held in Australia in May 2017. J ECT 2019, 35: 14–20

McClintock SM, Reti IM, Carpenter LL, McDonald WM, Dubin M, Taylor SF, et al. Consensus recommendations for the clinical application of repetitive transcranial magnetic stimulation (rTMS) in the treatment of depression. J Clin Psychiatry 2018, 79.

Chase HW, Boudewyn MA, Carter CS, Phillips ML. Transcranial direct current stimulation: a roadmap for research, from mechanism of action to clinical implementation. Mol Psychiatry 2020, 25: 397–407

Alexander ML, Alagapan S, Lugo CE, Mellin JM, Lustenberger C, Rubinow DR, et al. Double-blind, randomized pilot clinical trial targeting alpha oscillations with transcranial alternating current stimulation (tACS) for the treatment of major depressive disorder (MDD). Transl Psychiatry 2019, 9: 106

Akhtar H, Bukhari F, Nazir M, Anwar MN, Shahzad A. Therapeutic efficacy of neurostimulation for depression: Techniques, current modalities, and future challenges. Neurosci Bull 2016, 32: 115–126

Zhou C, Zhang H, Qin Y, Tian T, Xu B, Chen J, et al. A systematic review and meta-analysis of deep brain stimulation in treatment-resistant depression. Prog Neuropsychopharmacol Biol Psychiatry 2018, 82: 224–232

Li X, Li X. The antidepressant effect of light therapy from retinal projections. Neurosci Bull 2018, 34: 359–368

Shekelle PG, Cook IA, Miake-Lye IM, Booth MS, Beroes JM, Mak S. Benefits and harms of cranial electrical stimulation for chronic painful conditions, depression, anxiety, and insomnia: A systematic review. Ann Intern Med 2018, 168: 414–421

Kessler RC. The potential of predictive analytics to provide clinical decision support in depression treatment planning. Curr Opin Psychiatry 2018, 31: 32–39

Bradley P, Shiekh M, Mehra V, Vrbicky K, Layle S, Olson MC, et al. Improved efficacy with targeted pharmacogenetic-guided treatment of patients with depression and anxiety: A randomized clinical trial demonstrating clinical utility. J Psychiatr Res 2018, 96: 100–107

Greden JF, Parikh SV, Rothschild AJ, Thase ME, Dunlop BW, DeBattista C, et al. Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: A large, patient- and rater-blinded, randomized, controlled study. J Psychiatr Res 2019, 111: 59–67

Long Z, Du L, Zhao J, Wu S, Zheng Q, Lei X. Prediction on treatment improvement in depression with resting state connectivity: A coordinate-based meta-analysis. J Affect Disord 2020, 276: 62–68

Download references

Acknowledgments

This review was supported by the National Basic Research Development Program of China (2016YFC1307100), the National Natural Science Foundation of China (81930033 and 81771465; 81401127), Shanghai Key Project of Science & Technology (2018SHZDZX05), Shanghai Jiao Tong University Medical Engineering Foundation (YG2016MS48), Shanghai Jiao Tong University School of Medicine (19XJ11006), the Sanming Project of Medicine in Shenzhen Municipality (SZSM201612006), the National Key Technologies R&D Program of China (2012BAI01B04), and the Innovative Research Team of High-level Local Universities in Shanghai.

Author information

Authors and affiliations.

Clinical Research Center and Division of Mood Disorders of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China

Zezhi Li, Jun Chen & Yiru Fang

Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China

Shanghai Institute of Nutrition and Health, Shanghai Information Center for Life Sciences, Chinese Academy of Science, Shanghai, 200031, China

Meihua Ruan

Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, 200031, China

Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 201108, China

Jun Chen & Yiru Fang

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Yiru Fang .

Ethics declarations

Conflicts of interest.

The authors declare no conflicts of interest.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Li, Z., Ruan, M., Chen, J. et al. Major Depressive Disorder: Advances in Neuroscience Research and Translational Applications. Neurosci. Bull. 37 , 863–880 (2021). https://doi.org/10.1007/s12264-021-00638-3

Download citation

Received : 30 May 2020

Accepted : 30 September 2020

Published : 13 February 2021

Issue Date : June 2021

DOI : https://doi.org/10.1007/s12264-021-00638-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Major depressive disorder
  • Pathogenesis
  • Find a journal
  • Publish with us
  • Track your research

Change Password

Your password must have 8 characters or more and contain 3 of the following:.

  • a lower case character, 
  • an upper case character, 
  • a special character 

Password Changed Successfully

Your password has been changed

  • Sign in / Register

Request Username

Can't sign in? Forgot your username?

Enter your email address below and we will send you your username

If the address matches an existing account you will receive an email with instructions to retrieve your username

An Exploratory Study of Students with Depression in Undergraduate Research Experiences

  • Katelyn M. Cooper
  • Logan E. Gin
  • M. Elizabeth Barnes
  • Sara E. Brownell

*Address correspondence to: Katelyn M. Cooper ( E-mail Address: [email protected] ).

Department of Biology, University of Central Florida, Orlando, FL, 32816

Search for more papers by this author

Biology Education Research Lab, Research for Inclusive STEM Education Center, School of Life Sciences, Arizona State University, Tempe, AZ 85281

Depression is a top mental health concern among undergraduates and has been shown to disproportionately affect individuals who are underserved and underrepresented in science. As we aim to create a more inclusive scientific community, we argue that we need to examine the relationship between depression and scientific research. While studies have identified aspects of research that affect graduate student depression, we know of no studies that have explored the relationship between depression and undergraduate research. In this study, we sought to understand how undergraduates’ symptoms of depression affect their research experiences and how research affects undergraduates’ feelings of depression. We interviewed 35 undergraduate researchers majoring in the life sciences from 12 research-intensive public universities across the United States who identify with having depression. Using inductive and deductive coding, we identified that students’ depression affected their motivation and productivity, creativity and risk-taking, engagement and concentration, and self-perception and socializing in undergraduate research experiences. We found that students’ social connections, experiencing failure in research, getting help, receiving feedback, and the demands of research affected students’ depression. Based on this work, we articulate an initial set of evidence-based recommendations for research mentors to consider in promoting an inclusive research experience for students with depression.

INTRODUCTION

Depression is described as a common and serious mood disorder that results in persistent feelings of sadness and hopelessness, as well as a loss of interest in activities that one once enjoyed ( American Psychiatric Association [APA], 2013 ). Additional symptoms of depression include weight changes, difficulty sleeping, loss of energy, difficulty thinking or concentrating, feelings of worthlessness or excessive guilt, and suicidality ( APA, 2013 ). While depression results from a complex interaction of psychological, social, and biological factors ( World Health Organization, 2018 ), studies have shown that increased stress caused by college can be a significant contributor to student depression ( Dyson and Renk, 2006 ).

Depression is one of the top undergraduate mental health concerns, and the rate of depression among undergraduates continues to rise ( Center for Collegiate Mental Health, 2017 ). While we cannot discern whether these increasing rates of depression are due to increased awareness or increased incidence, it is clear that is a serious problem on college campuses. The percent of U.S. college students who self-reported a diagnosis with depression was recently estimated to be about 25% ( American College Health Association, 2019 ). However, higher rates have been reported, with one study estimating that up to 84% of undergraduates experience some level of depression ( Garlow et al. , 2008 ). Depression rates are typically higher among university students compared with the general population, despite being a more socially privileged group ( Ibrahim et al. , 2013 ). Prior studies have found that depression is negatively correlated with overall undergraduate academic performance ( Hysenbegasi et al. , 2005 ; Deroma et al. , 2009 ; American College Health Association, 2019 ). Specifically, diagnosed depression is associated with half a letter grade decrease in students’ grade point average ( Hysenbegasi et al. , 2005 ), and 21.6% of undergraduates reported that depression negatively affected their academic performance within the last year ( American College Health Association, 2019 ). Provided with a list of academic factors that may be affected by depression, students reported that depression contributed to lower exam grades, lower course grades, and not completing or dropping a course.

Students in the natural sciences may be particularly at risk for depression, given that such majors are noted to be particularly stressful due to their competitive nature and course work that is often perceived to “weed students out”( Everson et al. , 1993 ; Strenta et al. , 1994 ; American College Health Association, 2019 ; Seymour and Hunter, 2019 ). Science course instruction has also been described to be boring, repetitive, difficult, and math-intensive; these factors can create an environment that can trigger depression ( Seymour and Hewitt, 1997 ; Osborne and Collins, 2001 ; Armbruster et al ., 2009 ; Ceci and Williams, 2010 ). What also distinguishes science degree programs from other degree programs is that, increasingly, undergraduate research experiences are being proposed as an essential element of a science degree ( American Association for the Advancement of Science, 2011 ; President’s Council of Advisors on Science and Technology, 2012 ; National Academies of Sciences, Engineering, and Medicine [NASEM], 2017 ). However, there is some evidence that undergraduate research experiences can add to the stress of college for some students ( Cooper et al. , 2019c ). Students can garner multiple benefits from undergraduate research, including enhanced abilities to think critically ( Ishiyama, 2002 ; Bauer and Bennett, 2003 ; Brownell et al. , 2015 ), improved student learning ( Rauckhorst et al. , 2001 ; Brownell et al. , 2015 ), and increased student persistence in undergraduate science degree programs ( Jones et al. , 2010 ; Hernandez et al. , 2018 ). Notably, undergraduate research experiences are increasingly becoming a prerequisite for entry into medical and graduate programs in science, particularly elite programs ( Cooper et al. , 2019d ). Although some research experiences are embedded into formal lab courses as course-based undergraduate research experiences (CUREs; Auchincloss et al. , 2014 ; Brownell and Kloser, 2015 ), the majority likely entail working with faculty in their research labs. These undergraduate research experiences in faculty labs are often added on top of a student’s normal course work, so they essentially become an extracurricular activity that they have to juggle with course work, working, and/or personal obligations ( Cooper et al. , 2019c ). While the majority of the literature surrounding undergraduate research highlights undergraduate research as a positive experience ( NASEM, 2017 ), studies have demonstrated that undergraduate research experiences can be academically and emotionally challenging for students ( Mabrouk and Peters, 2000 ; Seymour et al. , 2004 ; Cooper et al. , 2019c ; Limeri et al. , 2019 ). In fact, 50% of students sampled nationally from public R1 institutions consider leaving their undergraduate research experience prematurely, and about half of those students, or 25% of all students, ultimately leave their undergraduate research experience ( Cooper et al. , 2019c ). Notably, 33.8% of these individuals cited a negative lab environment and 33.3% cited negative relationships with their mentors as factors that influenced their decision about whether to leave ( Cooper et al. , 2019c ). Therefore, students’ depression may be exacerbated in challenging undergraduate research experiences, because studies have shown that depression is positively correlated with student stress ( Hish et al. , 2019 ).

While depression has not been explored in the context of undergraduate research experiences, depression has become a prominent concern surrounding graduate students conducting scientific research. A recent study that examined the “graduate student mental health crisis” ( Flaherty, 2018 ) found that work–life balance and graduate students’ relationships with their research advisors may be contributing to their depression ( Evans et al. , 2018 ). Specifically, this survey of 2279 PhD and master’s students from diverse fields of study, including the biological/physical sciences, showed that 39% of graduate students have experienced moderate to severe depression. Fifty-five percent of the graduate students with depression who were surveyed disagreed with the statement “I have good work life balance,” compared to only 21% of students with depression who agreed. Additionally, the study highlighted that more students with depression disagreed than agreed with the following statements: their advisors provided “real” mentorship, their advisors provided ample support, their advisors positively impacted their emotional or mental well-being, their advisors were assets to their careers, and they felt valued by their mentors. Another recent study identified that depression severity in biomedical doctoral students was significantly associated with graduate program climate, a perceived lack of employment opportunities, and the quality of students’ research training environment ( Nagy et al. , 2019 ). Environmental stress, academic stress, and family and monetary stress have also been shown to be predictive of depression severity in biomedical doctoral students ( Hish et al. , 2019 ). Further, one study found that self-esteem is negatively correlated and stress is positively correlated with graduate student depression; presumably research environments that challenge students’ self-esteem and induce stress are likely contributing to depressive symptoms among graduate students ( Kreger, 1995 ). While these studies have focused on graduate students, and there are certainly notable distinctions between graduate and undergraduate research, the research-related factors that affect graduate student depression, including work–life balance, relationships with mentors, research environment, stress, and self-esteem, may also be relevant to depression among undergraduates conducting research. Importantly, undergraduates in the United States have reported identical levels of depression as graduate students but are often less likely to seek mental health care services ( Wyatt and Oswalt, 2013 ), which is concerning if undergraduate research experiences exacerbate depression.

Based on the literature on the stressors of undergraduate research experiences and the literature identifying some potential causes of graduate student depression, we identified three aspects of undergraduate research that may exacerbate undergraduates’ depression. Mentoring: Mentors can be an integral part of a students’ research experience, bolstering their connections with others in the science community, scholarly productivity, and science identity, as well as providing many other benefits ( Thiry and Laursen, 2011 ; Prunuske et al. , 2013 ; Byars-Winston et al. , 2015 ; Aikens et al. , 2016 , 2017 ; Thompson et al. , 2016 ; Estrada et al. , 2018 ). However, recent literature has highlighted that poor mentoring can negatively affect undergraduate researchers ( Cooper et al. , 2019c ; Limeri et al. , 2019 ). Specifically, one study of 33 undergraduate researchers who had conducted research at 10 institutions identified seven major ways that they experienced negative mentoring, which included absenteeism, abuse of power, interpersonal mismatch, lack of career support, lack of psychosocial support, misaligned expectations, and unequal treatment ( Limeri et al. , 2019 ). We hypothesize negative mentoring experiences may be particularly harmful for students with depression, because support, particularly social support, has been shown to be important for helping individuals with depression cope with difficult circumstances ( Aneshensel and Stone, 1982 ; Grav et al. , 2012 ). Failure: Experiencing failure has been hypothesized to be an important aspect of undergraduate research experiences that may help students develop some the most distinguishing abilities of outstanding scientists, such as coping with failure, navigating challenges, and persevering ( Laursen et al. , 2010 ; Gin et al. , 2018 ; Henry et al. , 2019 ). However, experiencing failure and the stress and fatigue that often accompany it may be particularly tough for students with depression ( Aldwin and Greenberger, 1987 ; Mongrain and Blackburn, 2005 ). Lab environment: Fairness, inclusion/exclusion, and social support within one’s organizational environment have been shown to be key factors that cause people to either want to remain in the work place and be productive or to want to leave ( Barak et al. , 2006 ; Cooper et al. , 2019c ). We hypothesize that dealing with exclusion or a lack of social support may exacerbate depression for some students; patients with clinical depression react to social exclusion with more pronounced negative emotions than do individuals without clinical depression ( Jobst et al. , 2015 ). While there are likely other aspects of undergraduate research that affect student depression, we hypothesize that these factors have the potential to exacerbate negative research experiences for students with depression.

Depression has been shown to disproportionately affect many populations that are underrepresented or underserved within the scientific community, including females ( American College Health Association, 2018 ; Evans et al. , 2018 ), first-generation college students ( Jenkins et al. , 2013 ), individuals from low socioeconomic backgrounds ( Eisenberg et al. , 2007 ), members of the LGBTQ+ community ( Eisenberg et al. , 2007 ; Evans et al. , 2018 ), and people with disabilities ( Turner and Noh, 1988 ). Therefore, as the science community strives to be more diverse and inclusive ( Intemann, 2009 ), it is important that we understand more about the relationship between depression and scientific research, because negative experiences with depression in scientific research may be contributing to the underrepresentation of these groups. Specifically, more information is needed about how the research process and environment of research experiences may affect depression.

Given the high rate of depression among undergraduates, the links between depression and graduate research, the potentially challenging environment of undergraduate research, and how depression could disproportionately impact students from underserved communities, it is imperative to begin to explore the relationship between scientific research and depression among undergraduates to create research experiences that could maximize student success. In this exploratory interview study, we aimed to 1) describe how undergraduates’ symptoms of depression affect their research experiences, 2) understand how undergraduate research affects students’ feelings of depression, and 3) identify recommendations based on the literature and undergraduates’ reported experiences to promote a positive research experience for students with depression.

This study was done with an approved Arizona State University Institutional Review Board protocol #7247.

In Fall 2018, we surveyed undergraduate researchers majoring in the life sciences across 25 research-intensive (R1) public institutions across the United States (specific details about the recruitment of the students who completed the survey can be found in Cooper et al. (2019c) ). The survey asked students for their opinions about their undergraduate research experiences and their demographic information and whether they would be interested in participating in a follow-up interview related to their research experiences. For the purpose of this study, we exclusively interviewed students about their undergraduate research experiences in faculty member labs; we did not consider students’ experiences in CUREs. Of the 768 undergraduate researchers who completed the survey, 65% ( n = 496) indicated that they would be interested in participating in a follow-up interview. In Spring 2019, we emailed the 496 students, explaining that we were interested in interviewing students with depression about their experiences in undergraduate research. Our specific prompt was: “If you identify as having depression, we would be interested in hearing about your experience in undergraduate research in a 30–60 minute online interview.” We did not define depression in our email recruitment because we conducted think-aloud interviews with four undergraduates who all correctly interpreted what we meant by depression ( APA, 2013 ). We had 35 students agree to participate in the interview study. The interview participants represented 12 of the 25 R1 public institutions that were represented in the initial survey.

Student Interviews

We developed an interview script to explore our research questions. Specifically, we were interested in how students’ symptoms of depression affect their research experiences, how undergraduate research negatively affects student depression, and how undergraduate research positively affects student depression.

We recognized that mental health, and specifically depression, can be a sensitive topic to discuss with undergraduates, and therefore we tried to minimize any discomfort that the interviewees might experience during the interview. Specifically, we conducted think-aloud interviews with three graduate students who self-identified with having depression at the time of the interview. We asked them to note whether any interview questions made them uncomfortable. We also sought their feedback on questions given their experiences as persons with depression who had once engaged in undergraduate research. We revised the interview protocol after each think-aloud interview. Next, we conducted four additional think-aloud interviews with undergraduates conducting basic science or biology education research who identified with having depression to establish cognitive validity of the questions and to elicit additional feedback about any questions that might make someone uncomfortable. The questions were revised after each think-aloud interview until no question was unclear or misinterpreted by the students and we were confident that the questions minimized students’ potential discomfort ( Trenor et al. , 2011 ). A copy of the final interview script can be found in the Supplemental Material.

All interviews were individually conducted by one of two researchers (K.M.C. and L.E.G.) who conducted the think-aloud interviews together to ensure that their interviewing practices were as similar as possible. The interviews were approximately an hour long, and students received a $15 gift card for their participation.

Personal, Research, and Depression Demographics

All student demographics and information about students’ research experiences were collected using the survey distributed to students in Fall 2018. We collected personal demographics, including the participants’ gender, race/ethnicity, college generation status, transfer status, financial stability, year in college, major, and age. We also collected information about the students’ research experiences, including the length of their first research experiences, the average number of hours they spend in research per week, how they were compensated for research, who their primary mentors were, and the focus areas of their research.

In the United States, mental healthcare is disproportionately unavailable to Black and Latinx individuals, as well as those who come from low socioeconomic backgrounds ( Kataoka et al. , 2002 ; Howell and McFeeters, 2008 ; Santiago et al. , 2013 ). Therefore, to minimize a biased sample, we invited anyone who identified with having depression to participate in our study; we did not require students to be diagnosed with depression or to be treated for depression in order to participate. However, we did collect information about whether students had been formally diagnosed with depression and whether they had been treated for depression. After the interview, all participants were sent a link to a short survey that asked them if they had ever been diagnosed with depression and how, if at all, they had ever been treated for depression. A copy of these survey questions can be found in the Supplemental Material. The combined demographic information of the participants is in Table 1 . The demographics for each individual student can be found in the Supplemental Material.

a Students reported the time they had spent in research 6 months before being interviewed and only reported on the length of time of their first research experiences.

b Students were invited to report multiple ways in which they were treated for their depression; other treatments included lifestyle changes and meditation.

c Students were invited to report multiple means of compensation for their research if they had been compensated for their time in different ways.

d Students were asked whether they felt financially stable, particularly during the undergraduate research experience.

e Students reported who they work/worked with most closely during their research experiences.

f Staff members included lab coordinators or lab managers.

g Other focus areas of research included sociology, linguistics, psychology, and public health.

Interview Analysis

The initial interview analysis aimed to explore each idea that a participant expressed ( Charmaz, 2006 ) and to identify reoccurring ideas throughout the interviews. First, three authors (K.M.C., L.E.G., and S.E.B.) individually reviewed a different set of 10 interviews and took detailed analytic notes ( Birks and Mills, 2015 ). Afterward, the authors compared their notes and identified reoccurring themes throughout the interviews using open coding methods ( Saldaña, 2015 ).

Once an initial set of themes was established, two researchers (K.M.C. and L.E.G.) individually reviewed the same set of 15 randomly selected interviews to validate the themes identified in the initial analysis and to screen for any additional themes that the initial analysis may have missed. Each researcher took detailed analytic notes throughout the review of an interview, which they discussed after reviewing each interview. The researchers compared what quotes from each interview they categorized into each theme. Using constant comparison methods, they assigned quotes to each theme and constantly compared the quotes to ensure that each quote fit within the description of the theme ( Glesne and Peshkin, 1992 ). In cases in which quotes were too different from other quotes, a new theme was created. This approach allowed for multiple revisions of the themes and allowed the authors to define a final set of codes; the researchers created a final codebook with refined definitions of emergent themes (the final coding rubric can be found in the Supplemental Material). Once the final codebook was established, the researchers (K.M.C. and L.E.G.) individually coded seven additional interviews (20% of all interviews) using the coding rubric. The researchers compared their codes, and their Cohen’s κ interrater score for these seven interviews was at an acceptable level (κ  =  0.88; Landis and Koch, 1977 ). One researcher (L.E.G.) coded the remaining 28 out of 35 interviews. The researchers determined that data saturation had been reached with the current sample and no further recruitment was needed ( Guest et al. , 2006 ). We report on themes that were mentioned by at least 20% of students in the interview study. In the Supplemental Material, we provide the final coding rubric with the number of participants whose interview reflected each theme ( Hannah and Lautsch, 2011 ). Reporting the number of individuals who reported themes within qualitative data can lead to inaccurate conclusions about the generalizability of the results to a broader population. These qualitative data are meant to characterize a landscape of experiences that students with depression have in undergraduate research rather than to make claims about the prevalence of these experiences ( Glesne and Peshkin, 1992 ). Because inferences about the importance of these themes cannot be drawn from these counts, they are not included in the results of the paper ( Maxwell, 2010 ). Further, the limited number of interviewees made it not possible to examine whether there were trends based on students’ demographics or characteristics of their research experiences (e.g., their specific area of study). Quotes were lightly edited for clarity by inserting clarification brackets and using ellipses to indicate excluded text. Pseudonyms were given to all students to protect their privacy.

The Effect of Depressive Symptoms on Undergraduate Research

We asked students to describe the symptoms associated with their depression. Students described experiencing anxiety that is associated with their depression; this could be anxiety that precedes their depression or anxiety that results from a depressive episode or a period of time when an individual has depression symptoms. Further, students described difficulty getting out of bed or leaving the house, feeling tired, a lack of motivation, being overly self-critical, feeling apathetic, and having difficulty concentrating. We were particularly interested in how students’ symptoms of depression affected their experiences in undergraduate research. During the think-aloud interviews that were conducted before the interview study, graduate and undergraduate students consistently described that their depression affected their motivation in research, their creativity in research, and their productivity in research. Therefore, we explicitly asked undergraduate researchers how, if at all, their depression affected these three factors. We also asked students to describe any additional ways in which their depression affected their research experiences. Undergraduate researchers commonly described five additional ways in which their depression affected their research; for a detailed description of each way students’ research was affected and for example quotes, see Table 2 . Students described that their depression negatively affected their productivity in the lab. Commonly, students described that their productivity was directly affected by a lack of motivation or because they felt less creative, which hindered the research process. Additionally, students highlighted that they were sometimes less productive because their depression sometimes caused them to struggle to engage intellectually with their research or caused them to have difficulty remembering or concentrating; students described that they could do mundane or routine tasks when they felt depressed, but that they had difficulty with more complex and intellectually demanding tasks. However, students sometimes described that even mundane tasks could be difficult when they were required to remember specific steps; for example, some students struggled recalling a protocol from memory when their depression was particularly severe. Additionally, students noted that their depression made them more self-conscious, which sometimes held them back from sharing research ideas with their mentors or from taking risks such as applying to competitive programs. In addition to being self-conscious, students highlighted that their depression caused them to be overly self-critical, and some described experiencing imposter phenomenon ( Clance and Imes, 1978 ) or feeling like they were not talented enough to be in research and were accepted into a lab by a fluke or through luck. Finally, students described that depression often made them feel less social, and they struggled to socially engage with other members of the lab when they were feeling down.

The Effect of Undergraduate Research Experiences on Student Depression

We also wanted to explore how research impacted students’ feelings of depression. Undergraduates described how research both positively and negatively affected their depression. In the following sections, we present aspects of undergraduate research and examine how each positively and/or negatively affected students’ depression using embedded student quotes to highlight the relationships between related ideas.

Lab Environment: Relationships with Others in the Lab.

Some aspects of the lab environment, which we define as students’ physical, social, or psychological research space, could be particularly beneficial for students with depression.

Specifically, undergraduate researchers perceived that comfortable and positive social interactions with others in the lab helped their depression. Students acknowledged how beneficial their relationships with graduate students and postdocs could be.

Marta: “I think always checking in on undergrads is important. It’s really easy [for us] to go a whole day without talking to anybody in the lab. But our grad students are like ‘Hey, what’s up? How’s school? What’s going on?’ (…) What helps me the most is having that strong support system. Sometimes just talking makes you feel better, but also having people that believe in you can really help you get out of that negative spiral. I think that can really help with depression.”

Kelley: “I know that anytime I need to talk to [my postdoc mentors] about something they’re always there for me. Over time we’ve developed a relationship where I know that outside of work and outside of the lab if I did want to talk to them about something I could talk to them. Even just talking to someone about hobbies and having that relationship alone is really helpful [for depression].”

In addition to highlighting the importance of developing relationships with graduate students or postdocs in the lab, students described that forming relationships with other undergraduates in the lab also helped their depression. Particularly, students described that other undergraduate researchers often validated their feelings about research, which in turn helped them realize that what they are thinking or feeling is normal, which tended to alleviate their negative thoughts. Interestingly, other undergraduates experiencing the same issues could sometimes help buffer them from perceiving that a mentor did not like them or that they were uniquely bad at research. In this article, we use the term “mentor” to refer to anyone who students referred to in the interviews as being their mentors or managing their research experiences; this includes graduate students, postdoctoral scholars, lab managers, and primary investigators (PIs).

Abby: “One of my best friends is in the lab with me.  A lot of that friendship just comes from complaining about our stress with the lab and our annoyance with people in the lab. Like when we both agree like, ‘Yeah, the grad students were really off today, it wasn’t us,’ that helps. ‘It wasn’t me, it wasn’t my fault that we were having a rough day in lab; it was the grad students.’ Just being able to realize, ‘Hey, this isn’t all caused by us,’ you know? (…) We understand the stresses in the lab. We understand the details of what each other are doing in the lab, so when something doesn’t work out, we understand that it took them like eight hours to do that and it didn’t work. We provide empathy on a different level.”

Meleana: “It’s great to have solidarity in being confused about something, and it’s just that is a form of validation for me too. When we leave a lab meeting and I look at [another undergrad] I’m like, ‘Did you understand anything that they were just saying?’ And they’re like, ‘Oh, no.’ (…) It’s just really validating to hear from the other undergrads that we all seem to be struggling with the same things.”

Developing positive relationships with faculty mentors or PIs also helped alleviate some students’ depressive feelings, particularly when PIs shared their own struggles with students. This also seemed to normalize students’ concerns about their own experiences.

Alexandra: “[Talking with my PI] is helpful because he would talk about his struggles, and what he faced. A lot of it was very similar to my struggles.  For example, he would say, ‘Oh, yeah, I failed this exam that I studied so hard for. I failed the GRE and I paid so much money to prepare for it.’ It just makes [my depression] better, like okay, this is normal for students to go through this. It’s not an out of this world thing where if you fail, you’re a failure and you can’t move on from it.”

Students’ relationships with others in the lab did not always positively impact their depression. Students described instances when the negative moods of the graduate students and PIs would often set the tone of the lab, which in turn worsened the mood of the undergraduate researchers.

Abby: “Sometimes [the grad students] are not in a good mood. The entire vibe of the lab is just off, and if you make a joke and it hits somebody wrong, they get all mad. It really depends on the grad students and the leadership and the mood that they’re in.”

Interviewer: “How does it affect your depression when the grad students are in a bad mood?”

Abby: “It definitely makes me feel worse. It feels like, again, that I really shouldn’t go ask them for help because they’re just not in the mood to help out. It makes me have more pressure on myself, and I have deadlines I need to meet, but I have a question for them, but they’re in a bad mood so I can’t ask. That’s another day wasted for me and it just puts more stress, which just adds to the depression.”

Additionally, some students described even more concerning behavior from research mentors, which negatively affected their depression.

Julie: “I had a primary investigator who is notorious in the department for screaming at people, being emotionally abusive, unreasonable, et cetera. (…) [He was] kind of harassing people, demeaning them, lying to them, et cetera, et cetera. (…) Being yelled at and constantly demeaned and harassed at all hours of the day and night, that was probably pretty bad for me.”

While the relationships between undergraduates and graduate, postdoc, and faculty mentors seemed to either alleviate or worsen students’ depressive symptoms, depending on the quality of the relationship, students in this study exclusively described their relationships with other undergraduates as positive for their depression. However, students did note that undergraduate research puts some of the best and brightest undergraduates in the same environment, which can result in students comparing themselves with their peers. Students described that this comparison would often lead them to feel badly about themselves, even though they would describe their personal relationship with a person to be good.

Meleana: “In just the research field in general, just feeling like I don’t really measure up to the people around me [can affect my depression]. A lot of the times it’s the beginning of a little spiral, mental spiral. There are some past undergrads that are talked about as they’re on this pedestal of being the ideal undergrads and that they were just so smart and contributed so much to the lab. I can never stop myself from wondering like, ‘Oh, I wonder if I’m having a contribution to the lab that’s similar or if I’m just another one of the undergrads that does the bare minimum and passes through and is just there.’”

Natasha: “But, on the other hand, [having another undergrad in the lab] also reminded me constantly that some people are invested in this and meant to do this and it’s not me. And that some people know a lot more than I do and will go further in this than I will.”

While students primarily expressed that their relationships with others in the lab affected their depression, some students explained that they struggled most with depression when the lab was empty; they described that they did not like being alone in the lab, because a lack of stimulation allowed their minds to be filled with negative thoughts.

Mia: “Those late nights definitely didn’t help [my depression]. I am alone, in the entire building.  I’m left alone to think about my thoughts more, so not distracted by talking to people or interacting with people. I think more about how I’m feeling and the lack of progress I’m making, and the hopelessness I’m feeling. That kind of dragged things on, and I guess deepened my depression.”

Freddy: “Often times when I go to my office in the evening, that is when I would [ sic ] be prone to be more depressed. It’s being alone. I think about myself or mistakes or trying to correct mistakes or whatever’s going on in my life at the time. I become very introspective. I think I’m way too self-evaluating, way too self-deprecating and it’s when I’m alone when those things are really, really triggered. When I’m talking with somebody else, I forget about those things.”

In sum, students with depression highlighted that a lab environment full of positive and encouraging individuals was helpful for their depression, whereas isolating or competitive environments and negative interactions with others often resulted in more depressive feelings.

Doing Science: Experiencing Failure in Research, Getting Help, Receiving Feedback, Time Demands, and Important Contributions.

In addition to the lab environment, students also described that the process of doing science could affect their depression. Specifically, students explained that a large contributor to their depression was experiencing failure in research.

Interviewer: “Considering your experience in undergraduate research, what tends to trigger your feelings of depression?”

Heather: “Probably just not getting things right. Having to do an experiment over and over again. You don’t get the results you want. (…) The work is pretty meticulous and it’s frustrating when I do all this work, I do a whole experiment, and then I don’t get any results that I can use. That can be really frustrating. It adds to the stress. (…) It’s hard because you did all this other stuff before so you can plan for the research, and then something happens and all the stuff you did was worthless basically.”

Julie: “I felt very negatively about myself [when a project failed] and pretty panicked whenever something didn’t work because I felt like it was a direct reflection on my effort and/or intelligence, and then it was a big glaring personal failure.”

Students explained that their depression related to failing in research was exacerbated if they felt as though they could not seek help from their research mentors. Perceived insufficient mentor guidance has been shown to be a factor influencing student intention to leave undergraduate research ( Cooper et al. , 2019c ). Sometimes students talked about their research mentors being unavailable or unapproachable.

Michelle: “It just feels like [the graduate students] are not approachable. I feel like I can’t approach them to ask for their understanding in a certain situation. It makes [my depression] worse because I feel like I’m stuck, and that I’m being limited, and like there’s nothing I can do. So then I kind of feel like it’s my fault that I can’t do anything.”

Other times, students described that they did not seek help in fear that they would be negatively evaluated in research, which is a fear of being judged by others ( Watson and Friend, 1969 ; Weeks et al. , 2005 ; Cooper et al. , 2018 ). That is, students fear that their mentor would think negatively about them or judge them if they were to ask questions that their mentor thought they should know the answer to.

Meleana: “I would say [my depression] tends to come out more in being more reserved in asking questions because I think that comes more like a fear-based thing where I’m like, ‘Oh, I don’t feel like I’m good enough and so I don’t want to ask these questions because then my mentors will, I don’t know, think that I’m dumb or something.’”

Conversely, students described that mentors who were willing to help them alleviated their depressive feelings.

Crystal: “Yeah [my grad student] is always like, ‘Hey, I can check in on things in the lab because you’re allowed to ask me for that, you’re not totally alone in this,’ because he knows that I tend to take on all this responsibility and I don’t always know how to ask for help. He’s like, ‘You know, this is my lab too and I am here to help you as well,’ and just reminds me that I’m not shouldering this burden by myself.”

Ashlyn: “The graduate student who I work with is very kind and has a lot of patience and he really understands a lot of things and provides simple explanations. He does remind me about things and he will keep on me about certain tasks that I need to do in an understanding way, and it’s just because he’s patient and he listens.”

In addition to experiencing failure in science, students described that making mistakes when doing science also negatively affected their depression.

Abby: “I guess not making mistakes on experiments [is important in avoiding my depression]. Not necessarily that your experiment didn’t turn out to produce the data that you wanted, but just adding the wrong enzyme or messing something up like that. It’s like, ‘Oh, man,’ you know? You can get really down on yourself about that because it can be embarrassing.”

Commonly, students described that the potential for making mistakes increased their stress and anxiety regarding research; however, they explained that how other people responded to a potential mistake was what ultimately affected their depression.

Briana: “Sometimes if I made a mistake in correctly identifying an eye color [of a fly], [my PI] would just ridicule me in front of the other students. He corrected me but his method of correcting was very discouraging because it was a ridicule. It made the others laugh and I didn’t like that.”

Julie: “[My PI] explicitly [asked] if I had the dedication for science. A lot of times he said I had terrible judgment. A lot of times he said I couldn’t be trusted. Once I went to a conference with him, and, unfortunately, in front of another professor, he called me a klutz several times and there was another comment about how I never learn from my mistakes.”

When students did do things correctly, they described how important it could be for them to receive praise from their mentors. They explained that hearing praise and validation can be particularly helpful for students with depression, because their thoughts are often very negative and/or because they have low self-esteem.

Crystal: “[Something that helps my depression is] I have text messages from [my graduate student mentor] thanking me [and another undergraduate researcher] for all of the work that we’ve put in, that he would not be able to be as on track to finish as he is if he didn’t have our help.”

Interviewer: “Why is hearing praise from your mentor helpful?”

Crystal: “Because a lot of my depression focuses on everybody secretly hates you, nobody likes you, you’re going to die alone. So having that validation [from my graduate mentor] is important, because it flies in the face of what my depression tells me.”

Brian: “It reminds you that you exist outside of this negative world that you’ve created for yourself, and people don’t see you how you see yourself sometimes.”

Students also highlighted how research could be overwhelming, which negatively affected their depression. Particularly, students described that research demanded a lot of their time and that their mentors did not always seem to be aware that they were juggling school and other commitments in addition to their research. This stress exacerbated their depression.

Rose: “I feel like sometimes [my grad mentors] are not very understanding because grad students don’t take as many classes as [undergrads] do. I think sometimes they don’t understand when I say I can’t come in at all this week because I have finals and they’re like, ‘Why though?’”

Abby: “I just think being more understanding of student life would be great. We have classes as well as the lab, and classes are the priority. They forget what it’s like to be a student. You feel like they don’t understand and they could never understand when you say like, ‘I have three exams this week,’ and they’re like, ‘I don’t care. You need to finish this.’”

Conversely, some students reported that their research labs were very understanding of students’ schedules. Interestingly, these students talked most about how helpful it was to be able to take a mental health day and not do research on days when they felt down or depressed.

Marta: “My lab tech is very open, so she’ll tell us, ‘I can’t come in today. I have to take a mental health day.’ So she’s a really big advocate for that. And I think I won’t personally tell her that I’m taking a mental health day, but I’ll say, ‘I can’t come in today, but I’ll come in Friday and do those extra hours.’ And she’s like, ‘OK great, I’ll see you then.’  And it makes me feel good, because it helps me take care of myself first and then I can take care of everything else I need to do, which is amazing.”

Meleana: “Knowing that [my mentors] would be flexible if I told them that I’m crazy busy and can’t come into work nearly as much this week [helps my depression]. There is flexibility in allowing me to then care for myself.”

Interviewer: “Why is the flexibility helpful given the depression?”

Meleana: “Because sometimes for me things just take a little bit longer when I’m feeling down. I’m just less efficient to be honest, and so it’s helpful if I feel like I can only go into work for 10 hours in a week. It declutters my brain a little bit to not have to worry about all the things I have to do in work in addition the things that I need to do for school or clubs, or family or whatever.”

Despite the demanding nature of research, a subset of students highlighted that their research and research lab provided a sense of stability or familiarity that distracted them from their depression.

Freddy: “I’ll [do research] to run away from those [depressive] feelings or whatever. (…) I find sadly, I hate to admit it, but I do kind of run to [my lab]. I throw myself into work to distract myself from the feelings of depression and sadness.”

Rose: “When you’re sad or when you’re stressed you want to go to things you’re familiar with. So because lab has always been in my life, it’s this thing where it’s going to be there for me I guess. It’s like a good book that you always go back to and it’s familiar and it makes you feel good. So that’s how lab is. It’s not like the greatest thing in the world but it’s something that I’m used to, which is what I feel like a lot of people need when they’re sad and life is not going well.”

Many students also explained that research positively affects their depression because they perceive their research contribution to be important.

Ashlyn: “I feel like I’m dedicating myself to something that’s worthy and something that I believe in. It’s really important because it contextualizes those times when I am feeling depressed. It’s like, no, I do have these better things that I’m working on. Even when I don’t like myself and I don’t like who I am, which is again, depression brain, I can at least say, ‘Well, I have all these other people relying on me in research and in this area and that’s super important.’”

Jessica: “I mean, it just felt like the work that I was doing had meaning and when I feel like what I’m doing is actually going to contribute to the world, that usually really helps with [depression] because it’s like not every day you can feel like you’re doing something impactful.”

In sum, students highlighted that experiencing failure in research and making mistakes negatively contributed to depression, especially when help was unavailable or research mentors had a negative reaction. Additionally, students acknowledged that the research could be time-consuming, but that research mentors who were flexible helped assuage depressive feelings that were associated with feeling overwhelmed. Finally, research helped some students’ depression, because it felt familiar, provided a distraction from depression, and reminded students that they were contributing to a greater cause.

We believe that creating more inclusive research environments for students with depression is an important step toward broadening participation in science, not only to ensure that we are not discouraging students with depression from persisting in science, but also because depression has been shown to disproportionately affect underserved and underrepresented groups in science ( Turner and Noh, 1988 ; Eisenberg et al. , 2007 ; Jenkins et al. , 2013 ; American College Health Association, 2018 ). We initially hypothesized that three features of undergraduate research—research mentors, the lab environment, and failure—may have the potential to exacerbate student depression. We found this to be true; students highlighted that their relationships with their mentors as well as the overall lab environment could negatively affect their depression, but could also positively affect their research experiences. Students also noted that they struggled with failure, which is likely true of most students, but is known to be particularly difficult for students with depression ( Elliott et al. , 1997 ). We expand upon our findings by integrating literature on depression with the information that students provided in the interviews about how research mentors can best support students. We provide a set of evidence-based recommendations focused on mentoring, the lab environment, and failure for research mentors wanting to create more inclusive research environments for students with depression. Notably, only the first recommendation is specific to students with depression; the others reflect recommendations that have previously been described as “best practices” for research mentors ( NASEM, 2017 , 2019 ; Sorkness et al. , 2017 ) and likely would benefit most students. However, we examine how these recommendations may be particularly important for students with depression. As we hypothesized, these recommendations directly address three aspects of research: mentors, lab environment, and failure. A caveat of these recommendations is that more research needs to be done to explore the experiences of students with depression and how these practices actually impact students with depression, but our national sample of undergraduate researchers with depression can provide an initial starting point for a discussion about how to improve research experiences for these students.

Recommendations to Make Undergraduate Research Experiences More Inclusive for Students with Depression

Recognize student depression as a valid illness..

Allow students with depression to take time off of research by simply saying that they are sick and provide appropriate time for students to recover from depressive episodes. Also, make an effort to destigmatize mental health issues.

Undergraduate researchers described both psychological and physical symptoms that manifested as a result of their depression and highlighted how such symptoms prevented them from performing to their full potential in undergraduate research. For example, students described how their depression would cause them to feel unmotivated, which would often negatively affect their research productivity. In cases in which students were motivated enough to come in and do their research, they described having difficulty concentrating or engaging in the work. Further, when doing research, students felt less creative and less willing to take risks, which may alter the quality of their work. Students also sometimes struggled to socialize in the lab. They described feeling less social and feeling overly self-critical. In sum, students described that, when they experienced a depressive episode, they were not able to perform to the best of their ability, and it sometimes took a toll on them to try to act like nothing was wrong, when they were internally struggling with depression. We recommend that research mentors treat depression like any other physical illness; allowing students the chance to recover when they are experiencing a depressive episode can be extremely important to students and can allow them to maximize their productivity upon returning to research ( Judd et al. , 2000 ). Students explained that if they are not able to take the time to focus on recovering during a depressive episode, then they typically continue to struggle with depression, which negatively affects their research. This sentiment is echoed by researchers in psychiatry who have found that patients who do not fully recover from a depressive episode are more likely to relapse and to experience chronic depression ( Judd et al. , 2000 ). Students described not doing tasks or not showing up to research because of their depression but struggling with how to share that information with their research mentors. Often, students would not say anything, which caused them anxiety because they were worried about what others in the lab would say to them when they returned. Admittedly, many students understood why this behavior would cause their research mentors to be angry or frustrated, but they weighed the consequences of their research mentors’ displeasure against the consequences of revealing their depression and decided it was not worth admitting to being depressed. This aligns with literature that suggests that when individuals have concealable stigmatized identities, or identities that can be hidden and that carry negative stereotypes, such as depression, they will often keep them concealed to avoid negative judgment or criticism ( Link and Phelan, 2001 ; Quinn and Earnshaw, 2011 ; Jones and King, 2014 ; Cooper and Brownell, 2016 ; Cooper et al. , 2019b ; Cooper et al ., unpublished data ). Therefore, it is important for research mentors to be explicit with students that 1) they recognize mental illness as a valid sickness and 2) that students with mental illness can simply explain that they are sick if they need to take time off. This may be useful to overtly state on a research website or in a research syllabus, contract, or agreement if mentors use such documents when mentoring undergraduates in their lab. Further, research mentors can purposefully work to destigmatize mental health issues by explicitly stating that struggling with mental health issues, such as depression and anxiety, is common. While we do not recommend that mentors ask students directly about depression, because this can force students to share when they are not comfortable sharing, we do recommend providing opportunities for students to reveal their depression ( Chaudoir and Fisher, 2010 ). Mentors can regularly check in with students about how they’re doing, and talk openly about the importance of mental health, which may increase the chance that students may feel comfortable revealing their depression ( Chaudoir and Quinn, 2010 ; Cooper et al ., unpublished data ).

Foster a Positive Lab Environment.

Encourage positivity in the research lab, promote working in shared spaces to enhance social support among lab members, and alleviate competition among undergraduates.

Students in this study highlighted that the “leadership” of the lab, meaning graduate students, postdocs, lab managers, and PIs, were often responsible for establishing the tone of the lab; that is, if they were in a bad mood it would trickle down and negatively affect the moods of the undergraduates. Explicitly reminding lab leadership that their moods can both positively and negatively affect undergraduates may be important in establishing a positive lab environment. Further, students highlighted how they were most likely to experience negative thoughts when they were alone in the lab. Therefore, it may be helpful to encourage all lab members to work in a shared space to enhance social interactions among students and to maximize the likelihood that undergraduates have access to help when needed. A review of 51 studies in psychiatry supported our undergraduate researchers’ perceptions that social relationships positively impacted their depression; the study found that perceived emotional support (e.g., someone available to listen or give advice), perceived instrumental support (e.g., someone available to help with tasks), and large diverse social networks (e.g., being socially connected to a large number of people) were significantly protective against depression ( Santini et al. , 2015 ). Additionally, despite forming positive relationships with other undergraduates in the lab, many undergraduate researchers admitted to constantly comparing themselves with other undergraduates, which led them to feel inferior, negatively affecting their depression. Some students talked about mentors favoring current undergraduates or talking positively about past undergraduates, which further exacerbated their feelings of inferiority. A recent study of students in undergraduate research experiences highlighted that inequitable distribution of praise to undergraduates can create negative perceptions of lab environments for students (Cooper et al. , 2019). Further, the psychology literature has demonstrated that when people feel insecure in their social environments, it can cause them to focus on a hierarchical view of themselves and others, which can foster feelings of inferiority and increase their vulnerability to depression ( Gilbert et al. , 2009 ). Thus, we recommend that mentors be conscious of their behaviors so that they do not unintentionally promote competition among undergraduates or express favoritism toward current or past undergraduates. Praise is likely best used without comparison with others and not done in a public way, although more research on the impact of praise on undergraduate researchers needs to be done. While significant research has been done on mentoring and mentoring relationships in the context of undergraduate research ( Byars-Winston et al. , 2015 ; Aikens et al. , 2017 ; Estrada et al. , 2018 ; Limeri et al. , 2019 ; NASEM, 2019 ), much less has been done on the influence of the lab environment broadly and how people in nonmentoring roles can influence one another. Yet, this study indicates the potential influence of many different members of the lab, not only their mentors, on students with depression.

Develop More Personal Relationships with Undergraduate Researchers and Provide Sufficient Guidance.

Make an effort to establish more personal relationships with undergraduates and ensure that they perceive that they have access to sufficient help and guidance with regard to their research.

When we asked students explicitly how research mentors could help create more inclusive environments for undergraduate researchers with depression, students overwhelmingly said that building mentor–student relationships would be extremely helpful. Students suggested that mentors could get to know students on a more personal level by asking about their career interests or interests outside of academia. Students also remarked that establishing a more personal relationship could help build the trust needed in order for undergraduates to confide in their research mentors about their depression, which they perceived would strengthen their relationships further because they could be honest about when they were not feeling well or their mentors might even “check in” with them in times where they were acting differently than normal. This aligns with studies showing that undergraduates are most likely to reveal a stigmatized identity, such as depression, when they form a close relationship with someone ( Chaudoir and Quinn, 2010 ). Many were intimidated to ask for research-related help from their mentors and expressed that they wished they had established a better relationship so that they would feel more comfortable. Therefore, we recommend that research mentors try to establish relationships with their undergraduates and explicitly invite them to ask questions or seek help when needed. These recommendations are supported by national recommendations for mentoring ( NASEM, 2019 ) and by literature that demonstrates that both social support (listening and talking with students) and instrumental support (providing students with help) have been shown to be protective against depression ( Santini et al. , 2015 ).

Treat Undergraduates with Respect and Remember to Praise Them.

Avoid providing harsh criticism and remember to praise undergraduates. Students with depression often have low self-esteem and are especially self-critical. Therefore, praise can help calibrate their overly negative self-perceptions.

Students in this study described that receiving criticism from others, especially harsh criticism, was particularly difficult for them given their depression. Multiple studies have demonstrated that people with depression can have an abnormal or maladaptive response to negative feedback; scientists hypothesize that perceived failure on a particular task can trigger failure-related thoughts that interfere with subsequent performance ( Eshel and Roiser, 2010 ). Thus, it is important for research mentors to remember to make sure to avoid unnecessarily harsh criticisms that make students feel like they have failed (more about failure is described in the next recommendation). Further, students with depression often have low self-esteem or low “personal judgment of the worthiness that is expressed in the attitudes the individual holds towards oneself” ( Heatherton et al. , 2003 , p. 220; Sowislo and Orth, 2013 ). Specifically, a meta-analysis of longitudinal studies found that low self-esteem is predictive of depression ( Sowislo and Orth, 2013 ), and depression has also been shown to be highly related to self-criticism ( Luyten et al. , 2007 ). Indeed, nearly all of the students in our study described thinking that they are “not good enough,” “worthless,” or “inadequate,” which is consistent with literature showing that people with depression are self-critical ( Blatt et al. , 1982 ; Gilbert et al. , 2006 ) and can be less optimistic of their performance on future tasks and rate their overall performance on tasks less favorably than their peers without depression ( Cane and Gotlib, 1985 ). When we asked students what aspects of undergraduate research helped their depression, students described that praise from their mentors was especially impactful, because they thought so poorly of themselves and they needed to hear something positive from someone else in order to believe it could be true. Praise has been highlighted as an important aspect of mentoring in research for many years ( Ashford, 1996 ; Gelso and Lent, 2000 ; Brown et al. , 2009 ) and may be particularly important for students with depression. In fact, praise has been shown to enhance individuals’ motivation and subsequent productivity ( Hancock, 2002 ; Henderlong and Lepper, 2002 ), factors highlighted by students as negatively affecting their depression. However, something to keep in mind is that a student with depression and a student without depression may process praise differently. For a student with depression, a small comment that praises the student’s work may not be sufficient for the student to process that comment as praise. People with depression are hyposensitive to reward or have reward-processing deficits ( Eshel and Roiser, 2010 ); therefore, praise may affect students without depression more positively than it would affect students with depression. Research mentors should be mindful that students with depression often have a negative view of themselves, and while students report that praise is extremely important, they may have trouble processing such positive feedback.

Normalize Failure and Be Explicit about the Importance of Research Contributions.

Explicitly remind students that experiencing failure is expected in research. Also explain to students how their individual work relates to the overall project so that they can understand how their contributions are important. It can also be helpful to explain to students why the research project as a whole is important in the context of the greater scientific community.

Experiencing failure has been thought to be a potentially important aspect of undergraduate research, because it may provide students with the potential to develop integral scientific skills such as the ability to navigate challenges and persevere ( Laursen et al. , 2010 ; Gin et al. , 2018 ; Henry et al. , 2019 ). However, in the interviews, students described that when their science experiments failed, it was particularly tough for their depression. Students’ negative reaction to experiencing failure in research is unsurprising, given recent literature that has predicted that students may be inadequately prepared to approach failure in science ( Henry et al. , 2019 ). However, the literature suggests that students with depression may find experiencing failure in research to be especially difficult ( Elliott et al. , 1997 ; Mongrain and Blackburn, 2005 ; Jones et al. , 2009 ). One potential hypothesis is that students with depression may be more likely to have fixed mindsets or more likely to believe that their intelligence and capacity for specific abilities are unchangeable traits ( Schleider and Weisz, 2018 ); students with a fixed mindset have been hypothesized to have particularly negative responses to experiencing failure in research, because they are prone to quitting easily in the face of challenges and becoming defensive when criticized ( Forsythe and Johnson, 2017 ; Dweck, 2008 ). A study of life sciences undergraduates enrolled in CUREs identified three strategies of students who adopted adaptive coping mechanisms, or mechanisms that help an individual maintain well-being and/or move beyond the stressor when faced with failure in undergraduate research: 1) problem solving or engaging in strategic planning and decision making, 2) support seeking or finding comfort and help with research, and 3) cognitive restructuring or reframing a problem from negative to positive and engaging in self encouragement ( Gin et al. , 2018 ). We recommend that, when undergraduates experience failure in science, their mentors be proactive in helping them problem solve, providing help and support, and encouraging them. Students also explained that mentors sharing their own struggles as undergraduate and graduate students was helpful, because it normalized failure. Sharing personal failures in research has been recommended as an important way to provide students with psychosocial support during research ( NASEM, 2019 ). We also suggest that research mentors take time to explain to students why their tasks in the lab, no matter how small, contribute to the greater research project ( Cooper et al. , 2019a ). Additionally, it is important to make sure that students can explain how the research project as a whole is contributing to the scientific community ( Gin et al. , 2018 ). Students highlighted that contributing to something important was really helpful for their depression, which is unsurprising, given that studies have shown that meaning in life or people’s comprehension of their life experiences along with a sense of overarching purpose one is working toward has been shown to be inversely related to depression ( Steger, 2013 ).

Limitations and Future Directions

This work was a qualitative interview study intended to document a previously unstudied phenomenon: depression in the context of undergraduate research experiences. We chose to conduct semistructured interviews rather than a survey because of the need for initial exploration of this area, given the paucity of prior research. A strength of this study is the sampling approach. We recruited a national sample of 35 undergraduates engaged in undergraduate research at 12 different public R1 institutions. Despite our representative sample from R1 institutions, these findings may not be generalizable to students at other types of institutions; lab environments, mentoring structures, and interactions between faculty and undergraduate researchers may be different at other institution types (e.g., private R1 institutions, R2 institutions, master’s-granting institutions, primarily undergraduate institutions, and community colleges), so we caution against making generalizations about this work to all undergraduate research experiences. Future work could assess whether students with depression at other types of institutions have similar experiences to students at research-intensive institutions. Additionally, we intentionally did not explore the experiences of students with specific identities owing to our sample size and the small number of students in any particular group (e.g., students of a particular race, students with a graduate mentor as the primary mentor). We intend to conduct future quantitative studies to further explore how students’ identities and aspects of their research affect their experiences with depression in undergraduate research.

The students who participated in the study volunteered to be interviewed about their depression; therefore, it is possible that depression is a more salient part of these students’ identities and/or that they are more comfortable talking about their depression than the average population of students with depression. It is also important to acknowledge the personal nature of the topic and that some students may not have fully shared their experiences ( Krumpal, 2013 ), particularly those experiences that may be emotional or traumatizing ( Kahn and Garrison, 2009 ). Additionally, our sample was skewed toward females (77%). While females do make up approximately 60% of students in biology programs on average ( Eddy et al. , 2014 ), they are also more likely to report experiencing depression ( American College Health Association, 2018 ; Evans et al. , 2018 ). However, this could be because women have higher rates of depression or because males are less likely to report having depression; clinical bias, or practitioners’ subconscious tendencies to overlook male distress, may underestimate depression rates in men ( Smith et al. , 2018 ). Further, females are also more likely to volunteer to participate in studies ( Porter and Whitcomb, 2005 ); therefore, many interview studies have disproportionately more females in the data set (e.g., Cooper et al. , 2017 ). If we had been able to interview more male students, we might have identified different findings. Additionally, we limited our sample to life sciences students engaged in undergraduate research at public R1 institutions. It is possible that students in other majors may have different challenges and opportunities for students with depression, as well as different disciplinary stigmas associated with mental health.

In this exploratory interview study, we identified a variety of ways in which depression in undergraduates negatively affected their undergraduate research experiences. Specifically, we found that depression interfered with students’ motivation and productivity, creativity and risk-taking, engagement and concentration, and self-perception and socializing. We also identified that research can negatively affect depression in undergraduates. Experiencing failure in research can exacerbate student depression, especially when students do not have access to adequate guidance. Additionally, being alone or having negative interactions with others in the lab worsened students’ depression. However, we also found that undergraduate research can positively affect students’ depression. Research can provide a familiar space where students can feel as though they are contributing to something meaningful. Additionally, students reported that having access to adequate guidance and a social support network within the research lab also positively affected their depression. We hope that this work can spark conversations about how to make undergraduate research experiences more inclusive of students with depression and that it can stimulate additional research that more broadly explores the experiences of undergraduate researchers with depression.

Important note

If you or a student experience symptoms of depression and want help, there are resources available to you. Many campuses provide counseling centers equipped to provide students, staff, and faculty with treatment for depression, as well as university-dedicated crisis hotlines. Additionally, there are free 24/7 services such as Crisis Text Line, which allows you to text a trained live crisis counselor (Text “CONNECT” to 741741; Text Depression Hotline , 2019 ), and phone hotlines such as the National Suicide Prevention Lifeline at 1-800-273-8255 (TALK). You can also learn more about depression and where to find help near you through the Anxiety and Depression Association of American website: https://adaa.org ( Anxiety and Depression Association of America, 2019 ) and the Depression and Biopolar Support Alliance: http://dbsalliance.org ( Depression and Biopolar Support Alliance, 2019 ).

ACKNOWLEDGMENTS

We are extremely grateful to the undergraduate researchers who shared their thoughts and experiences about depression with us. We acknowledge the ASU LEAP Scholars for helping us create the original survey and Rachel Scott for her helpful feedback on earlier drafts of this article. L.E.G. was supported by a National Science Foundation (NSF) Graduate Fellowship (DGE-1311230) and K.M.C. was partially supported by a Howard Hughes Medical Institute (HHMI) Inclusive Excellence grant (no. 11046) and an NSF grant (no. 1644236). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or HHMI.

  • Aikens, M. L., Robertson, M. M., Sadselia, S., Watkins, K., Evans, M., Runyon, C. R. , … & Dolan, E. L. ( 2017 ). Race and gender differences in undergraduate research mentoring structures and research outcomes . CBE—Life Sciences Education , 16 (2), ar34. Link ,  Google Scholar
  • Aikens, M. L., Sadselia, S., Watkins, K., Evans, M., Eby, L. T., & Dolan, E. L. ( 2016 ). A social capital perspective on the mentoring of undergraduate life science researchers: An empirical study of undergraduate–postgraduate–faculty triads . CBE—Life Sciences Education , 15 (2), ar16. Link ,  Google Scholar
  • Aldwin, C., & Greenberger, E. ( 1987 ). Cultural differences in the predictors of depression . American Journal of Community Psychology , 15 (6), 789–813. Medline ,  Google Scholar
  • American Association for the Advancement of Science . ( 2011 ). Vision and change in undergraduate biology education: A call to action . Retrieved November 29, 2019, from http://visionandchange.org/files/2013/11/aaas-VISchange-web1113.pdf Google Scholar
  • American College Health Association . ( 2018 ). Undergraduate reference group executive summary, Fall 2018 . Retrieved November 29, 2019, from www.acha.org/documents/ncha/NCHA-II_Fall_2018_Reference_Group_Executive_Summary.pdf Google Scholar
  • American College Health Association . ( 2019 ). Retrieved November 29, 2019, from NCHA-II_SPRING_2019_UNDERGRADUATE_REFERENCE_GROUP_DATA_REPORT.pdf www.acha.org/documents/ncha/NCHA-II_SPRING_2019_UNDERGRADUATE_REFERENCE_GROUP_DATA_REPORT.pdf Google Scholar
  • American Psychiatric Association . ( 2013 ). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Publishing. Google Scholar
  • Aneshensel, C. S., & Stone, J. D. ( 1982 ). Stress and depression: A test of the buffering model of social support . Archives of General Psychiatry , 39 (12), 1392–1396. Medline ,  Google Scholar
  • Anxiety and Depression Association of America . ( 2019 ). Home page . Retrieved November 29, 2019, from https://adaa.org Google Scholar
  • Armbruster, P., Patel, M., Johnson, E., & Weiss, M. ( 2009 ). Active learning and student-centered pedagogy improve student attitudes and performance in introductory biology . CBE—Life Sciences Education , 8 (3), 203–213. Link ,  Google Scholar
  • Ashford, S. J. ( 1996 ). Working with doctoral students: Rhythms of Academic Life: Personal Accounts of Careers in Academia . In Front, P. J.Taylor, M. S. (Eds.), Rhythms of Academic Life: Personal Accounts of Careers in Academia (pp. 153–158). Thousand Oaks, CA: Sage. Google Scholar
  • Auchincloss, L. C., Laursen, S. L., Branchaw, J. L., Eagan, K., Graham, M., Hanauer, D. I. , … & Rowland, S. ( 2014 ). Assessment of course-based undergraduate research experiences: A meeting report . CBE—Life Sciences Education , 13 (1), 29–40. Link ,  Google Scholar
  • Barak, M. E. M., Levin, A., Nissly, J. A., & Lane, C. J. ( 2006 ). Why do they leave? Modeling child welfare workers’ turnover intentions . Children and Youth Services Review , 28 (5), 548–577. Google Scholar
  • Bauer, K. W., & Bennett, J. S. ( 2003 ). Alumni perceptions used to assess undergraduate research experience . Journal of Higher Education , 74 (2), 210–230. Google Scholar
  • Birks, M., & Mills, J. ( 2015 ). Grounded theory: A practical guide . Thousand Oaks, CA: Sage. Google Scholar
  • Blatt, S. J., Quinlan, D. M., Chevron, E. S., McDonald, C., & Zuroff, D. ( 1982 ). Dependency and self-criticism: Psychological dimensions of depression . Journal of Consulting and Clinical Psychology , 50 (1), 113. Medline ,  Google Scholar
  • Brown, R. T., Daly, B. P., & Leong, F. T. ( 2009 ). Mentoring in research: A developmental approach . Professional Psychology: Research and Practice , 40 (3), 306. Google Scholar
  • Brownell, S. E., Hekmat-Scafe, D. S., Singla, V., Seawell, P. C., Imam, J. F. C., Eddy, S. L. , … & Cyert, M. S. ( 2015 ). A high-enrollment course-based undergraduate research experience improves student conceptions of scientific thinking and ability to interpret data . CBE—Life Sciences Education , 14 (2), ar21. Link ,  Google Scholar
  • Brownell, S. E., & Kloser, M. J. ( 2015 ). Toward a conceptual framework for measuring the effectiveness of course-based undergraduate research experiences in undergraduate biology . Studies in Higher Education , 40 (3), 525–544. Google Scholar
  • Byars-Winston, A. M., Branchaw, J., Pfund, C., Leverett, P., & Newton, J. ( 2015 ). Culturally diverse undergraduate researchers’ academic outcomes and perceptions of their research mentoring relationships . International Journal of Science Education , 37 (15), 2533–2554. Medline ,  Google Scholar
  • Cane, D. B., & Gotlib, I. H. ( 1985 ). Depression and the effects of positive and negative feedback on expectations, evaluations, and performance . Cognitive Therapy and Research , 9 (2), 145–160. Google Scholar
  • Ceci, S. J., & Williams, W. M. ( 2010 ). Sex differences in math-intensive fields . Current Directions in Psychological Science , 19 (5), 275–279. Medline ,  Google Scholar
  • Center for Collegiate Mental Health . ( 2017 ). Center for Collegiate Mental Health 2017 Annual Report . State College, PA: Penn State Universit. Google Scholar
  • Charmaz, K. ( 2006 ). Constructing grounded theory: A practical guide through qualitative research . Thousand Oaks, CA: Sage. Google Scholar
  • Chaudoir, S. R., & Fisher, J. D. ( 2010 ). The disclosure processes model: Understanding disclosure decision making and postdisclosure outcomes among people living with a concealable stigmatized identity . Psychological Bulletin , 136 (2), 236. Medline ,  Google Scholar
  • Chaudoir, S. R., & Quinn, D. M. ( 2010 ). Revealing concealable stigmatized identities: The impact of disclosure motivations and positive first-disclosure experiences on fear of disclosure and well-being . Journal of Social Issues , 66 (3), 570–584. Medline ,  Google Scholar
  • Clance, P. R., & Imes, S. A. ( 1978 ). The imposter phenomenon in high achieving women: Dynamics and therapeutic intervention . Psychotherapy: Theory, Research & Practice , 15 (3), 241. Google Scholar
  • Cooper, K. M., Ashley, M., & Brownell, S. E. ( 2017 ). A bridge to active learning: A summer bridge program helps students maximize their active-learning experiences and the active-learning experiences of others . CBE—Life Sciences Education , 16 (1), ar17. Link ,  Google Scholar
  • Cooper, K. M., Blattman, J. N., Hendrix, T., & Brownell, S. E. ( 2019a ). The impact of broadly relevant novel discoveries on student project ownership in a traditional lab course turned CURE . CBE—Life Sciences Education , 18 (4), ar57. Link ,  Google Scholar
  • Cooper, K. M., & Brownell, S. E. ( 2016 ). Coming out in class: Challenges and benefits of active learning in a biology classroom for LGBTQIA students . CBE—Life Sciences Education , 15 (3), ar37. https://doi.org/10.1187/cbe.16-01-0074 Link ,  Google Scholar
  • Cooper, K. M., Brownell, S. E., & Gormally, C. C. ( 2019b ). Coming out to the class: Identifying factors that influence college biology instructor decisions about whether to reveal their LGBQ identity in class . Journal of Women and Minorities in Science and Engineering , 25 (3). Google Scholar
  • Cooper, K. M., Downing, V. R., & Brownell, S. E. ( 2018 ). The influence of active learning practices on student anxiety in large-enrollment college science classrooms . International Journal of STEM Education , 5 (1), 23. Medline ,  Google Scholar
  • Cooper, K. M., Gin, L. E., Akeeh, B., Clark, C. E., Hunter, J. S., Roderick, T. B. , … & Brownell, S. E. ( 2019c ). Factors that predict life sciences student persistence in undergraduate research experiences . PLoS ONE , 14 (8). https://doi.org/10.1371/journal.pone.0220186 Google Scholar
  • Cooper, K. M., Gin, L. E., & Brownell, S. E. ( 2019d ). Diagnosing differences in what introductory biology students in a fully online and an in-person biology degree program know and do regarding medical school admission . Advances in Physiology Education , 43 (2), 221–232. Medline ,  Google Scholar
  • Cooper, K. M., Gin, L. E., & Brownell, S. E. ( In press ). Depression as a concealable stigmatized identity: What influences whether students conceal or reveal their depression in undergraduate research experiences? International Journal of STEM Education , ( in press ). Google Scholar
  • Depression and Biopolar Support Alliance . ( 2019 ). Home page . Retrieved November 28, 2019, from www.dbsalliance.org Google Scholar
  • Deroma, V. M., Leach, J. B., & Leverett, J. P. ( 2009 ). The relationship between depression and college academic performance . College Student Journal , 43 (2), 325–335. Google Scholar
  • Dweck, C. S. ( 2008 ). Mindset: The new psychology of success . New York, NY: Random House Digital. Google Scholar
  • Dyson, R., & Renk, K. ( 2006 ). Freshmen adaptation to university life: Depressive symptoms, stress, and coping . Journal of Clinical Psychology , 62 (10), 1231–1244. Medline ,  Google Scholar
  • Eddy, S. L., Brownell, S. E., & Wenderoth, M. P. ( 2014 ). Gender gaps in achievement and participation in multiple introductory biology classrooms . CBE—Life Sciences Education , 13 (3), 478–492. https://doi.org/10.1187/cbe.13-10-0204 Link ,  Google Scholar
  • Eisenberg, D., Gollust, S. E., Golberstein, E., & Hefner, J. L. ( 2007 ). Prevalence and correlates of depression, anxiety, and suicidality among university students . American Journal of Orthopsychiatry , 77 (4), 534–542. Medline ,  Google Scholar
  • Elliott, R., Sahakian, B. J., Herrod, J. J., Robbins, T. W., & Paykel, E. S. ( 1997 ). Abnormal response to negative feedback in unipolar depression: Evidence for a diagnosis specific impairment . Journal of Neurology, Neurosurgery & Psychiatry , 63 (1), 74–82. Medline ,  Google Scholar
  • Eshel, N., & Roiser, J. P. ( 2010 ). Reward and punishment processing in depression . Biological Psychiatry , 68 (2), 118–124. Medline ,  Google Scholar
  • Estrada, M., Hernandez, P. R., & Schultz, P. W. ( 2018 ). A longitudinal study of how quality mentorship and research experience integrate underrepresented minorities into STEM careers . CBE—Life Sciences Education , 17 (1), ar9. Link ,  Google Scholar
  • Evans, T. M., Bira, L., Gastelum, J. B., Weiss, L. T., & Vanderford, N. L. ( 2018 ). Evidence for a mental health crisis in graduate education . Nature Biotechnology , 36 (3), 282. Medline ,  Google Scholar
  • Everson, H. T., Tobias, S., Hartman, H., & Gourgey, A. ( 1993 ). Test anxiety and the curriculum: The subject matters . Anxiety, Stress, and Coping , 6 (1), 1–8. Google Scholar
  • Flaherty, C. ( 2018 ). New study says graduate students’ mental health is a “crisis.” Retrieved November 29, 2019, from www.insidehighered.com/news/2018/03/06/new-study-says-graduate-students-mental-health-crisis Google Scholar
  • Forsythe, A., & Johnson, S. ( 2017 ). Thanks, but no-thanks for the feedback . Assessment & Evaluation in Higher Education , 42 (6), 850–859. Google Scholar
  • Garlow, S. J., Rosenberg, J., Moore, J. D., Haas, A. P., Koestner, B., Hendin, H., & Nemeroff, C. B. ( 2008 ). Depression, desperation, and suicidal ideation in college students: Results from the American Foundation for Suicide Prevention College Screening Project at Emory University . Depression and Anxiety , 25 (6), 482–488. Medline ,  Google Scholar
  • Gelso, C. J., & Lent, R. W. ( 2000 ). Scientific training and scholarly productivity: The person, the training environment, and their interaction . In Brown, S. D.Lent, R. W. (Eds.), Handbook of counseling psychology (pp. 109–139). Hoboken, NJ: John Wiley & Sons Inc. Google Scholar
  • Gilbert, P., Baldwin, M. W., Irons, C., Baccus, J. R., & Palmer, M. ( 2006 ). Self-criticism and self-warmth: An imagery study exploring their relation to depression . Journal of Cognitive Psychotherapy , 20 (2), 183. Google Scholar
  • Gilbert, P., McEwan, K., Bellew, R., Mills, A., & Gale, C. ( 2009 ). The dark side of competition: How competitive behaviour and striving to avoid inferiority are linked to depression, anxiety, stress and self-harm . Psychology and Psychotherapy: Theory, Research and Practice , 82 (2), 123–136. Medline ,  Google Scholar
  • Gin, L. E., Rowland, A. A., Steinwand, B., Bruno, J., & Corwin, L. A. ( 2018 ). Students who fail to achieve predefined research goals may still experience many positive outcomes as a result of CURE participation . CBE—Life Sciences Education , 17 (4), ar57. Link ,  Google Scholar
  • Glesne, C., & Peshkin, A. ( 1992 ). Becoming qualitative researchers: An introduction . London, England, UK: Longman. Google Scholar
  • Grav, S., Hellzèn, O., Romild, U., & Stordal, E. ( 2012 ). Association between social support and depression in the general population: The HUNT study, a cross-sectional survey . Journal of Clinical Nursing , 21 (1–2), 111–120. Medline ,  Google Scholar
  • Guest, G., Bunce, A., & Johnson, L. ( 2006 ). How many interviews are enough? An experiment with data saturation and variability . Field Methods , 18 (1), 59–82. Google Scholar
  • Hancock, D. R. ( 2002 ). Influencing graduate students’ classroom achievement, homework habits and motivation to learn with verbal praise . Educational Research , 44 (1), 83–95. Google Scholar
  • Hannah, D. R., & Lautsch, B. A. ( 2011 ). Counting in qualitative research: Why to conduct it, when to avoid it, and when to closet it . Journal of Management Inquiry , 20 (1), 14–22. Google Scholar
  • Heatherton, T. F., & Wyland, C. L. ( 2003 ). Assessing self-esteem . In Lopez, S. J.Snyder, C. R. (Eds.), Positive psychological assessment: A handbook of models and measures (pp. 219–233). Washington, DC: American Psychological Association. https://doi.org/10.1037/10612-014 . Google Scholar
  • Henderlong, J., & Lepper, M. R. ( 2002 ). The effects of praise on children’s intrinsic motivation: A review and synthesis . Psychological Bulletin , 128 (5), 774. Medline ,  Google Scholar
  • Henry, M. A., Shorter, S., Charkoudian, L., Heemstra, J. M., & Corwin, L. A. ( 2019 ). FAIL is not a four-letter word: A theoretical framework for exploring undergraduate students’ approaches to academic challenge and responses to failure in STEM learning environments . CBE—Life Sciences Education , 18 (1), ar11. Link ,  Google Scholar
  • Hernandez, P. R., Woodcock, A., Estrada, M., & Schultz, P. W. ( 2018 ). Undergraduate research experiences broaden diversity in the scientific workforce . BioScience , 68 (3), 204–211. Google Scholar
  • Hish, A. J., Nagy, G. A., Fang, C. M., Kelley, L., Nicchitta, C. V., Dzirasa, K., & Rosenthal, M. Z. ( 2019 ). Applying the stress process model to stress–burnout and stress–depression relationships in biomedical doctoral students: A cross-sectional pilot study . CBE—Life Sciences Education , 18 (4), ar51. Link ,  Google Scholar
  • Howell, E., & McFeeters, J. ( 2008 ). Children’s mental health care: Differences by race/ethnicity in urban/rural areas . Journal of Health Care for the Poor and Underserved , 19 (1), 237–247. Medline ,  Google Scholar
  • Hysenbegasi, A., Hass, S. L., & Rowland, C. R. ( 2005 ). The impact of depression on the academic productivity of university students . Journal of Mental Health Policy and Economics , 8 (3), 145. Medline ,  Google Scholar
  • Ibrahim, A. K., Kelly, S. J., Adams, C. E., & Glazebrook, C. ( 2013 ). A systematic review of studies of depression prevalence in university students . Journal of Psychiatric Research , 47 (3), 391–400. Medline ,  Google Scholar
  • Intemann, K. ( 2009 ). Why diversity matters: Understanding and applying the diversity component of the National Science Foundation’s broader impacts criterion . Social Epistemology , 23 (3–4), 249–266. Google Scholar
  • Ishiyama, J. ( 2002 ). Does early participation in undergraduate research benefit social science and humanities students? College Student Journal , 36 (3), 381–387. Google Scholar
  • Jenkins, S. R., Belanger, A., Connally, M. L., Boals, A., & Durón, K. M. ( 2013 ). First-generation undergraduate students’ social support, depression, and life satisfaction . Journal of College Counseling , 16 (2), 129–142. Google Scholar
  • Jobst, A., Sabass, L., Palagyi, A., Bauriedl-Schmidt, C., Mauer, M. C., Sarubin, N. , … & Zill, P. ( 2015 ). Effects of social exclusion on emotions and oxytocin and cortisol levels in patients with chronic depression . Journal of Psychiatric Research , 60 , 170–177. Medline ,  Google Scholar
  • Jones, K. P., & King, E. B. ( 2014 ). Managing concealable stigmas at work: A review and multilevel model . Journal of Management , 40 (5), 1466–1494. Google Scholar
  • Jones, M. T., Barlow, A. E., & Villarejo, M. ( 2010 ). Importance of undergraduate research for minority persistence and achievement in biology . Journal of Higher Education , 81 (1), 82–115. Google Scholar
  • Jones, N. P., Papadakis, A. A., Hogan, C. M., & Strauman, T. J. ( 2009 ). Over and over again: Rumination, reflection, and promotion goal failure and their interactive effects on depressive symptoms . Behaviour Research and Therapy , 47 (3), 254–259. Medline ,  Google Scholar
  • Judd, L. L., Paulus, M. J., Schettler, P. J., Akiskal, H. S., Endicott, J., Leon, A. C. , … & Keller, M. B. ( 2000 ). Does incomplete recovery from first lifetime major depressive episode herald a chronic course of illness? American Journal of Psychiatry , 157 (9), 1501–1504. Medline ,  Google Scholar
  • Kahn, J. H., & Garrison, A. M. ( 2009 ). Emotional self-disclosure and emotional avoidance: Relations with symptoms of depression and anxiety . Journal of Counseling Psychology , 56 (4), 573. Google Scholar
  • Kataoka, S. H., Zhang, L., & Wells, K. B. ( 2002 ). Unmet need for mental health care among US children: Variation by ethnicity and insurance status . American Journal of Psychiatry , 159 (9), 1548–1555. Medline ,  Google Scholar
  • Kreger, D. W. ( 1995 ). Self-esteem, stress, and depression among graduate students . Psychological Reports , 76 (1), 345–346. Medline ,  Google Scholar
  • Krumpal, I. ( 2013 ). Determinants of social desirability bias in sensitive surveys: A literature review . Quality & Quantity , 47 (4), 2025–2047. Google Scholar
  • Landis, J. R., & Koch, G. G. ( 1977 ). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers . Biometrics , 33 (2), 363–374. Medline ,  Google Scholar
  • Laursen, S., Hunter, A.-B., Seymour, E., Thiry, H., & Melton, G. ( 2010 ). Undergraduate research in the sciences: Engaging students in real science . Hoboken, NJ: Wiley. Google Scholar
  • Limeri, L. B., Asif, M. Z., Bridges, B. H., Esparza, D., Tuma, T. T., Sanders, D. , … & Maltese, A. V. ( 2019 ). “Where’s my mentor?” Characterizing negative mentoring experiences in undergraduate life science research . CBE—Life Sciences Education , 18 (4), ar61. Link ,  Google Scholar
  • Link, B. G., & Phelan, J. C. ( 2001 ). Conceptualizing stigma . Annual Review of Sociology , 27 (1), 363–385. Google Scholar
  • Luyten, P., Sabbe, B., Blatt, S. J., Meganck, S., Jansen, B., De Grave, C. , … & Corveleyn, J. ( 2007 ). Dependency and self-criticism: Relationship with major depressive disorder, severity of depression, and clinical presentation . Depression and Anxiety , 24 (8), 586–596. Medline ,  Google Scholar
  • Mabrouk, P. A., & Peters, K. ( 2000 ). Student perspectives on undergraduate research (UR) experiences in chemistry and biology . CUR Quarterly , 21 (1), 25–33. Google Scholar
  • Maxwell, J. A. ( 2010 ). Using numbers in qualitative research . Qualitative Inquiry , 16 (6), 475–482. Google Scholar
  • Mongrain, M., & Blackburn, S. ( 2005 ). Cognitive vulnerability, lifetime risk, and the recurrence of major depression in graduate students . Cognitive Therapy and Research , 29 (6), 747–768. Google Scholar
  • Nagy, G. A., Fang, C. M., Hish, A. J., Kelly, L., Nicchitta, C. V., Dzirasa, K., & Rosenthal, M. Z. ( 2019 ). Burnout and mental health problems in biomedical doctoral students . CBE—Life Sciences Education , 18 (2), ar27. Link ,  Google Scholar
  • National Academies of Sciences, Engineering, and Medicine (NASEM) . ( 2017 ). Undergraduate research experiences for STEM students: Successes, challenges, and opportunities . Washington, DC: National Academies Press. https://doi.org/10.17226/24622 Google Scholar
  • NASEM . ( 2019 ). The science of effective mentorship in STEMM . Washington, DC: National Academies Press. Retrieved November 29, 2019, from www.nap.edu/download/25568 Google Scholar
  • Osborne, J., & Collins, S. ( 2001 ). Pupils’ views of the role and value of the science curriculum: A focus-group study . International Journal of Science Education , 23 (5), 441–467. https://doi.org/10.1080/09500690010006518 Google Scholar
  • Porter, S. R., & Whitcomb, M. E. ( 2005 ). Non-response in student surveys: The role of demographics, engagement and personality . Research in Higher Education , 46 (2), 127–152. Google Scholar
  • President’s Council of Advisors on Science and Technology . ( 2012 ). Engage to excel: Producing one million additional college graduates with degrees in science, Technology, Engineering, and mathematics . Washington, DC: U.S. Government Office of Science and Technology. Google Scholar
  • Prunuske, A. J., Wilson, J., Walls, M., & Clarke, B. ( 2013 ). Experiences of mentors training underrepresented undergraduates in the research laboratory . CBE—Life Sciences Education , 12 (3), 403–409. Link ,  Google Scholar
  • Quinn, D. M., & Earnshaw, V. A. ( 2011 ). Understanding concealable stigmatized identities: The role of identity in psychological, physical, and behavioral outcomes . Social Issues and Policy Review , 5 (1), 160–190. Google Scholar
  • Rauckhorst, W. H., Czaja, J. A., & Baxter Magolda, M. ( 2001 ). Measuring the impact of the undergraduate research experience on student intellectual development . Snowbird, UT: Project Kaleidoscope Summer Institute. Google Scholar
  • Saldaña, J. ( 2015 ). The coding manual for qualitative researchers . Thousand Oaks, CA: Sage. Google Scholar
  • Santiago, C. D., Kaltman, S., & Miranda, J. ( 2013 ). Poverty and mental health: How do low-income adults and children fare in psychotherapy? Journal of Clinical Psychology , 69 (2), 115–126. Medline ,  Google Scholar
  • Santini, Z. I., Koyanagi, A., Tyrovolas, S., Mason, C., & Haro, J. M. ( 2015 ). The association between social relationships and depression: A systematic review . Journal of Affective Disorders , 175 , 53–65. Medline ,  Google Scholar
  • Schleider, J., & Weisz, J. ( 2018 ). A single-session growth mindset intervention for adolescent anxiety and depression: 9-month outcomes of a randomized trial . Journal of Child Psychology and Psychiatry , 59 (2), 160–170. Medline ,  Google Scholar
  • Seymour, E., & Hewitt, N. M. ( 1997 ). Talking about leaving: Why undergraduates leave the sciences . Westview Press. Google Scholar
  • Seymour, E., & Hunter, A.-B. ( 2019 ). Talking about leaving revisited . New York, NY: Springer. Google Scholar
  • Seymour, E., Hunter, A.-B., Laursen, S. L., & DeAntoni, T. ( 2004 ). Establishing the benefits of research experiences for undergraduates in the sciences: First findings from a three-year study . Science Education , 88 (4), 493–534. Google Scholar
  • Smith, D. T., Mouzon, D. M., & Elliott, M. ( 2018 ). Reviewing the assumptions about men’s mental health: An exploration of the gender binary . American Journal of Men’s Health , 12 (1), 78–89. Medline ,  Google Scholar
  • Sorkness, C. A., Pfund, C., Ofili, E. O., Okuyemi, K. S., Vishwanatha, J. K., Zavala, M. E. , … & Deveci, A. ( 2017 ). A new approach to mentoring for research careers: The National Research Mentoring Network . BMC Proceedings , 11 , 22. Medline ,  Google Scholar
  • Sowislo, J. F., & Orth, U. ( 2013 ). Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies . Psychological Bulletin , 139 (1), 213. Medline ,  Google Scholar
  • Steger, M. F. ( 2013 ). Experiencing meaning in life: Optimal functioning at the nexus of well-being, psychopathology, and spirituality . In Wong, P. T. P. (Ed.), The human quest for meaning (pp. 211–230). England, UK: Routledge. Google Scholar
  • Strenta, A. C., Elliott, R., Adair, R., Matier, M., & Scott, J. ( 1994 ). Choosing and leaving science in highly selective institutions . Research in Higher Education , 35 (5), 513–547. Google Scholar
  • Text Depression Hotline . ( 2019 ). Crisis text line . Retrieved November 29, 2019, from www.crisistextline.org/depression Google Scholar
  • Thiry, H., & Laursen, S. L. ( 2011 ). The role of student–advisor interactions in apprenticing undergraduate researchers into a scientific community of practice . Journal of Science Education and Technology , 20 (6), 771–784. Google Scholar
  • Thompson, J. J., Conaway, E., & Dolan, E. L. ( 2016 ). Undergraduate students’ development of social, cultural, and human capital in a networked research experience . Cultural Studies of Science Education , 11 (4), 959–990. Google Scholar
  • Trenor, J. M., Miller, M. K., & Gipson, K. G. ( 2011 ). Utilization of a think-aloud protocol to cognitively validate a survey instrument identifying social capital resources of engineering undergraduates . 118th American Society for Engineering Education Annual Conference and Exposition, Vancouver, BC, Canada . Google Scholar
  • Turner, R. J., & Noh, S. ( 1988 ). Physical disability and depression: A longitudinal analysis . Journal of Health and Social Behavior , 29 (1), 23–37. Medline ,  Google Scholar
  • Watson, D., & Friend, R. ( 1969 ). Measurement of social-evaluative anxiety . Journal of Consulting and Clinical Psychology , 33 (4), 448. Medline ,  Google Scholar
  • Weeks, J. W., Heimberg, R. G., Fresco, D. M., Hart, T. A., Turk, C. L., Schneier, F. R., & Liebowitz, M. R. ( 2005 ). Empirical validation and psychometric evaluation of the Brief Fear of Negative Evaluation Scale in patients with social anxiety disorder . Psychological Assessment , 17 (2), 179. Medline ,  Google Scholar
  • World Health Organization . ( 2018 ). Depression . Retrieved November 29, 2019, from www.who.int/news-room/fact-sheets/detail/depression Google Scholar
  • Wyatt, T., & Oswalt, S. B. ( 2013 ). Comparing mental health issues among undergraduate and graduate students . American Journal of Health Education , 44 (2), 96–107. Google Scholar
  • Baylee A. Edwards ,
  • Chloe Bowen ,
  • M. Elizabeth Barnes , and
  • Tati Russo-Tait, Monitoring Editor
  • Sara E. Grineski ,
  • Danielle X. Morales , and
  • Timothy W. Collins
  • Derek Braun, Monitoring Editor
  • Tala Araghi ,
  • Carly A. Busch , and
  • Carly A. Busch ,
  • Tasneem F. Mohammed ,
  • Erika M. Nadile ,
  • Madison L. Witt ,
  • Cindy Vargas ,
  • Missy Tran ,
  • Joseph Gazing Wolf ,
  • Danielle Brister , and
  • Sehoya Cotner, Monitoring Editor
  • Katelyn M. Cooper ,
  • Sarah L. Eddy , and
  • Coping behavior versus coping style: characterizing a measure of coping in undergraduate STEM contexts 14 February 2022 | International Journal of STEM Education, Vol. 9, No. 1
  • Lisa A. Corwin ,
  • Michael E. Ramsey ,
  • Eric A. Vance ,
  • Elizabeth Woolner ,
  • Stevie Maiden ,
  • Nina Gustafson and
  • Joseph A. Harsh
  • Erin Shortlidge, Monitoring Editor
  • K. Supriya ,
  • Katelyn M. Cooper , and
  • Brian Sato, Monitoring Editor
  • Logan E. Gin ,
  • Danielle Pais ,
  • Clark Coffman, Monitoring Editor
  • Nicholas J. Wiesenthal , and
  • Maryrose Weatherton and
  • Elisabeth E. Schussler
  • Erika Offerdahl, Monitoring Editor
  • Eight Recommendations to Promote Effective Study Habits for Biology Students Enrolled in Online Courses Journal of Microbiology & Biology Education, Vol. 23, No. 1
  • Fostering professional development through undergraduate research: supporting faculty mentors and student researchers 30 March 2022 | Mentoring & Tutoring: Partnership in Learning, Vol. 30, No. 2
  • Jeffrey Maloy ,
  • Monika B. Kwapisz , and
  • Bryce E. Hughes
  • Terrell Morton, Monitoring Editor
  • Anxiety and depression among US college students engaging in undergraduate research during the COVID-19 pandemic 14 December 2021 | Journal of American College Health, Vol. 9
  • Danielle Brister ,
  • Sara E. Brownell ,
  • Chade T. Claiborne ,
  • Curtis Lunt ,
  • Kobe M. Walker ,
  • Tamiru D. Warkina ,
  • Yi Zheng , and
  • Rebecca Price, Monitoring Editor
  • Dominant Learning Styles of Interior Design Students in Generation Z 26 July 2021 | Journal of Interior Design, Vol. 46, No. 4
  • Linking Emotional Intelligence, Physical Activity and Aggression among Undergraduates 26 November 2021 | International Journal of Environmental Research and Public Health, Vol. 18, No. 23
  • Advancing undergraduate synthetic biology education: insights from a Canadian iGEM student perspective Canadian Journal of Microbiology, Vol. 67, No. 10
  • Frank A. Guerrero ,
  • Sara E. Brownell , and
  • Jennifer Momsen, Monitoring Editor
  • Nicholas J. Wiesenthal ,
  • Isabella Ferreira , and
  • Grant Ean Gardner, Monitoring Editor
  • Carolyn E. Clark ,
  • Deanna B. Elliott ,
  • Travis B. Roderick ,
  • Rachel A. Scott ,
  • Denisse Arellano ,
  • Diana Ramirez ,
  • Kimberly Velarde ,
  • Allyson Aeschliman ,
  • Sarah T. Avalle ,
  • Jessica Berkheimer ,
  • Rachel Campos ,
  • Michael Gerbasi ,
  • Sophia Hughes ,
  • Julie A. Roberts ,
  • Quinn M. White ,
  • Ehren Wittekind ,
  • Rachelle Spell, Monitoring Editor
  • Christine Pfund ,
  • Janet L. Branchaw ,
  • Melissa McDaniels ,
  • Angela Byars-Winston ,
  • Steven P. Lee ,, and
  • Bruce Birren
  • Emma C. Goodwin ,
  • Vladimir Anokhin ,
  • MacKenzie J. Gray ,
  • Daniel E. Zajic ,
  • Jason E. Podrabsky , and
  • Erin E. Shortlidge
  • Depression as a concealable stigmatized identity: what influences whether students conceal or reveal their depression in undergraduate research experiences? 4 June 2020 | International Journal of STEM Education, Vol. 7, No. 1

research paper about stress and depression

Submitted: 4 November 2019 Revised: 24 February 2020 Accepted: 6 March 2020

© 2020 K. M. Cooper, L. E. Gin, et al. CBE—Life Sciences Education © 2020 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Philip Gold M.D.

The Relationship Between Depression and Stress

Depression represents a stress response that has run awry..

Posted February 7, 2024 | Reviewed by Davia Sills

  • What Is Depression?
  • Find a therapist to overcome depression
  • Research reveals that a wayward stress system attack on the brain can lead to depression.
  • The legacy of our evolutionary and biological stress responses is a significant burden for humans today.
  • While a normal stress response is highly adaptive, depression is a stress response run awry.
  • Increased understanding of stress response and depression is a critical advantage in treatment options today.

Hippocrates wrote that we are all beset by disturbing forces that upset our balance (1). Fortunately, there are restorative forces that can re-establish balance. Galen called these Vis Medicatrix Naturae , the healing forces of nature (1, 2). We now call the disturbing forces stressors , the balance homeostasis , and the healing forces adaptive responses (3).

Stressors are imminent or perceived challenges to balance or homeostasis (4). Stress is almost always accompanied by increased vigilance and anxiety . The response is strongest when the stressor is highly unpleasant and uncontrollable.

Like other responses, such as the immune response, the stress system is essential for survival. The immune system can also run awry and lead to autoimmune disease (5), where the immune system attacks our own tissues. In depression , a wayward stress system attacks the brain foremost, as well as multiple tissues throughout the body, leading to depression (6, 7).

The Normal Stress Response

Let’s consider a normal stress response. Imagine that a small group of individuals is hiking in the woods as night approaches. A wildfire is spreading rapidly nearby. The hikers are anxious, highly vigilant, and focused entirely on the threat. Their collective mood fixes in a distressed and fearful mode. To keep them focused, the stress response lowers their distractibility. In particular, they aren’t drawn to pleasurable stimuli. During this threatening situation, they won’t be distracted by food, sleep, sex , or a pretty scene (8).

Our hikers’ brains push the pause button on complex thinking about anything beyond finding their way back to their base safely. Physical responses accompany their behavioral responses. Their hearts race, and their blood pressure increases. Cortisol and adrenalin levels rise precipitously. Inflammation occurs during the normal stress response to prepare for a potential injury. Blood clotting is activated to prepare for a possible hemorrhage. Blood sugar rises to provide extra fuel for their stressed brains. Notably, and regrettably, these behavioral and physiological responses also occur during psychological stressors such as public speaking (8).

The presence of phenomena such as inflammation and increased blood clotting as a routine component of almost any stress response is an important new concept that helps explain why chronic stress and melancholic depression can have such devastating consequences (8).

How Did Increased Inflammation, Blood Clotting, and Blood Sugar Levels Get Folded Into Our Normal Stress Response?

Our evolutionary and biological roots provide a possible answer. In the past, major stressors consisted of being hunted and competing for a mate. The risk of injury in each of these contexts was very great. Thus, the connection between the perception of stress or danger (9) activated responses of the inflammatory and coagulation systems and signaled blood sugars to rise. Their legacy for those of us in the present, when they are irrelevant to most of our stress responses, is a significant burden.

The Hikers Reach Home Base

When the hikers reach their home base, most feel much better and can think more clearly. They can enjoy a beautiful scene, and their heart rates, blood pressures, immune systems, blood clotting systems, and blood sugar levels return to normal. In one individual, the stress response did not resolve but evolved into a severe case of melancholia (8).

How Does the Stress Response Morph Into a Melancholic Depression?

The defining behavioral characteristics of both stress and melancholia consist of fear , anxiety, and alarm. As noted in an earlier paper in Psychology Today , melancholia contradicts the word “depression” in that it is often a state of increased arousal and anxiety, often directed at the self. Individuals with melancholia are often bombarded by negatively charged emotional memories of failure and loss that significantly contribute to the depressed state. They lose the capacity to experience pleasure, experience decreased appetite and weight loss, have insomnia , and lose interest in sex.

During stress, sufficient anxiety promotes substantial efforts to avoid being hurt without interfering with effective functioning. In melancholic depression, fear, anxiety, and alarm can be profoundly greater than during stress, produce anguish and hopelessness, and interfere with the capacity to fight off depression (8).

research paper about stress and depression

As noted above, during stress, cognition shifts from complex reasoning to automatic, instinctual actions or actions that had previously worked in dangerous situations. In melancholia, cognition is often limited to obsessive, ruminative preoccupations regarding the deficiencies of the self. Concentration is impaired, and overall cognitive function suffers (8).

During stress, there is a modest decrease in the capacity to respond to pleasurable stimuli as a means of protecting against unwanted distractions. This does not lead to a demoralization that could interfere with an effective stress response. In melancholia, the decrease in the capacity to anticipate or experience pleasure is pervasive and profound, leading to the incapacity to enjoy anything or remember ever being happy (8).

Additionally, during stress, there is a tendency towards a decreased appetite and a decreased propensity to sleep to allow total focus on the threat at hand. Melancholic patients lose their appetite, which can be life-threatening in the elderly, and have insomnia, most often with early morning awakening.

The inflammation, increased blood clotting, and high blood sugars associated with melancholic depression are likely to contribute to premature coronary artery disease (10), diabetes (11, 12), stroke, and osteoporosis (13) that occur in depression and that shorten lives by 7-10 years, independent of suicide or smoking (14, 15).

It is clear that melancholic depression responds best to a combination of psychotherapy and medication . Patients suffering from melancholic depression need more than relatively infrequent doctor’s visits to manage medication alone. Doctors must also be aware of the depressed patient’s susceptibility to premature systemic illnesses and utilize the medical means available to prevent, treat, or keep these illnesses from progressing. In addition to a battery of medical tests, antidepressants can also neutralize many of the physiological stigmata of depressive illness (14).

Overall, while a normal stress response is highly adaptive, depression represents a distorted, prolonged, pernicious version of a stress response. This is compatible with our knowledge that stress imprints itself on the stress system of the brain, damages or destroys critical tissues, and leads to the clinical and biochemical manifestations of illnesses such as melancholic depression (6, 7, 16, 17). Having this increased understanding of stress response and its impact on depression and overall health brings new and critical advantages in treatment options and efforts today, offering promising paths to wellness for millions.

1. Taylor HO. Greek Biology and Medicine. Boston Massachusetts: Marshall Jones Co.; 1922.

2. Singer C. A Short History of Science. Oxford, England: Oxford University Press; 1941.

3. Selye H. Montreal. Quebec, Canada: Acta Medical Publisher. 1950.

4. Chrousos GP, Gold PW. The Concepts of Stress and Stress System Disorders: Overview of Physical and Behavioral Homeostasis. JAMA. 1992;267:1255-2.

5. Sternberg EM, Chrousos GP, Wilder RL, Gold PW. The stress response and the regulation of inflammatory disease. Annals of Internal Medicine. 1992;117(10):854-66.

6. Gold PW, Goodwin FK, Chrousos GP. Clinical and biochemical manifestations of depression. Relation to the neurobiology of stress (1). N Engl J Med. 1988;319(6):348-53.

7. Gold PW, Goodwin FK, Chrousos GP. Clinical and biochemical manifestations of depression. Relation to the neurobiology of stress (2). N Engl J Med. 1988;319(7):413-20.

8. Gold PW. Breaking Through Depression: A Guide to the Next Generation of Promising Research and Revolutionary New Treatments. New York, NY: 12 Hachette Book Group; 2023.

9. Raison CL MA. The evolutionary significance of depression in pathogen host defense (Pathos-D). Mol Psychiatry2012. p. 15-37.

10. Whooley MA, de Jonge P, Vittinghoff E, et al. Depressive symptoms, health behaviors, and risk of cardiovascular events in patients with coronary heart disease. JAMA. 2008;300(20):2379-88.

11. Knol MJ, Twisk JWR, Beekman ATF, et al. Depression as a risk factor for the onset of type II diabetes. Diabetologia. 2005;49:837-45.

12. Badescu SV TC, Zagrean L. The association between diabetes mellitus and depressionb. JMed Life. 2016;9:120-5.

13. Michelson D, Stratakis C, Hill L, Reynolds J, Galliven E, Chrousos G, Gold P. Bone mineral density in women with depression. N Engl J Med. 1996;335(16):1176-81.

14. Gold PW. The organization of the stress system and its dysregulation in depressive illness. Mol Psychiat. 2015;20:32-47.

15. Gold PW. Endocrine factors in key structural and intracellular changes in depression. Trends endocrinol Metab. 2021;32:212-23.

16. Gold PW, Wong ML, Goldstein DS, Gold HK, Ronsaville DS, Esler M, et al. Cardiac implications of increased arterial entry and reversible 24-h central and peripheral norepinephrine levels in melancholia. Proc Natl Acad Sci U S A. 2005;102(23):8303-8.

17. Gold PW Loriaux L, Roy A, et al. Responses to corticotropin-releasing hormone in the hypercortisolism of depression and Cushing's disease. Pathophysiologic and diagnostic implications. N. Eng. J. Med.1986. p. 1329-35.

Philip Gold M.D.

Philip Gold, M.D. , has worked at the National Institutes of Health and the National Institute of Mental Health. He is the author of Breaking Through Depression.

  • Find a Therapist
  • Find a Treatment Center
  • Find a Psychiatrist
  • Find a Support Group
  • Find Teletherapy
  • United States
  • Brooklyn, NY
  • Chicago, IL
  • Houston, TX
  • Los Angeles, CA
  • New York, NY
  • Portland, OR
  • San Diego, CA
  • San Francisco, CA
  • Seattle, WA
  • Washington, DC
  • Asperger's
  • Bipolar Disorder
  • Chronic Pain
  • Eating Disorders
  • Passive Aggression
  • Personality
  • Goal Setting
  • Positive Psychology
  • Stopping Smoking
  • Low Sexual Desire
  • Relationships
  • Child Development
  • Therapy Center NEW
  • Diagnosis Dictionary
  • Types of Therapy

January 2024 magazine cover

Overcome burnout, your burdens, and that endless to-do list.

  • Coronavirus Disease 2019
  • Affective Forecasting
  • Neuroscience

Depression, Anxiety, and Stress as a Function of Psychological Strains: Towards an Etiological Theory of Mood Disorders and Psychopathologies

Affiliations.

  • 1 Central University of Finance and Economics School of Sociology and Psychology, Beijing, China; State University of New York Buffalo State Department of Sociology, USA.
  • 2 Department of Social Work and Social Administration, The University of Hong Kong, Hong Kong, China.
  • 3 Department of Social Psychology, Putra University of Malaysia, Malaysia.
  • 4 Eramishantsev City Clinical Hospital, Moscow, Russia.
  • 5 Department of Human Development and Family Study, Putra University of Malaysia, Malaysia.
  • 6 Faculty of Social Sciences & Liberal Arts, UCSI University, Kuala Lumpur, Malaysia.
  • 7 Department of Psychology and the Centre for Psychosocial Health,The Education University of Hong Kong, Hong Kong, China. Electronic address: [email protected].
  • PMID: 32479327
  • DOI: 10.1016/j.jad.2020.03.076

Background: The etiological factors of mood disorders and psychopathologies are understudied. In this paper, we explored whether social psychological strains are related to depression, anxiety, and stress in non-clinical populations.

Methods: 6,305 college students (39.3% men; 60.7% women) from six Chinese provincial-level jurisdictions completed a paper-and-pencil survey with Psychological Strain Scales (PSS-40) and Depression, Anxiety, and Stress Scales-21 (DASS-21), both validated in Chinese populations.

Results: Both PSS-40 and DASS-21 have high internal consistency reliabilities, and are highly correlated with each other. Hence, Chinese college students with greater psychological strains (value, aspiration, deprivation, or coping) have greater depression, anxiety, and stress. These results still held after controlling for relevant socio-demographic variables in the multiple regression models.

Limitations: This was a cross-sectional study, and the sample only included several provinces in mainland China, not a representative sample of all of them.

Conclusions: Mood disorders and psychopathologies are linked to suicidal thoughts and behaviors. The results of this study extend the Strain Theory of Suicide from explaining the risk factors of suicidality to mood disorders and psychopathologies. Hence, these findings can inform prevention measures among college students, and possibly the general population.

Keywords: Anxiety; China; College students; Depression; Psychological strains; Stress.

Copyright © 2020. Published by Elsevier B.V.

  • Anxiety / epidemiology
  • Anxiety / etiology
  • China / epidemiology
  • Cross-Sectional Studies
  • Depression* / epidemiology
  • Depression* / etiology
  • Mood Disorders* / epidemiology
  • Mood Disorders* / etiology
  • Stress, Psychological / epidemiology
  • Type 2 Diabetes
  • Heart Disease
  • Digestive Health
  • Multiple Sclerosis
  • COVID-19 Vaccines
  • Occupational Therapy
  • Healthy Aging
  • Health Insurance
  • Public Health
  • Patient Rights
  • Caregivers & Loved Ones
  • End of Life Concerns
  • Health News
  • Thyroid Test Analyzer
  • Doctor Discussion Guides
  • Hemoglobin A1c Test Analyzer
  • Lipid Test Analyzer
  • Complete Blood Count (CBC) Analyzer
  • What to Buy
  • Editorial Process
  • Meet Our Medical Expert Board

Anxiety and Depression Overlap: Link Between Comorbid Disorders

  • How They Feel
  • When Treatment-Resistant

Having depression and anxiety at the same time is somewhat common. Research shows that 60% of people with anxiety will also have symptoms of depression. The rate is the same for those who have depression with symptoms of anxiety.

Anxiety and depression are two distinct conditions that can occur at the same time. This can make symptoms more complex. However, the same treatments can address both problems. They can often improve with psychotherapy (talk therapy), drugs, or both.

This article describes the link between anxiety and depression. It also explains their symptoms, diagnosis, and treatment when they occur at the same time.

MementoJpeg / Getty Images

Anxiety and Depression: An Indirect or Direct Link?

The relationship between anxiety and depression is complex. While depression is typically regarded as a low-energy condition and anxiety a high-energy condition, these disorders and their symptoms commonly occur together. The reason they are often linked is well understood, though several potential factors exist.

Many of the same factors that predispose you to anxiety also make you vulnerable to depression. Both are considered internalizing disorders, problems that are developed and maintained to a great extent within the affected person.

Like other internalizing disorders, anxiety and depression are linked to similar factors that include genetic risk and neuroticism (the tendency toward negative thoughts). They are also associated with several shared nongenetic risk factors such as early trauma and current stress.

Anxiety and depression have many overlapping symptoms because they both involve changes in the function of neurotransmitters like serotonin in your brain. Your symptoms may meet the criteria of both disorders.

The relationship between anxiety and depression may not be a situation in which one causes the other, but the fact that they may be two sides of the same coin. Being depressed can often make you feel worried or anxious. Similarly, having an anxiety attack can make you feel hopeless with depression.

Related Causes/Risk Factors

While the exact causes of comorbid depression and anxiety are not known, the following risk factors increase your chances of having these disorders together:

  • Lifetime history of anxiety or depression
  • Adversity during childhood
  • Poor parenting
  • Recent major life events
  • Current exposure to stress
  • High neuroticism
  • Substance use disorders
  • Family history

How Anxiety and Depression Symptoms Feel

Symptoms of anxiety and depression can vary by individual. However, both disorders can cause symptoms that can interfere with daily life and interpersonal relationships.

Similarities

Symptoms common in both anxiety and depression include:

  • Problems with digestion
  • Unintended changes in appetite or weight
  • Inability to concentrate or make decisions
  • Problems sleeping, either too much or too little
  • Feeling constantly restless or irritable

Differences

Worrying is normal in some situations. Anxiety differs from normal worrying because it involves excessive fear that can be debilitating. Symptoms that may be characteristic of anxiety include:

  • Constantly feeling wound up or restless
  • Ongoing excessive worry about the immediate or long-term future
  • Focusing on negative outcomes when decision-making
  • Uncontrollable, racing thoughts about something going wrong
  • Avoiding situations that could cause worry and anxiety
  • Feeling a lack of certainty

The key characteristics of depression involve a persistent feeling of extremely low mood and/or loss of interest in activities you once enjoyed. Symptoms that may be characteristic of depression include:

  • Feelings of sadness and persistent low mood
  • Lack of interest or enjoyment in life experiences
  • Loss of energy or extreme fatigue
  • Increase in purposeless physical activities such as hand-wringing that is noticeable to others
  • Increase in slowed movements or speech that occur often enough to be noticed by others
  • Feelings of worthlessness or guilt
  • Emphasis on loss or deprivation
  • Thoughts of death or suicide

Help Is Available

If you or someone you know is having suicidal thoughts, call or text 988  to contact the  988 Suicide & Crisis Lifeline  and connect with a trained counselor. If you or they are in immediate danger, dial 911 .

For more mental health resources, see our  National Helpline Database .

Anxiety, Depression, or Both: How to Diagnose Symptoms

Many symptoms of anxiety and depression overlap, making it harder to determine which disorder is causing the problem. When anxiety and depression occur together, symptoms tend to be more intense and persistent because they work together. This can make your condition harder to diagnose and more complex to treat.

Diagnosing symptoms of a mental health disorder requires a comprehensive evaluation by a mental health provider. This can help ensure you get an accurate diagnosis and treatment.

Symptoms that might indicate that both anxiety and depression exist include:

  • Persistent irrational fears or worries
  • Physical symptoms like fatigue, headaches , labored breathing , abdominal pain , or rapid heartbeat
  • Persistent feelings of worthlessness or sadness
  • Problems going to sleep or staying asleep
  • Difficulty remembering or concentrating
  • Inability to make decisions
  • Loss of interest in hobbies or activities
  • Constantly feeling tired and cranky
  • Panic attacks or a sense of losing inner control
  • Inability to live in the moment and relax

Role of Gut Microbiome

Gut microbiome includes all the microorganisms living in your digestive system. It affects your digestive health as well as your overall health.

Research indicates that there is evidence of a link between gut microbes and depression. It is attributed to the gut and brain connection, called the gut-brain axis. Evidence shows that inflammation caused by gut microbes can influence mood in depression.

How to Cope With Comorbid Anxiety and Depression

There is no single treatment appropriate for every case of comorbid (co-occurring) anxiety and depression. Therapies typically include antidepressant drugs and/or a form of psychotherapy. Self-care can help you maintain your progress.

While research indicates that a combination of medication and therapy can provide the best results, your treatment plan may differ. Depending on your symptoms, you may be advised to start your treatment with either one of these therapies.

Self-care includes behaviors that support your physical and mental well-being. It involves actions that can help manage symptoms of anxiety and/or depression and complement therapy and/or medications.

The following strategies are ways to prioritize self-care:

  • Establish and maintain a regular exercise routine with a target of 30 minutes daily. Exercising for smaller amounts of time can also make a difference.
  • Follow a diet of nutritious meals and adequate hydration. Limit caffeinated beverages, alcohol, and added sugar.
  • Maintain proper sleep hygiene , which involves following a daily sleep schedule and other behaviors supporting a good night's sleep.
  • Try activities that involve relaxation, meditation, and breathing exercises to relieve stress and reduce feelings linked with anxiety and depression.
  • Remain connected with friends or family members you can count on to provide practical help and emotional support if needed.
  • Practice gratitude by journaling to remind yourself of the positive things in your life.
  • Establish goals and priorities to avoid taking on new tasks and responsibilities that can overwhelm you.

Therapy is regarded as a key part of treatment for symptoms that involve anxiety and/or depression. Your results and the time it takes to achieve them depend on your symptoms and your unique situation.

The following types of therapy are used to treat anxiety and depression:

  • Cognitive behavioral therapy (CBT) : This type of psychotherapy is considered the gold standard for treating anxiety and depression, among other mental health conditions.
  • CBT is a time-limited and goal-oriented therapy. It focuses on changing negative thought patterns by altering negative behaviors and emotions.
  • Interpersonal therapy (IPT) : This type of time-limited psychotherapy helps you see emotions as social signals so you can use them to improve interpersonal challenges. Rather than focusing on your past, IPT focuses on communication and current interpersonal relationships and issues you're having related to them.
  • Dialectical  behavioral therapy (DBT) : DBT is a modified version of CBT that focuses on healthy ways to live in the moment, regulate emotions, and improve interpersonal relationships. It integrates mindfulness skills, interpersonal effectiveness, distress tolerance, and emotion regulation into treatment.
  • Acceptance and commitment therapy (ACT) : ACT is a type of psychotherapy that focuses on mindfulness, remaining in the present, and strategies for behavioral changes. It focuses on helping you become psychologically flexible so you can accept difficult thoughts and emotions while committing to meaningful life activities consistent with your goals and values.

With Medication

Medication for anxiety and/or depression works by increasing the activity of neurotransmitters, like serotonin , dopamine , norepinephrine , and gamma-aminobutyric acid ( GABA ). These are the chemical messengers in your brain that affect mood regulation.

The type of medication you receive depends on your symptoms and other factors regarding your overall condition. The following classes of medications are commonly used:

Selective serotonin reuptake inhibitors (SSRIs) : SSRIs are the first-line treatments preferred for treating depression and many comorbid anxiety disorders. They work by increasing serotonin levels.

SSRIs include:

  • Celexa ( citalopram )
  • Lexapro ( escitalopram )
  • Paxil ( paroxetine )
  • Prozac ( fluoxetine )
  • Zoloft ( sertraline )

Serotonin-norepinephrine reuptake inhibitors (SNRIs) : SNRIs increase levels of serotonin and norepinephrine. These drugs are also acceptable first-line treatments for comorbid anxiety and depression.

SNRIs include:

  • Effexor ( venlafaxine )
  • Pristiq ( desvenlafaxine )
  • Cymbalta ( duloxetine )
  • Savella ( milnacipran ):
  • Fetzima ( levomilnacipran ):

Tricyclic antidepressants (TCAs) : TCAs boost levels of serotonin and norepinephrine. TCAs include:

  • Elavil ( amitriptyline )
  • Pamelor ( nortriptyline )
  • Tofranil ( imipramine )
  • Norpramin ( desipramine )
  • Anafranil ( clomipramine )

Monoamine oxidase inhibitors (MAOIs) : MAOIs were the first class of antidepressants. They are generally regarded as outdated because of their side effects, though they may be appropriate for treatment-resistant depression in its later stages.

MAOIs include:

  • Marplan ( isocarboxazid )
  • Nardil ( phenelzine )
  • Emsam ( selegiline patch)

Treatment-Resistant Depression (With Anxiety)

Treatment-resistant depression (with anxiety) describes depression that hasn't responded to an adequate trial of at least two different antidepressants. Research indicates that the situation is not uncommon. Between 29% and 46% of people with depression show partial or no response to treatments.

Therapies for treatment-resistant depression (with anxiety) involve the following:

  • Transcranial magnetic stimulation (TMS) : TMS is a noninvasive treatment that involves placing electromagnets on your head. The magnets send hundreds of thousands of targeted magnetic pulses to stimulate and reset the neurological processes regulating mood.
  • Electroconvulsive therapy (ECT) : ECT, previously known as electroshock therapy, is a procedure in which controlled electric currents are passed through your brain while you are under anesthesia. Treatment is usually given two or three times a week for six to 12 weeks, depending on your symptoms and response.
  • Ketamine : Ketamine has been used as an anesthetic in surgeries for many years. It is also used off-label for treatment-resistant depression. It works by targeting subsets of neurotransmitters that are different from those affected by traditional antidepressants. Ketamine is delivered by intravenous infusion (directly into your vein) in a procedure that takes up to an hour.
  • Spravato (esketamine): Esketamine is a ketamine formulation approved by the Food and Drug Administration (FDA) for depression. Esketamine is more potent than ketamine, so it may produce results with lower doses than ketamine. It is administered as an intranasal spray in monitored treatment sessions over a few weeks.

Feelings of sadness and worry are normal. However, when these types of feelings intrude on your daily life, they may be signs of mental health problems.

Anxiety and depression are two of the most commonly diagnosed mental health problems. While they are two distinct conditions, they often occur at the same time.

When these disorders occur together, treatments are more complex. Symptoms can overlap and often worsen when more than one mental health problem exists. The good news is that treating these comorbid disorders is most effective when they are handled at the same time.

National Association on Mental Illness NAMI. The comorbidity of anxiety and depression .

Hartgrove Behavioral Health System. The relationship between anxiety and depression .

Kalin NH. The critical relationship between anxiety and depression .  AJP . 2020;177(5):365-367. doi:10.1176/appi.ajp.2020.20030305

Hopwood M. Anxiety symptoms in patients with major depressive disorder: commentary on prevalence and clinical implications .  Neurol Ther . 2023;12(1):5-12. doi:10.1007/s40120-023-00469-6

Möller HJ, Bandelow B, Volz HP, Barnikol UB, Seifritz E, Kasper S. The relevance of ‘mixed anxiety and depression’ as a diagnostic category in clinical practice .  Eur Arch Psychiatry Clin Neurosci . 2016;266(8):725-736. doi:10.1007/s00406-016-0684-7

Cleveland Clinic Health Essentials. Anxiety vs. depression: which do I have (or is it both)?

Mental Health Foundation. Generalized anxiety disorder .

American Psychiatric Association. What is depression?

Pennisi E.  Gut microbe linked to depression in large health study .  Science . Published online February 4, 2022. doi:10.1126/science.ada0998

Harvard Health Publishing Harvard Medical School. Medication or therapy for depression? Or both?

National Institute of Mental Health. Caring for your mental health .

David D, Cristea I, Hofmann SG.  Why cognitive behavioral therapy is the current gold standard of psychotherapy .  Front Psychiatry . 2018;9. doi:10.3389/fpsyt.2018.00004

Coffey SF, Banducci AN, Vinci C.  Common questions about cognitive behavior therapy for psychiatric disorders .  Am Fam Physician . 2015;92(9):807-812. PMID: 26554473.

International Society of Interpersonal Psychotherapy. Overview of IPT .

The Linehan Institute Behavioral Tech.  What is dialectical behavior therapy (DBT)? .

Dindo L, Van Liew JR, Arch JJ. Acceptance and commitment therapy: a transdiagnostic behavioral intervention for mental health and medical conditions .  Neurotherapeutics . 2017;14(3):546-553. doi:10.1007/s13311-017-0521-3

Centre for Addiction and Mental Health (CAMH). Antidepressant medications .

Coplan JD, Aaronson CJ, Panthangi V, Kim Y. Treating comorbid anxiety and depression: Psychosocial and pharmacological approaches .  World Journal of Psychiatry . 2015;5(4):366. doi:10.5498/wjp.v5.i4.366

UpToDate. Patient education: medicines for depression (the basics) .

Columbia University Department of Psychiatry. Finding solutions when depression resists treatment .

UCSanDiego Health. Transcranial magnetic stimulation .

American Psychiatric Association. What is electroconvulsive therapy?

Nebraska Medicine. What is esketamine, and is it effective in treating depression?

Yale Medicine. How ketamine drug helps with depression .

By Anna Giorgi Anna Zernone Giorgi is a writer who specializes in health and lifestyle topics. Her experience includes over 25 years of writing on health and wellness-related subjects for consumers and medical professionals, in addition to holding positions in healthcare communications.

How exercise can treat and prevent common mental health issues like anxiety and depression

During the pandemic, Nikola Sowry made a decision that helped her become happier and healthier.

After feeling challenged and disconnected during recurring lockdowns, the 29-year-old decided to try out a community football team in Melbourne's inner suburbs. 

"Finding footy and this club genuinely changed my life," she said.

Before football, Nikola struggled to find exercise that suited her.

Nikola holds a football and claps surrounded by teammates at a weeknight training session

While she never had a diagnosed mental health condition, she credits the South Melbourne Districts team with transforming her physical and mental health.

"I'm just such a happier, healthy version of myself by being here," she said.

What Nikola experienced is backed by research. 

The link between mental health and physical activity is strong enough that studies are showing exercise can be used on its own as a treatment for mild to moderate depression or anxiety. 

A woman in a red footy jumper handballs a yellow football.

Physical activity has also been shown to prevent the onset of common mental health conditions in the first place.

With the latest figures pointing to declining mental wellbeing and an alarming rise in mental illness, particularly among younger Australians , experts say increasing the use of exercise for mental health should be part of the solution.

Exercise can change the brain, researchers say

Last year, a group of Australian researchers published a review  summarising what we know about the effects of physical activity on symptoms of depression, anxiety and mental distress in adults.

The scope of the study was large, and looked at previous reviews that captured the results of more than 1,000 trials involving 128,000 participants. It was peer-reviewed and published in the British Journal of Sports Medicine.

"What we found was that basically any type of exercise is effective for improving our mental health," said University of South Australia researcher Ben Singh.

A bearded man in a blue collared shirt sits on a park bench, with a serious expression.

The review found that using physical activity to treat mild to moderate depression and anxiety was more effective than conventional treatments like therapy.

"And on average, we found that it was about 1.5 times more effective than medications," Dr Singh said.

Exercise has also been shown to prevent the onset of mental disorders  like depression. 

"There is a lot of strong evidence to show that people who are regularly active over a long period of time have a lower rate of being diagnosed with a mental health condition," Dr Singh said.

Female footballer players high five each other on an oval at training

Part of this is due to the sense of community and achievement physical activity can provide, the research suggests.

Exercise   has also been shown to trigger structural and biological effects on the brain.

While there's still more to learn, exercise has been proven to help reduce brain inflammation, promote the growth of neurons and trigger the release of mood-boosting chemical messengers like serotonin.

And even a small amount of physical activity can help. 

From tai chi to swimming, all exercise can bring benefits

Dr Singh and his co-authors found all kinds of physical activity could help relieve the symptoms of depression and anxiety, or distress.

That included cardio such as walking, cycling, swimming, running or playing a team sport. 

A group of walkers walk up a dirt hill during a parkrun event.

Strength and resistance training was found to have the biggest impact on symptoms of depression.

Mind-body exercises like tai chi and yoga were most effective at reducing anxiety and were shown to help with symptoms of depression too, the study found.

Dr Singh said it was important people chose the type of exercise that suited them. 

In general, the review found the more vigorous the exercise was, the bigger the improvement in mental wellbeing.

"But what was important is we found that also low-intensity exercise — so just getting outdoors for a leisurely stroll — is still extremely beneficial," he said.

A checklist graphic for the use of exercise for mental health concerns. 

The national physical activity guidelines recommend adults aged 18 to 64 should aim to be active on most days, if not every day. The advice is to aim for 2.5 to 5 hours of moderate intensity physical activity and between 1.25 and 2.5 hours of vigorous physical activity a week.

For some people, that might sound like a lot.

But Dr Singh's research found even those doing less than 2.5 hours of physical activity per week experienced mental health benefits.

A young woman wearing a red footy jumpy braces herself to take a mark.

Exercise should be used more often for mental health conditions, researcher says

Jodie Sheehy, a PhD candidate with Melbourne's Victoria University, thinks exercise should be used more often to treat mental health conditions and promote mental wellbeing. 

Her current project is investigating how to encourage general practitioners to prescribe exercise specifically for mental health concerns.

"There's actually been a number of studies that look at GPs prescribing physical activity for mental health, and they really don't," she said.

A curly-haired woman wearing a blank singlet sits in a gym, surrounded by weights.

"Some recommend it, but they seldom prescribe it."

She said using physical exercise to treat mental health concerns was not a big part of the GP training curriculum, despite the fact most people saw their doctor more than any other mental health professional.

"What I would like to see happen is for there to be something specific, so that a GP can actually prescribe the exercise — the type, the dose and the frequency," she said.

Challenges for using exercise in mental health treatment

Caroline Johnson is a Melbourne GP who delivers mental health training to doctors wanting to become general practitioners. 

The Royal Australian College of General Practitioners said exercise was included in medical school curriculum on mental health. The college   also   produces resources for GPs on this topic .

Dr Johnson admitted it was a small mention in a "jam-packed" curriculum. 

"But most GPs know that exercise is good for depression. It's more about how do you deliver that message to the person in a way that will actually help them engage with it," she said.

An older woman wearing a red top and glasses is pictured  in her GP consulting room. She is smiling.

She said the more pressing issue was whether patients had the time, money or ability to actually do it.

"Depression really does affect your sense of self — you lose motivation, you lose interest in doing things and sometimes you even lose a belief that you're worth working on," Dr Johnson said.

She said it was easy to portray exercise as free and easy, but that was certainly not the case for people of different abilities or those who were time-poor. 

"If you've got low income, or you're not in an urban environment where walking is easy to do, where there's not parklands, those kinds of things, then that's a much harder thing for you to change," she said.

That's why it was important to view exercise in the context of other treatments for mental health issues, like therapy and medication, and recognise that it was not necessarily a solution for everyone, she said.

Overcoming barriers to exercise

One strategy for those who may be struggling to exercise is to seek advice from an exercise physiologist, if they can afford it.

Jason Gardner, an exercise physiologist on Victoria's Mornington Peninsula, said there were plenty of people living with mental illness or mental health concerns.

"And certainly not all of those are receiving support around exercise," he said.

An exercise physiologist wearing pants and a polo shirt talks to a client, who is walking on a treadmill.

He helps people manage injuries and chronic health issues, but some patients see him specifically for mental health concerns.

Subsidised exercise physiology services are not available under existing mental health care plans, which can help people access subsidised psychology. However they can be accessed through a chronic disease plan, also organised through a GP. 

Mr Gardner, Dr Johnson and Ms Sheehy all agreed that expanding access to exercise physiology could remove barriers for people who wanted to to use physical activity for mental health concerns, but were struggling to access it. 

"We often often paint exercise as something 'extra'," Mr Gardner said.

"But in many cases it's sufficient as a treatment on its own, or in addition to medication and psychological support."

  • X (formerly Twitter)

Related Stories

New study offers 'strongest evidence' yet that exercise helps prevent depression.

Two feet in running shoes leap in the air

Exercise is not just good for your heart, it helps your brain too. Here's how

Image of a man's torso and arms lifting weights

Exercise can prevent depression, study finds

A large group of people running up a city street

  • Exercise and Fitness
  • General Practice
  • Mental Health
  • Share full article

Advertisement

Supported by

Therapists Trade the Couch for the Great Outdoors

Mental health practitioners are hiking, camping and braving the elements with their clients — all in an effort to help them connect with the Earth, and with themselves.

Aimee Frazier with a former client, Chase Brockett, talk while sitting on a wooden bench as a forest surrounds them at a park in Portland, Ore.

By Christina Caron

Sometimes a pine cone is just a pine cone.

But on a January day, the rough edges of the cone — and the lone feather sticking out of it — meant something different to Rachel Oppenheimer, 25, a counselor at the Chesapeake Mental Health Collaborative in Towson, Md.

“Growing up, I had some challenges,” Ms. Oppenheimer said, referring to her prickly teenage past, “some struggles with managing my emotions.”

But her grandmother, who died four years ago, was soft like the feather, and gave her unconditional love that reminded Ms. Oppenheimer how important it was to treat herself with “soothing tenderness,” especially when she became self-critical.

Ms. Oppenheimer and her clinical supervisor, Heidi Schreiber-Pan, were visiting Talmar, a nonprofit farm that offers therapeutic programs and vocational training — a short drive from the busy road and nondescript strip malls near their office. At the farm, the only sounds were a burbling stream, trilling birds and several inches of snow crunching beneath their feet. It was the perfect location to teach Ms. Oppenheimer therapeutic techniques that make use of the natural world.

They set up camping chairs under a bright blue sky during their session — a makeshift office without walls — and discussed how to create a circular design called a mandala. Next they would arrange items that Ms. Oppenheimer found on the ground, each symbolizing the complex feelings that stemmed from mourning her grandmother.

Dr. Schreiber-Pan is one of a growing number of therapists who are taking their therapy sessions outdoors and, in some cases, training other counselors to do the same. They say that combining traditional talk therapy with nature and movement can help clients feel more open, find new perspectives and express their feelings, all while helping them connect with the outside world.

“It’s a sense of belonging to something bigger — and that is, I think, a really powerful ‘aha!’ moment for a lot of people ,” Dr. Schreiber-Pan said. As humans evolved they spent much of their time outdoors, she added, yet our modern life is mostly spent indoors, looking at digital devices.

Outdoor therapy falls under the umbrella of ecotherapy, a broad and nebulous term that includes activities as varied as equine therapy and outings like wilderness and adventure therapy. During the pandemic, while many therapists moved online, others held sessions outside, seeking a safer way to meet in person. But the concept has been around for much longer.

Decades ago, the psychiatrist Dr. Thaddeus Kostrubala, author of the 1976 book “The Joy of Running,” was known for jogging alongside his patients . The practice never really caught on, in part because most therapists were trained to meet with clients in controlled indoor settings, to maintain confidentiality and strong boundaries.

Now, however, students are being trained in ecotherapy at a smattering of schools, including Lewis and Clark College in Oregon and Prescott College in Arizona.

And some therapists, like Dr. Schreiber-Pan, are creating their own curriculums. In 2020, she founded the Center for Nature Informed Therapy, which offers certification and continuing education credits to any social worker or certified counselor who completes the program. So far, more than 100 people have graduated.

Outdoor sessions are not one size fits all. Not every client will want to walk in the snow, for example. Dr. Schreiber-Pan and other therapists also give clients the option to explore nature indoors, drawing from a collection of shells, stones, sticks and spiky gumballs. And there is no special license for this therapy — no established best practices that would dictate the exercises or activities that therapists should use when meeting with clients outdoors.

Some in the field are leery of the emerging discipline. Dr. Petros Levounis, the president of the American Psychiatric Association, said he would feel a bit “skeptical” about taking a patient to the park.

“There is a formality in psychotherapy — tried and true parameters,” he said. “You sit across from them; there’s the 45-minute session. And I don’t know exactly what would happen in the outdoors. It starts raining. What do you do with the patient?”

Psychiatrists need to think about it more carefully, he added, and consider special training “before we sign on the dotted line of such novel interventions.”

Even so, he added, a number of studies have found that being immersed in nature can be beneficial to mental health. A 2023 analysis of the effects of “ forest bathing ,” the Japanese practice of taking a relaxing walk through the woods, suggested that it can significantly reduce symptoms of depression and anxiety. And being physically active is associated with a lower risk of depression . One review of a variety of studies went so far as to conclude that “physical activity should be a mainstay approach” when managing psychological distress.

‘It connects me to being human’

Outdoor or nature-informed therapy has especially become a big draw for men and people under 40, Dr. Schreiber-Pan and other therapists said.

Chase Brockett, 36, who lives in Portland, Ore., began hiking therapy in 2022 and continued for about a year and a half, despite having to pay for sessions out of pocket.

“It connects me to being human, to being alive,” he said. “Not being subject to the world, but being a part of it.”

During his sessions, he and his therapist, Aimee Frazier, would go out in all kinds of weather, including rain.

“You have to be uncomfortable and just accept that’s what’s happening,” he said, a lesson that became an analogy for his anxiety. “I think a lot of anxiety comes from A) viewing anxiety as a bad thing and B) trying to escape it at all times,” he said.

Therapists also see other benefits: clients who are more receptive and relaxed.

“I think that for some young people, therapy feels very prescribed,” said Andrew Tepper, the founder of Boda Therapy, who often works with adolescents and young adults in New York City and the Catskills. “It’s one lane. Oh, we’re going to sit. We’re going to talk and maybe we’ll play a board game. And with that, I think, comes some resistance.”

Mr. Tepper, a psychotherapist, steers his clients toward outdoor movement — hiking or skiing — if they are receptive to it. During one retreat in early February, he took three clients snowshoeing, went on long walks and cooked lunch over a campfire.

“I believe therapy can be fun, and part of that is doing a little upfront assessment of what your clients like to do,” he said.

‘I began to feel a lot like my wilting office plant’

Therapists are noticing that a nature-informed practice can improve their own well-being and help to stave off professional burnout, too.

Years ago, when Ms. Frazier had finished a clinical internship in a dimly lit, windowless office, she realized that she needed a “more enlivening setting” — for her clients and for herself.

“I began to feel a lot like my wilting office plant that sat in the dark corner,” she said. “I longed to be out in the sun and the rain, surrounded by the calming presence of nature.”

In 2021, she began offering hiking therapy to clients under the supervision of Thomas J. Doherty , a Portland psychologist who founded the certificate program in ecotherapy at Lewis and Clark College. For some clients, she said, the setting makes therapy feel more approachable and less intimidating.

Maria Nazarian, a clinical psychologist in Santa Monica, Calif., does not rent an office. She sees clients only virtually or while walking on the beach, which she described as her “happy place.” And, she said, her clients have benefited from getting off the couch.

Walking side by side promotes collaboration, Dr. Nazarian said, and being on the shore often brings moments of wonder and awe, all of which help build “connectedness and trust.”

‘Winter has to happen’

Amy Fuggi, 63, has been seeing Dr. Schreiber-Pan on and off for six years to cope with grief over her mother’s death.

“You want to push it away — you want to bury it, you want to ignore it,” she said. “But that doesn’t work too well.”

While outside, she said, she feels a “huge connection” to her mother, who loved the outdoors and often planned camping trips for Ms. Fuggi and her siblings.

“I feel like she’s walking with me,” Ms. Fuggi said.

On a sunny Monday recently, she and Dr. Schreiber-Pan waded through the snow to visit a nearby college campus, disappearing into a tree-lined path near a small pond, where they played with the concept of wintering — the ability to lean into the dark times in our lives.

“They have a purpose, you know, just like winter has to happen for us to enjoy spring,” Dr. Schreiber-Pan said.

After the session, Ms. Fuggi said she felt lighter.

“When you’re walking around, you’ve got the fresh air and you’ve got all this openness ,” she said. “It’s very easy to just relax and talk about things.”

Rosem Morton contributed reporting for this story.

Christina Caron is a Times reporter covering mental health. More about Christina Caron

How to Be Happy

Happiness can predict health and longevity, but it doesn’t just happen to you..

Small changes in your behavior and surroundings can set you on course for happiness.  Here ’s how .

Our seven-day Happiness Challenge  will help you focus on a crucial element of living a good life — your relationships .

Finland has been ranked the happiest country on earth for six consecutive years. What’s the secret? The answer is complicated .

Cultivating a sense of wonder can be a salve  for a turbulent mind. Here is how to make it part of your everyday life .

It can seem impossible to be optimistic about the future. But these questions  will help you understand what all optimists have in common.

Exercise, even in small doses, can improve your mood. Try our eight-minute routine  that's based on movements researchers say are inspired by joy.

IMAGES

  1. (PDF) ACADEMIC STRESS AND DEPRESSION AMONG ADOLESCENT STUDENTS OF

    research paper about stress and depression

  2. (DOC) Depression Research Paper

    research paper about stress and depression

  3. College Stress Essay

    research paper about stress and depression

  4. 💄 Psychological reasons for depression research paper. Psychological

    research paper about stress and depression

  5. (PDF) Academic Stress and Depression among Adolescents: A Cross

    research paper about stress and depression

  6. (PDF) The impact of anxiety and depression on academic performance in

    research paper about stress and depression

VIDEO

  1. What Is Depression, Stress & Anxiety? #beyouunfiltered #depression #anxiety #mentalhealth

  2. Some of them the Solution for getting Stress,Depression

  3. Paper stress reliever. Kağıtdan stress giderici😉#shorts

  4. Case study of depression

  5. How to make paper stress reliever

  6. 3 Things that Actually Help with Anxiety and Depression According to Science #depression

COMMENTS

  1. The Effects of Psychological Stress on Depression

    Abstract. Major depressive disorder is a serious mental disorder that profoundly affects an individual's quality of life. Although the aetiologies underlying this disorder remain unclear, an increasing attention has been focused on the influence imposed by psychological stress over depression. Despite limited animal models of psychological ...

  2. The Critical Relationship Between Anxiety and Depression

    From the perspective of anxiety disorders, the lifetime comorbidity with depression is estimated to range from 20% to 70% for patients with social anxiety disorder ( 6 ), 50% for patients with panic disorder ( 6 ), 48% for patients with posttraumatic stress disorder (PTSD) ( 7 ), and 43% for patients with generalized anxiety disorder ( 8 ).

  3. Biological, Psychological, and Social Determinants of Depression: A

    The findings showed that there are a plethora of risk and protective factors (relating to biological, psychological, and social determinants) that are related to depression; these determinants are interlinked and influence depression outcomes through a web of causation.

  4. The Links Between Stress and Depression: Psychoneuroendocrinological

    The link between stressful life events and the origin and development of depression has been widely investigated, providing an increasing body of evidence supporting this association. 1 - 3 Environmental factors likely affect individuals in somewhat different manners, therefore triggering an adaptive response to stress, which depends on both psy...

  5. Stress, Anxiety, and Depression Among Undergraduate Students during the

    Stress, Anxiety, and Depression Among Undergraduate Students during the COVID-19 Pandemic and their Use of Mental Health Services Jungmin Lee, 1 Hyun Ju Jeong, 2 and Sujin Kim 3 Author information Article notes Copyright and License information PMC Disclaimer Associated Data Data Availability Statement Go to: Abstract

  6. From Stress to Depression: Bringing Together Cognitive and Biological

    Fig. 1. The pathways through which exposure to stressful life events leads to the onset or intensification of depression. In the context of stressor exposure, some individuals show cognitive and biological stress responses that contribute to symptoms of depression.

  7. Anxiety, Depression and Quality of Life—A Systematic Review of Evidence

    The World Health Organization [ 1] estimates that 264 million people worldwide were suffering from an anxiety disorder and 322 million from a depressive disorder in 2015, corresponding to prevalence rates of 3.6% and 4.4%.

  8. Stress generation in social anxiety and depression: A two-study

    Given the robust associations between depression and stress generation (Liu and Alloy, 2010), as well as high co-occurrence of social anxiety and depression at both the symptom and disorder levels (Belzer and Schneier, 2004; Heeren et al., 2018), we tested if observed stress generation effects in social anxiety were better explained by depression.

  9. Depression, Anxiety, and Stress as a Function of Psychological Strains

    Research paper. Depression, Anxiety, and Stress as a Function of Psychological Strains: Towards an Etiological Theory of Mood Disorders and Psychopathologies ... In estimating the prevalence rates, we defined the participants as having depression, anxiety, or stress disorders if their DASS-21 respective scale scores are larger or equal to 10, ...

  10. Systematic review and meta-analysis of depression, anxiety, and

    In all, 16 studies reported the prevalence of depression among a total of 23,469 Ph.D. students (Fig. 2; range, 10-47%).Of these, the most widely used depression scales were the PHQ-9 (9 studies ...

  11. The serotonin theory of depression: a systematic umbrella ...

    Joanna Moncrieff, Ruth E. Cooper, Tom Stockmann, Simone Amendola, Michael P. Hengartner & Mark A. Horowitz Molecular Psychiatry 28 , 3243-3256 ( 2023) Cite this article 1.21m Accesses 205 Citations...

  12. Anxiety and depression symptoms, and lack of emotional support among

    Anxiety and depression symptoms (ADS) Anxiety and depressive symptoms were examined in all four surveys using the 5-item Mental Health Index or Inventory (MHI-5; Means-Christensen et al., 2005; Ware and Sherbourne, 1992). Respondents were asked to rate their mental health during the past month on 6-point Likert scales (0 = never to 5 ...

  13. Treatment outcomes for depression: challenges and opportunities

    In the past decades, more than 500 randomised trials have examined the effects of antidepressant medications, and more than 600 trials have examined the effects of psychotherapies for depression (although comparatively few are conducted for early-onset depression).

  14. Frontiers

    Mobile phone addiction and sleep quality together played a serial mediating role in the influence of perceived academic stress on depression (B = 0.11, 95% boot CI = 0.08-0.14). Furthermore, the indirect effect (i.e., the mediating effect of mobile phone addiction and sleep quality) was significant and accounted for 64.01% of the total effect

  15. The neuroscience of depressive disorders: A brief review of the past

    Depression is a common and debilitating mental health condition whose underlying aetiology and pathophysiology is still relatively poorly understood. ... Implications for anxiety, depression, and social behaviors. Trends in Neurosciences 35(11): 649-659. Crossref. Google Scholar. Pariante CM, Lightman SL (2008) The HPA axis in major ...

  16. Major Depressive Disorder: Advances in Neuroscience Research and

    Analysis of published papers around the world from 2009 to 2019 in depressive disorder. A The total number of papers [from a search of the Web of Science database (search strategy: TI = (depression$) or ts = ("major depressive disorder$")) and py = (2009-2019), Articles)].B The top 10 countries publishing on the topic.C Comparison of papers in China and the USA.

  17. An Exploratory Study of Students with Depression in Undergraduate

    Stephanie Gardner, Monitoring Editor Published Online: 15 May 2020 https://doi.org/10.1187/cbe.19-11-0217 View article Tools Share Abstract Depression is a top mental health concern among undergraduates and has been shown to disproportionately affect individuals who are underserved and underrepresented in science.

  18. [PDF] Stress and depression.

    There is growing interest in moving away from unidirectional models of the stress-depression association, toward recognition of the effects of contexts and personal characteristics on the occurrence of stressors, and on the likelihood of progressive and dynamic relationships between stress and depression over time. Improved methods of assessment and research design have established a robust ...

  19. The Relationship Between Depression and Stress

    Key points. Research reveals that a wayward stress system attack on the brain can lead to depression. The legacy of our evolutionary and biological stress responses is a significant burden for ...

  20. Full article: The impact of stress on students in secondary school and

    Methods. A single author (MP) searched PubMed and Google Scholar for peer-reviewed articles published at any time in English. Search terms included academic, school, university, stress, mental health, depression, anxiety, youth, young people, resilience, stress management, stress education, substance use, sleep, drop-out, physical health with a combination of any and/or all of the preceding terms.

  21. A systematic review: the influence of social media on depression

    In the 13 studies, depression was the most commonly measured outcome. The prominent risk factors for depression, anxiety and psychological distress emerging from this review comprised time spent on social media, activities such as repeated checking for messages, personal investment, and addictive or problematic use.

  22. Depression, Anxiety, and Stress as a Function of Psychological Strains

    Methods: 6,305 college students (39.3% men; 60.7% women) from six Chinese provincial-level jurisdictions completed a paper-and-pencil survey with Psychological Strain Scales (PSS-40) and Depression, Anxiety, and Stress Scales-21 (DASS-21), both validated in Chinese populations.

  23. (PDF) Depression and anxiety

    353-360. 36 Rizzo M, Creed F, Goldberg D, et al. A systematic review of non- pharmacological treatments for dep ression in people with chronic phy sical health problems. J Psych osom Re s 2011; 71:...

  24. (PDF) Stress among students: An emerging issue

    Stress is an emotional imbalance which may occur due to various reasons such as tests, papers and projects, competitive nature within one's chosen field, financial worries about school and ...

  25. ACADEMIC STRESS AND DEPRESSION AMONG COLLEGE STUDENTS

    Academic stress is perceived as distress that will lead to recurring potentially negative behaviours and potentially harmful health outcomes such as depression (Skipworth, 2011). In this study ...

  26. Having Both Anxiety and Depression: How to Cope

    Cognitive behavioral therapy (CBT): This type of psychotherapy is considered the gold standard for treating anxiety and depression, among other mental health conditions. CBT is a time-limited and goal-oriented therapy. It focuses on changing negative thought patterns by altering negative behaviors and emotions.

  27. How exercise can treat and prevent common mental health issues like

    The review found that using physical activity to treat mild to moderate depression and anxiety was more effective than conventional treatments like therapy. "And on average, we found that it was ...

  28. Why Some Therapists Are Taking Their Clients Outdoors

    Chase Brockett, right, of Portland, Ore., began scheduling outdoor counseling sessions with the therapist Aimee Frazier to address his anxiety and depression and saw her for about a year and a half.