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Research Article

Anxiety, Affect, Self-Esteem, and Stress: Mediation and Moderation Effects on Depression

Affiliations Department of Psychology, University of Gothenburg, Gothenburg, Sweden, Network for Empowerment and Well-Being, University of Gothenburg, Gothenburg, Sweden

Affiliation Network for Empowerment and Well-Being, University of Gothenburg, Gothenburg, Sweden

Affiliations Department of Psychology, University of Gothenburg, Gothenburg, Sweden, Network for Empowerment and Well-Being, University of Gothenburg, Gothenburg, Sweden, Department of Psychology, Education and Sport Science, Linneaus University, Kalmar, Sweden

* E-mail: [email protected]

Affiliations Network for Empowerment and Well-Being, University of Gothenburg, Gothenburg, Sweden, Center for Ethics, Law, and Mental Health (CELAM), University of Gothenburg, Gothenburg, Sweden, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

• Ali Al Nima, 
• Patricia Rosenberg, 
• Trevor Archer, 
• Danilo Garcia

• Published: September 9, 2013
• https://doi.org/10.1371/journal.pone.0073265
• Reader Comments

23 Sep 2013: Nima AA, Rosenberg P, Archer T, Garcia D (2013) Correction: Anxiety, Affect, Self-Esteem, and Stress: Mediation and Moderation Effects on Depression. PLOS ONE 8(9): 10.1371/annotation/49e2c5c8-e8a8-4011-80fc-02c6724b2acc. https://doi.org/10.1371/annotation/49e2c5c8-e8a8-4011-80fc-02c6724b2acc View correction

Mediation analysis investigates whether a variable (i.e., mediator) changes in regard to an independent variable, in turn, affecting a dependent variable. Moderation analysis, on the other hand, investigates whether the statistical interaction between independent variables predict a dependent variable. Although this difference between these two types of analysis is explicit in current literature, there is still confusion with regard to the mediating and moderating effects of different variables on depression. The purpose of this study was to assess the mediating and moderating effects of anxiety, stress, positive affect, and negative affect on depression.

Two hundred and two university students (males  = 93, females  = 113) completed questionnaires assessing anxiety, stress, self-esteem, positive and negative affect, and depression. Mediation and moderation analyses were conducted using techniques based on standard multiple regression and hierarchical regression analyses.

Main Findings

The results indicated that (i) anxiety partially mediated the effects of both stress and self-esteem upon depression, (ii) that stress partially mediated the effects of anxiety and positive affect upon depression, (iii) that stress completely mediated the effects of self-esteem on depression, and (iv) that there was a significant interaction between stress and negative affect, and between positive affect and negative affect upon depression.

The study highlights different research questions that can be investigated depending on whether researchers decide to use the same variables as mediators and/or moderators.

Citation: Nima AA, Rosenberg P, Archer T, Garcia D (2013) Anxiety, Affect, Self-Esteem, and Stress: Mediation and Moderation Effects on Depression. PLoS ONE 8(9): e73265. https://doi.org/10.1371/journal.pone.0073265

Editor: Ben J. Harrison, The University of Melbourne, Australia

Received: February 21, 2013; Accepted: July 22, 2013; Published: September 9, 2013

Copyright: © 2013 Nima et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors have no support or funding to report.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Mediation refers to the covariance relationships among three variables: an independent variable (1), an assumed mediating variable (2), and a dependent variable (3). Mediation analysis investigates whether the mediating variable accounts for a significant amount of the shared variance between the independent and the dependent variables–the mediator changes in regard to the independent variable, in turn, affecting the dependent one [1] , [2] . On the other hand, moderation refers to the examination of the statistical interaction between independent variables in predicting a dependent variable [1] , [3] . In contrast to the mediator, the moderator is not expected to be correlated with both the independent and the dependent variable–Baron and Kenny [1] actually recommend that it is best if the moderator is not correlated with the independent variable and if the moderator is relatively stable, like a demographic variable (e.g., gender, socio-economic status) or a personality trait (e.g., affectivity).

Although both types of analysis lead to different conclusions [3] and the distinction between statistical procedures is part of the current literature [2] , there is still confusion about the use of moderation and mediation analyses using data pertaining to the prediction of depression. There are, for example, contradictions among studies that investigate mediating and moderating effects of anxiety, stress, self-esteem, and affect on depression. Depression, anxiety and stress are suggested to influence individuals' social relations and activities, work, and studies, as well as compromising decision-making and coping strategies [4] , [5] , [6] . Successfully coping with anxiety, depressiveness, and stressful situations may contribute to high levels of self-esteem and self-confidence, in addition increasing well-being, and psychological and physical health [6] . Thus, it is important to disentangle how these variables are related to each other. However, while some researchers perform mediation analysis with some of the variables mentioned here, other researchers conduct moderation analysis with the same variables. Seldom are both moderation and mediation performed on the same dataset. Before disentangling mediation and moderation effects on depression in the current literature, we briefly present the methodology behind the analysis performed in this study.

Mediation and moderation

Baron and Kenny [1] postulated several criteria for the analysis of a mediating effect: a significant correlation between the independent and the dependent variable, the independent variable must be significantly associated with the mediator, the mediator predicts the dependent variable even when the independent variable is controlled for, and the correlation between the independent and the dependent variable must be eliminated or reduced when the mediator is controlled for. All the criteria is then tested using the Sobel test which shows whether indirect effects are significant or not [1] , [7] . A complete mediating effect occurs when the correlation between the independent and the dependent variable are eliminated when the mediator is controlled for [8] . Analyses of mediation can, for example, help researchers to move beyond answering if high levels of stress lead to high levels of depression. With mediation analysis researchers might instead answer how stress is related to depression.

In contrast to mediation, moderation investigates the unique conditions under which two variables are related [3] . The third variable here, the moderator, is not an intermediate variable in the causal sequence from the independent to the dependent variable. For the analysis of moderation effects, the relation between the independent and dependent variable must be different at different levels of the moderator [3] . Moderators are included in the statistical analysis as an interaction term [1] . When analyzing moderating effects the variables should first be centered (i.e., calculating the mean to become 0 and the standard deviation to become 1) in order to avoid problems with multi-colinearity [8] . Moderating effects can be calculated using multiple hierarchical linear regressions whereby main effects are presented in the first step and interactions in the second step [1] . Analysis of moderation, for example, helps researchers to answer when or under which conditions stress is related to depression.

Mediation and moderation effects on depression

Cognitive vulnerability models suggest that maladaptive self-schema mirroring helplessness and low self-esteem explain the development and maintenance of depression (for a review see [9] ). These cognitive vulnerability factors become activated by negative life events or negative moods [10] and are suggested to interact with environmental stressors to increase risk for depression and other emotional disorders [11] , [10] . In this line of thinking, the experience of stress, low self-esteem, and negative emotions can cause depression, but also be used to explain how (i.e., mediation) and under which conditions (i.e., moderation) specific variables influence depression.

Using mediational analyses to investigate how cognitive therapy intervations reduced depression, researchers have showed that the intervention reduced anxiety, which in turn was responsible for 91% of the reduction in depression [12] . In the same study, reductions in depression, by the intervention, accounted only for 6% of the reduction in anxiety. Thus, anxiety seems to affect depression more than depression affects anxiety and, together with stress, is both a cause of and a powerful mediator influencing depression (See also [13] ). Indeed, there are positive relationships between depression, anxiety and stress in different cultures [14] . Moreover, while some studies show that stress (independent variable) increases anxiety (mediator), which in turn increased depression (dependent variable) [14] , other studies show that stress (moderator) interacts with maladaptive self-schemata (dependent variable) to increase depression (independent variable) [15] , [16] .

The present study

In order to illustrate how mediation and moderation can be used to address different research questions we first focus our attention to anxiety and stress as mediators of different variables that earlier have been shown to be related to depression. Secondly, we use all variables to find which of these variables moderate the effects on depression.

The specific aims of the present study were:

• To investigate if anxiety mediated the effect of stress, self-esteem, and affect on depression.
• To investigate if stress mediated the effects of anxiety, self-esteem, and affect on depression.
• To examine moderation effects between anxiety, stress, self-esteem, and affect on depression.

Ethics statement

This research protocol was approved by the Ethics Committee of the University of Gothenburg and written informed consent was obtained from all the study participants.

Participants

The present study was based upon a sample of 206 participants (males  = 93, females  = 113). All the participants were first year students in different disciplines at two universities in South Sweden. The mean age for the male students was 25.93 years ( SD  = 6.66), and 25.30 years ( SD  = 5.83) for the female students.

In total, 206 questionnaires were distributed to the students. Together 202 questionnaires were responded to leaving a total dropout of 1.94%. This dropout concerned three sections that the participants chose not to respond to at all, and one section that was completed incorrectly. None of these four questionnaires was included in the analyses.

Instruments

Hospital anxiety and depression scale [17] ..

The Swedish translation of this instrument [18] was used to measure anxiety and depression. The instrument consists of 14 statements (7 of which measure depression and 7 measure anxiety) to which participants are asked to respond grade of agreement on a Likert scale (0 to 3). The utility, reliability and validity of the instrument has been shown in multiple studies (e.g., [19] ).

Perceived Stress Scale [20] .

The Swedish version [21] of this instrument was used to measures individuals' experience of stress. The instrument consist of 14 statements to which participants rate on a Likert scale (0 =  never , 4 =  very often ). High values indicate that the individual expresses a high degree of stress.

Rosenberg's Self-Esteem Scale [22] .

The Rosenberg's Self-Esteem Scale (Swedish version by Lindwall [23] ) consists of 10 statements focusing on general feelings toward the self. Participants are asked to report grade of agreement in a four-point Likert scale (1 =  agree not at all, 4 =  agree completely ). This is the most widely used instrument for estimation of self-esteem with high levels of reliability and validity (e.g., [24] , [25] ).

Positive Affect and Negative Affect Schedule [26] .

This is a widely applied instrument for measuring individuals' self-reported mood and feelings. The Swedish version has been used among participants of different ages and occupations (e.g., [27] , [28] , [29] ). The instrument consists of 20 adjectives, 10 positive affect (e.g., proud, strong) and 10 negative affect (e.g., afraid, irritable). The adjectives are rated on a five-point Likert scale (1 =  not at all , 5 =  very much ). The instrument is a reliable, valid, and effective self-report instrument for estimating these two important and independent aspects of mood [26] .

Questionnaires were distributed to the participants on several different locations within the university, including the library and lecture halls. Participants were asked to complete the questionnaire after being informed about the purpose and duration (10–15 minutes) of the study. Participants were also ensured complete anonymity and informed that they could end their participation whenever they liked.

Correlational analysis

Depression showed positive, significant relationships with anxiety, stress and negative affect. Table 1 presents the correlation coefficients, mean values and standard deviations ( sd ), as well as Cronbach ' s α for all the variables in the study.

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https://doi.org/10.1371/journal.pone.0073265.t001

Mediation analysis

Regression analyses were performed in order to investigate if anxiety mediated the effect of stress, self-esteem, and affect on depression (aim 1). The first regression showed that stress ( B  = .03, 95% CI [.02,.05], β = .36, t  = 4.32, p <.001), self-esteem ( B  = −.03, 95% CI [−.05, −.01], β = −.24, t  = −3.20, p <.001), and positive affect ( B  = −.02, 95% CI [−.05, −.01], β = −.19, t  = −2.93, p  = .004) had each an unique effect on depression. Surprisingly, negative affect did not predict depression ( p  = 0.77) and was therefore removed from the mediation model, thus not included in further analysis.

The second regression tested whether stress, self-esteem and positive affect uniquely predicted the mediator (i.e., anxiety). Stress was found to be positively associated ( B  = .21, 95% CI [.15,.27], β = .47, t  = 7.35, p <.001), whereas self-esteem was negatively associated ( B  = −.29, 95% CI [−.38, −.21], β = −.42, t  = −6.48, p <.001) to anxiety. Positive affect, however, was not associated to anxiety ( p  = .50) and was therefore removed from further analysis.

A hierarchical regression analysis using depression as the outcome variable was performed using stress and self-esteem as predictors in the first step, and anxiety as predictor in the second step. This analysis allows the examination of whether stress and self-esteem predict depression and if this relation is weaken in the presence of anxiety as the mediator. The result indicated that, in the first step, both stress ( B  = .04, 95% CI [.03,.05], β = .45, t  = 6.43, p <.001) and self-esteem ( B  = .04, 95% CI [.03,.05], β = .45, t  = 6.43, p <.001) predicted depression. When anxiety (i.e., the mediator) was controlled for predictability was reduced somewhat but was still significant for stress ( B  = .03, 95% CI [.02,.04], β = .33, t  = 4.29, p <.001) and for self-esteem ( B  = −.03, 95% CI [−.05, −.01], β = −.20, t  = −2.62, p  = .009). Anxiety, as a mediator, predicted depression even when both stress and self-esteem were controlled for ( B  = .05, 95% CI [.02,.08], β = .26, t  = 3.17, p  = .002). Anxiety improved the prediction of depression over-and-above the independent variables (i.e., stress and self-esteem) (Δ R 2  = .03, F (1, 198) = 10.06, p  = .002). See Table 2 for the details.

https://doi.org/10.1371/journal.pone.0073265.t002

A Sobel test was conducted to test the mediating criteria and to assess whether indirect effects were significant or not. The result showed that the complete pathway from stress (independent variable) to anxiety (mediator) to depression (dependent variable) was significant ( z  = 2.89, p  = .003). The complete pathway from self-esteem (independent variable) to anxiety (mediator) to depression (dependent variable) was also significant ( z  = 2.82, p  = .004). Thus, indicating that anxiety partially mediates the effects of both stress and self-esteem on depression. This result may indicate also that both stress and self-esteem contribute directly to explain the variation in depression and indirectly via experienced level of anxiety (see Figure 1 ).

Changes in Beta weights when the mediator is present are highlighted in red.

https://doi.org/10.1371/journal.pone.0073265.g001

For the second aim, regression analyses were performed in order to test if stress mediated the effect of anxiety, self-esteem, and affect on depression. The first regression showed that anxiety ( B  = .07, 95% CI [.04,.10], β = .37, t  = 4.57, p <.001), self-esteem ( B  = −.02, 95% CI [−.05, −.01], β = −.18, t  = −2.23, p  = .03), and positive affect ( B  = −.03, 95% CI [−.04, −.02], β = −.27, t  = −4.35, p <.001) predicted depression independently of each other. Negative affect did not predict depression ( p  = 0.74) and was therefore removed from further analysis.

The second regression investigated if anxiety, self-esteem and positive affect uniquely predicted the mediator (i.e., stress). Stress was positively associated to anxiety ( B  = 1.01, 95% CI [.75, 1.30], β = .46, t  = 7.35, p <.001), negatively associated to self-esteem ( B  = −.30, 95% CI [−.50, −.01], β = −.19, t  = −2.90, p  = .004), and a negatively associated to positive affect ( B  = −.33, 95% CI [−.46, −.20], β = −.27, t  = −5.02, p <.001).

A hierarchical regression analysis using depression as the outcome and anxiety, self-esteem, and positive affect as the predictors in the first step, and stress as the predictor in the second step, allowed the examination of whether anxiety, self-esteem and positive affect predicted depression and if this association would weaken when stress (i.e., the mediator) was present. In the first step of the regression anxiety ( B  = .07, 95% CI [.05,.10], β = .38, t  = 5.31, p  = .02), self-esteem ( B  = −.03, 95% CI [−.05, −.01], β = −.18, t  = −2.41, p  = .02), and positive affect ( B  = −.03, 95% CI [−.04, −.02], β = −.27, t  = −4.36, p <.001) significantly explained depression. When stress (i.e., the mediator) was controlled for, predictability was reduced somewhat but was still significant for anxiety ( B  = .05, 95% CI [.02,.08], β = .05, t  = 4.29, p <.001) and for positive affect ( B  = −.02, 95% CI [−.04, −.01], β = −.20, t  = −3.16, p  = .002), whereas self-esteem did not reach significance ( p < = .08). In the second step, the mediator (i.e., stress) predicted depression even when anxiety, self-esteem, and positive affect were controlled for ( B  = .02, 95% CI [.08,.04], β = .25, t  = 3.07, p  = .002). Stress improved the prediction of depression over-and-above the independent variables (i.e., anxiety, self-esteem and positive affect) (Δ R 2  = .02, F (1, 197)  = 9.40, p  = .002). See Table 3 for the details.

https://doi.org/10.1371/journal.pone.0073265.t003

Furthermore, the Sobel test indicated that the complete pathways from the independent variables (anxiety: z  = 2.81, p  = .004; self-esteem: z  =  2.05, p  = .04; positive affect: z  = 2.58, p <.01) to the mediator (i.e., stress), to the outcome (i.e., depression) were significant. These specific results might be explained on the basis that stress partially mediated the effects of both anxiety and positive affect on depression while stress completely mediated the effects of self-esteem on depression. In other words, anxiety and positive affect contributed directly to explain the variation in depression and indirectly via the experienced level of stress. Self-esteem contributed only indirectly via the experienced level of stress to explain the variation in depression. In other words, stress effects on depression originate from “its own power” and explained more of the variation in depression than self-esteem (see Figure 2 ).

https://doi.org/10.1371/journal.pone.0073265.g002

Moderation analysis

Multiple linear regression analyses were used in order to examine moderation effects between anxiety, stress, self-esteem and affect on depression. The analysis indicated that about 52% of the variation in the dependent variable (i.e., depression) could be explained by the main effects and the interaction effects ( R 2  = .55, adjusted R 2  = .51, F (55, 186)  = 14.87, p <.001). When the variables (dependent and independent) were standardized, both the standardized regression coefficients beta (β) and the unstandardized regression coefficients beta (B) became the same value with regard to the main effects. Three of the main effects were significant and contributed uniquely to high levels of depression: anxiety ( B  = .26, t  = 3.12, p  = .002), stress ( B  = .25, t  = 2.86, p  = .005), and self-esteem ( B  = −.17, t  = −2.17, p  = .03). The main effect of positive affect was also significant and contributed to low levels of depression ( B  = −.16, t  = −2.027, p  = .02) (see Figure 3 ). Furthermore, the results indicated that two moderator effects were significant. These were the interaction between stress and negative affect ( B  = −.28, β = −.39, t  = −2.36, p  = .02) (see Figure 4 ) and the interaction between positive affect and negative affect ( B  = −.21, β = −.29, t  = −2.30, p  = .02) ( Figure 5 ).

https://doi.org/10.1371/journal.pone.0073265.g003

Low stress and low negative affect leads to lower levels of depression compared to high stress and high negative affect.

https://doi.org/10.1371/journal.pone.0073265.g004

High positive affect and low negative affect lead to lower levels of depression compared to low positive affect and high negative affect.

https://doi.org/10.1371/journal.pone.0073265.g005

The results in the present study show that (i) anxiety partially mediated the effects of both stress and self-esteem on depression, (ii) that stress partially mediated the effects of anxiety and positive affect on depression, (iii) that stress completely mediated the effects of self-esteem on depression, and (iv) that there was a significant interaction between stress and negative affect, and positive affect and negative affect on depression.

Mediating effects

The study suggests that anxiety contributes directly to explaining the variance in depression while stress and self-esteem might contribute directly to explaining the variance in depression and indirectly by increasing feelings of anxiety. Indeed, individuals who experience stress over a long period of time are susceptible to increased anxiety and depression [30] , [31] and previous research shows that high self-esteem seems to buffer against anxiety and depression [32] , [33] . The study also showed that stress partially mediated the effects of both anxiety and positive affect on depression and that stress completely mediated the effects of self-esteem on depression. Anxiety and positive affect contributed directly to explain the variation in depression and indirectly to the experienced level of stress. Self-esteem contributed only indirectly via the experienced level of stress to explain the variation in depression, i.e. stress affects depression on the basis of ‘its own power’ and explains much more of the variation in depressive experiences than self-esteem. In general, individuals who experience low anxiety and frequently experience positive affect seem to experience low stress, which might reduce their levels of depression. Academic stress, for instance, may increase the risk for experiencing depression among students [34] . Although self-esteem did not emerged as an important variable here, under circumstances in which difficulties in life become chronic, some researchers suggest that low self-esteem facilitates the experience of stress [35] .

Moderator effects/interaction effects

The present study showed that the interaction between stress and negative affect and between positive and negative affect influenced self-reported depression symptoms. Moderation effects between stress and negative affect imply that the students experiencing low levels of stress and low negative affect reported lower levels of depression than those who experience high levels of stress and high negative affect. This result confirms earlier findings that underline the strong positive association between negative affect and both stress and depression [36] , [37] . Nevertheless, negative affect by itself did not predicted depression. In this regard, it is important to point out that the absence of positive emotions is a better predictor of morbidity than the presence of negative emotions [38] , [39] . A modification to this statement, as illustrated by the results discussed next, could be that the presence of negative emotions in conjunction with the absence of positive emotions increases morbidity.

The moderating effects between positive and negative affect on the experience of depression imply that the students experiencing high levels of positive affect and low levels of negative affect reported lower levels of depression than those who experience low levels of positive affect and high levels of negative affect. This result fits previous observations indicating that different combinations of these affect dimensions are related to different measures of physical and mental health and well-being, such as, blood pressure, depression, quality of sleep, anxiety, life satisfaction, psychological well-being, and self-regulation [40] – [51] .

Limitations

The result indicated a relatively low mean value for depression ( M  = 3.69), perhaps because the studied population was university students. These might limit the generalization power of the results and might also explain why negative affect, commonly associated to depression, was not related to depression in the present study. Moreover, there is a potential influence of single source/single method variance on the findings, especially given the high correlation between all the variables under examination.

Conclusions

The present study highlights different results that could be arrived depending on whether researchers decide to use variables as mediators or moderators. For example, when using meditational analyses, anxiety and stress seem to be important factors that explain how the different variables used here influence depression–increases in anxiety and stress by any other factor seem to lead to increases in depression. In contrast, when moderation analyses were used, the interaction of stress and affect predicted depression and the interaction of both affectivity dimensions (i.e., positive and negative affect) also predicted depression–stress might increase depression under the condition that the individual is high in negative affectivity, in turn, negative affectivity might increase depression under the condition that the individual experiences low positive affectivity.

Acknowledgments

The authors would like to thank the reviewers for their openness and suggestions, which significantly improved the article.

Author Contributions

Conceived and designed the experiments: AAN TA. Performed the experiments: AAN. Analyzed the data: AAN DG. Contributed reagents/materials/analysis tools: AAN TA DG. Wrote the paper: AAN PR TA DG.

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The Critical Relationship Between Anxiety and Depression

• Ned H. Kalin , M.D.

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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 .

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• Cited by None

• Neuroanatomy
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• DOI: 10.1080/01411920701208258
• Corpus ID: 59457268

Researching academic stress and anxiety in students: some methodological considerations

• Published 9 March 2007
• Psychology, Education
• British Educational Research Journal

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• Neurobiol Stress
• v.11; 2019 Nov

Neurobiological links between stress and anxiety

Nuria daviu.

a Hotchkiss Brain Institute. Department of Physiology & Pharmacology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada

Michael R. Bruchas

b Department of Anesthesiology and Pain Medicine. Center for Neurobiology of Addiction, Pain, and Emotion. University of Washington. 1959 NE Pacific Street, J-wing Health Sciences. Seattle, WA 98195, USA

Bita Moghaddam

c Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, 97239, USA

Carmen Sandi

d Laboratory of Behavioral Genetics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Station 19, CH, 1015, Lausanne, Switzerland

Anna Beyeler

e Neurocentre Magendie, INSERM 1215, Université de Bordeaux, 146 Rue Léo Saignat, 33000 Bordeaux, France

Stress and anxiety have intertwined behavioral and neural underpinnings. These commonalities are critical for understanding each state, as well as their mutual interactions. Grasping the mechanisms underlying this bidirectional relationship will have major clinical implications for managing a wide range of psychopathologies. After briefly defining key concepts for the study of stress and anxiety in pre-clinical models, we present circuit, as well as cellular and molecular mechanisms involved in either or both stress and anxiety. First, we review studies on divergent circuits of the basolateral amygdala (BLA) underlying emotional valence processing and anxiety-like behaviors, and how norepinephrine inputs from the locus coeruleus (LC) to the BLA are responsible for acute-stress induced anxiety. We then describe recent studies revealing a new role for mitochondrial function within the nucleus accumbens (NAc), defining individual trait anxiety in rodents, and participating in the link between stress and anxiety. Next, we report findings on the impact of anxiety on reward encoding through alteration of circuit dynamic synchronicity. Finally, we present work unravelling a new role for hypothalamic corticotropin-releasing hormone (CRH) neurons in controlling anxiety-like and stress-induce behaviors. Altogether, the research reviewed here reveals circuits sharing subcortical nodes and underlying the processing of both stress and anxiety. Understanding the neural overlap between these two psychobiological states, might provide alternative strategies to manage disorders such as post-traumatic stress disorder (PTSD).

1. Introduction

Although the relationship between psychological stress and anxiety seems intuitive, the biological nuances that distinguish the two states are extremely complex. Indeed, after decades of research in psychology, ethology and neurophysiology, overlapping neural substrates of these two psychobiological states have been identified. However, the boundaries between stress and anxiety remain an open discussion.

A stress response, created by a real or perceived threat (stressor), can be defined as an emergency state of an organism in response to a challenge to its homeostasis ( Chrousos, 2009 ; Selye, 1936 ). During this emergency state, the organism initiates an integrated reaction including physiological and behavioral responses. Internal threats, or so-called systemic stressors, include physical changes in the body, such as hypoglycemia or hypovolemia (decreased blood volume), happening, for example, after a severe car accident. On the other hand, perceived threats, or so-called psychological stressors, include situations that can potentially lead to a danger and induce a homeostatic challenge, introducing the critical factor of anticipation ( de Kloet et al., 2005 ; Koolhaas, 2011 ). The concept of anticipation in the stress response is critical in understanding the relationship between stress and anxiety. In that regard, stress as a physiological reaction to a stimulus is accompanied by a concomitant emotional response. That emotional response is determined in part by the perception of the threat imminence ( Anderson and Adolphs, 2014 ; Davis et al., 2010 ). According to the definition of The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) ( American Psychiatric Association, 2013 ) “ Fear is the emotional response to a real or perceived imminent threat, whereas anxiety is the anticipation of a future threat”. Thus, the emotional state that our body experiences differs between fear when we encounter an aggressive dog, and anxiety when we know we will visit a friend which has an aggressive dog.

Anxiety is defined as a temporally diffused emotional state caused by a potentially harmful situation, with the probability or occurrence of harm being low or uncertain ( Goes et al., 2018 ; Spielberger et al., 1983 ; Takagi et al., 2018 ). Historically, psychologists and psychiatrists have differentiated state and trait anxiety ( Belzung and Griebel, 2001 ; Goes et al., 2018 ; Spielberger et al., 1983 ; Takagi et al., 2018 ). The diverging element of these two types of anxiety is their duration: state anxiety is an acute response to a potential threat, while trait anxiety is chronic, as it is expressed constantly during the life of the individual, and is therefore considered as a trait of an individual's personality ( Endler and Kocovski, 2001 ; Spielberger et al., 1983 ). State anxiety can be defined as hypervigilance in anticipation of a threat that can be triggered by acute stress, and has the primary function of avoiding dangerous situations and also to facilitate memory consolidation ( Roozendaal et al., 2008 ). On the other hand, trait anxiety is a predisposition of an individual to express constant anxiety, and increases the probability of state anxiety in potentially dangerous situations ( Endler and Kocovski, 2001 ; Spielberger et al., 1983 ). State and trait anxiety are not mutually exclusive, and state anxiety triggered by an event can be superimposed on trait anxiety. Importantly, both state and trait anxiety responses represent an evolutionary advantage to anticipate and avoid danger ( Goes et al., 2018 ; Spielberger et al., 1983 ; Takagi et al., 2018 ). Therefore, anxiety per se is not a pathological state, as it can prevent exposure to dangerous situations. However, when anxiety is sustained and/or elicited by non-threating stimuli, it becomes maladaptive ( Belzung and Griebel, 2001 ; Sylvers et al., 2011 ). While state and trait anxiety are essential psychological metrics to evaluate normal and pathological levels of anxiety, these metrics do not consider the neural substrate of anxiety, which partly explains the lack of new and effective therapies for anxiety disorders.

Over the past twenty years, human functional imaging has identified multiple brain areas including the hypothalamus, amygdala, prefrontal cortex and nuclei of the brainstem which are active during both stress and anxiety responses in healthy individuals ( Mobbs et al., 2007 ; Takagi et al., 2018 ). Interestingly, a subset of brain regions including the basolateral amygdala (BLA), medial prefrontal cortex (mPFC), locus coeruleus (LC), as well as reward processing areas such as the nucleus accumbens (NAc), appear to be affected in animal models of both stress disorders and anxiety disorders ( Calhoon and Tye, 2015 ; Etkin and Wager, 2007 ; Sailer et al., 2008 ; Shin and Liberzon, 2010 ). The intermingled neural circuits controlling both stress and anxiety suggests a strong bidirectional relationship between stress experiences and anxiety in both healthy and pathological conditions. Therefore, alterations of the connectivity between the brain regions influencing both stress and anxiety behaviors might contribute to the etiology of psychopathologies such as generalized anxiety disorder (GAD), social anxiety disorders or post-traumatic stress disorder (PTSD).

Herein, we summarize the views of the panel on Stress, Anxiety and Corticolimbic Pathways , presented at the 2018 Stress Neurobiology meeting, held in Banff, Canada. This perspective reflects the diverse and shared structures involved in both stress and anxiety responses. We aim to reveal common subcortical processes that could support the interplay between stress and anxiety-related behaviors.

2. Coding of emotional valence in the basolateral amygdala (BLA)

The attribution of emotional valence to sensory information is a key process that allows individuals to navigate the world, and has been shown to be altered in both anxiety and stress disorders ( Etkin and Wager, 2007 ; Sailer et al., 2008 ). Valence is the subjective value assigned to sensory stimuli, which determines subsequent behavior. Positive valence leads to approach and consummatory behaviors while negative valence leads to defensive and avoidance behaviors ( Pignatelli and Beyeler, 2019 ; Russell, 1980 ). Attentional bias for stimuli of negative valence have been extensively demonstrated in patients with anxiety disorders ( MacLeod et al., 2019 ). For example, anxiety increases negative interpretations of ambiguous sentences ( Richards and French, 1992 ) and scenarios ( Hirsch and Mathews, 1997 ) suggesting an anxiety-induced valence bias. Recent publications showed that high trait anxiety individuals exhibit a bias towards negative interpretations of surprised faces ( Park et al., 2016a ). The existence of a correlation between negative valence bias and the level of anxiety in health and disease observed in human studies supports the hypothesis that the circuits encoding emotional valence could be dysfunctional in anxiety disorders ( Etkin and Wager, 2007 ; Lammel et al., 2014 ; Mervaala et al., 2000 ; Pignatelli and Beyeler, 2019 ). A key structure encoding emotional valence and therefore guiding animal behavior is the basolateral nucleus of the amygdala (BLA). This region receives sensory inputs of multiple modalities, and projects to output structures controlling behavioral responses ( Janak and Tye, 2015 ; McDonald, 1998 ). This central connectivity has made the BLA a focus for identifying the neural substrate of valence processing. Moreover, a vast body of literature has shown that BLA integrity is critical for processing positive ( Bucy and Klüver, 1955 ; Tye et al., 2008 ; Weiskrantz, 1956 ) and negative valence ( Bucy and Klüver, 1955 ; LeDoux et al., 1990 ; McDonald, 1998 ; Tye et al., 2008 ; Weiskrantz, 1956 ).

Interestingly, studies have shown that when defined by their projection target, neurons of the BLA differentially control and encode emotional valence ( Fig. 1 A). A set of studies revealed that photostimulation of BLA neurons synapsing in the medial core/shell section of the NAc (BLA-NAc) support reward seeking ( Namburi et al., 2015 ). Meanwhile, BLA neurons projecting to the medial section of the central amygdala (BLA-CeA) mediate place avoidance ( Namburi et al., 2015 ). Importantly, these two different BLA populations have divergent responses to valence predicting cues. Indeed, single-unit in vivo recordings combined with optogenetic photoidentification indicated that a higher proportion of BLA-NAc neurons were excited by a cue predicting a reward, while BLA-CeA neurons showed a higher proportion of neurons excited by a stimulus predicting an aversive outcome ( Beyeler et al., 2016 ).

Valence coding in the basolateral amygdala (BLA) projector populations. A. Projector valence coding (adapted from Beyeler et al. 2016 ). a. Schematic of Pavlovian conditioning paradigm. Head-fixed mice were trained to discriminate between one cue paired with sucrose (CS–S) and a different cue paired with quinine (CS-Q). b. Peri-stimulus time histogram (PSTH) of the firing rates of representative units excited (top) or inhibited (bottom) during a CS-S presentation followed by a sucrose delivery. c. Fraction of BLA neurons excited or inhibited by CS-S, CS-Q or both. d. PSTH of action potentials of a BLA single-unit photoidentified as a BLA-NAc projector. e. Within-cell difference of response to CS-S and CS-Q depending on the neurons projection targets. f. Percentage of positive and negative valence units in the BLA. B. Behavioral impact of optogenetic activation of different BLA pathways (BLA-NAc and BLA-CeA projectors and BLA-vHPC terminals). C. Synaptic plasticity mechanism observed in BLA-NAc and BLA-CeA projection neurons after learning of valence associations. D. Topographic maps of three projectors populations in the BLA. CS: conditioned stimuli, S: sucorse, Q: quinine, NAc: nucleus accumbens, CeA: central amygdala, vHPC: ventral hippocampus.

Furthermore, the authors identified a synaptic mechanism for learning of valence associations. Specifically, synaptic inputs onto BLA-NAc neurons and BLA-CeA neurons undergo opposing synaptic changes following reward or fear conditioning ( Fig. 1 C, Namburi et al., 2015 ). Notably, BLA neurons projecting to those distinct areas are intermingled within the BLA, but are distributed following topographical gradients ( Fig. 1 D, Beyeler et al., 2018 ), which are correlated with a dorso-ventral bias of negative to positive valence coding ( Beyeler et al., 2018 ).

Another set of optogenetic experiments have shown that activation of the BLA projection to the ventral hippocampus (vHPC) is sufficient to induce real time anxiogenic effects and conversely, inhibition of those projections causes an anxiolytic effect ( Fig. 1 B, Felix-Ortiz et al., 2013 ). Single-unit recordings combined with optogenetic photoidentification have shown that BLA-vHPC neurons have no coding bias for learned stimuli predicting outcomes of positive or negative valence compared to the entire BLA ( Beyeler et al., 2016 ). This observation supports the idea that BLA-vHPC neurons may mediate anxiety-related behaviors which can be defined as an innate state of negative valence, rather than learned valence.

Altogether, the BLA is a single structure which includes neural populations that underlie processing of learned emotional valence (BLA-NAc and BLA-CeA) and a population that generates innate emotional states (BLA-vHPC). This finding suggests that BLA is a key structure to study how emotional states such as anxiety interfere with emotional valence processing.

3. Locus coeruleus noradrenergic (LC-NE) projections to BLA: acute stress-induced anxiety

Stressful experiences engage multiple structures to generate a coordinated physical and psychological response to a challenge. The locus coeruleus noradrenergic system (LC-NE) presents a brain-wide projection pattern ( Schwarz et al., 2015 ) and is linked to both physical and emotional responses to stress ( Berridge and Waterhouse, 2003 ; Valentino and Van Bockstaele, 2008 ), as well as aversive memory consolidation ( Roozendaal et al., 2008 ). Noradrenaline release during an acute stress induces a state anxiety response that allow the organism to maintain high attention, facilitate sensory processing and enhance executive functions in order to increase memory consolidation during stressful experiences ( Berridge and Waterhouse, 2003 ; Sara and Bouret, 2012 ).

Electrophysiological and optogenetic studies indicate that LC-NE neurons normally display three activation profiles: low tonic (1–2 Hz), high tonic (3–8 Hz) and phasic activity ( Carter et al., 2010 ). Acute stress causes a robust increase in tonic firing rate in LC-NE ( Valentino and Van Bockstaele, 2008 ) and this stress-induced tonic firing is associated with an increase in anxiety-like behavior. The role of tonic activation of LC-NE neurons is further supported by the observations that optogenetic stimulation of NE cell bodies in the LC, in the absence of a stressor, mimics LC-NE tonic activity as well as acute-stress induced anxiety ( McCall et al., 2015 ). Furthermore, they suggest that this increase of activity in LC-NE neurons is caused by synaptic inputs into the LC containing corticotropin releasing hormone (CRH + ). Specifically, stimulation of CRH + CeA-LC terminals increases activity in LC and drives anxiety-like behaviors through type 1 CRH receptor (CRH1R) activation ( McCall et al., 2015 ).

Acute stress also promotes anxiety and other stress related behaviors through BLA adrenergic receptor activation ( Chang and Grace, 2013 ). Even though the anatomical projections from LC and the role of NE in stress and anxiety have been studied extensively, the mechanism by which LC-NE influences BLA function to promote negative emotional states has only recently been unravelled. McCall and collaborators (2017) demonstrated that optogenetic activation of LC-NE fibers in the BLA in acute brain slices causes norepinephrine release into the BLA. In vivo photostimulation of these terminals modulates BLA activity, and LC-BLA stimulation is sufficient to cause conditioned place aversion as well as anxiety-like behaviors. These stimulation-induced behavioral changes require β-adrenergic receptor activity in the BLA, providing in vivo evidence that endogenous NE release from LC terminals alters BLA function and, as a consequence, modifies behavior. Additional support for β-adrenergic signaling promoting anxiety-like behaviors is provided in Siuda et al. (2015) , whereby selective optical activation of β-adrenergic signaling in CaMKII(+) neurons of the BLA produces robust anxiety-like phenotypes.

LC-NE neurons preferentially target neurons in the BLA that project to the ventral hippocampus (BLA-vHPC) and CeA (BLA-CeA), both downstream structures involved in negative valence and anxiety-related behaviors ( Beyeler et al., 2018 ; Felix-Ortiz et al., 2013 ; Namburi et al., 2015 ). This suggests that LC-NE projections to BLA increase anxiety-like behaviors following stress exposure, through projections to downstream structures such as CeA or vHPC ( Fig. 2 A). This recent work reveals that part of the circuit underlying acute-stress, induces state anxiety.

Circuit and molecular mechanisms of stress and anxiety. A. LC-NE projections to BLA increases anxiety-like behaviors acting on β-adrenergic receptors (βARs) and through projections to downstream structures such as the CeA. B. NAc mitochondrial function and anxiety, and its influence on social dominance. C. Circuit synchronicity between the PFC and VTA under different punishment probabilities. LC: locus coeruleus, BLA: basolateral amygdala, CeA: central amygdala, NAc: nucleus accumbens, PFC: prefrontal cortex, VTA: ventral tegmental area, NE: norepinephrine; ATP: adenosine triphosphate CRH: corticotropin-releasing hormone TH: tyrosine hydroxylase DBH: dopamine beta-hydroxylase Gal: galanin.

3.1. A new mitochondrial function linking stress and emotional traits

Even in our modern society, the necessity of positioning ourselves in a social group through social competition has an enormous impact on our daily lives. In spite of the importance that social competition has in organizing and structuring our society, the psychological characteristics that affect social competitiveness of an individual have been largely overlooked. Several brain regions such as the amygdala and the NAc have been implicated in social status and competition in both humans ( Zink et al., 2008 ), and rodents ( Goette et al., 2015 ). Recent studies have specifically revealed the critical role of the NAc in social competition and the establishment of social status ( Hollis et al., 2015 ; Larrieu et al., 2017 ; Van der Kooij et al., 2018 ). During social competition, D1-containing medium spiny neurons (MSNs) in the NAc are activated and show a positive correlation with the level of offensive behavior observed in a social hierarchy test. In addition, when the NAc, but not the BLA, is inactivated with a GABA A receptor agonist during a social competition test, rats showed reduced social dominance ( Hollis et al., 2015 ).

The NAc is involved in motivation and has been implicated in the regulation of anxiety and depressive-like symptoms ( Lüthi and Lüscher, 2014 ). The neural mechanism through which anxiety might affect social hierarchy has been poorly investigated. In humans, high-anxiety individuals tend to display subordinate roles and to be less competitive in social environments ( Gilbert et al., 2009 ). Likewise, high-anxiety rats show less social dominance after a social competition for a territory ( Hollis et al., 2015 ). These results are consistent with data obtained in humans where high anxiety traits predispose subjects for social submission ( Goette et al., 2015 ). These behavioral results, together with the new data revealing a unique role of the NAc in the establishment of social hierarchy open a new path to investigating how NAc function can bridge anxiety and social competition. In search of potential mechanisms within the NAc, which could differentiate low and high anxiety rats, Hollis et al. (2015) showed that high anxiety rats had lower mitochondrial activity in NAc, compared to low anxiety rats. Specifically, with similar mitochondrial numbers and density, highly anxious rats have lower levels of respiratory complexes I and II of the electron transport chain, resulting in a reduced mitochondrial function ( Fig. 2 B). Furthermore, social status also predicts behavioral stress susceptibility and metabolic profile in the NAc after chronic social defeat ( Larrieu et al., 2017 ). These studies establish a key role of mitochondrial function in individual differences that impact social dominance in a non-pathological condition. Altogether, the newly discovered role of mitochondrial energy metabolism in the NAc in anxiety-induced social deficits opens a new path for therapeutic treatment that targets cell metabolism.

In regards to the relationship between stress and anxiety, social competition itself induces an endocrine stress response ( Turan et al., 2015 ). In humans, stress exposure differentially affects low and high anxiety subjects in a competitive task. Under stressful conditions, low-anxiety individuals become overconfident, while high-anxiety individuals show less social confidence ( Goette et al., 2015 ). Moreover, several studies have reported increased risk of adult psychopathologies after early life adversity ( Haller et al., 2014 ; Maccari et al., 2014 ). In preclinical models, early life stress paradigms have been proposed as a tool to program or bias anxiety traits and, as a consequence, have negative impact in social competence ( Tzanoulinou and Sandi, 2017 ). Indeed, peri-pubertal stress leads to enhanced anxiety ( Cordero et al., 2016 ) and changes in social behavior in adulthood ( Haller et al., 2014 ). Interestingly, play-fighting is a peri-pubertal social behavior which has been linked to aggression in adulthood. Specifically, peri-puberal stress increases play-fighting and increases the chances to display abnormal aggressive behaviors later in adulthood ( Papilloud et al., 2018 ). Importantly, this study also revealed a role of mitochondrial energy balance in regulating stress-induced behaviors, by showing that enhanced play-fighting behaviors following peri-pubertal stress was accompanied by enhanced mitochondrial function in the amygdala.

4. Encoding reward-directed behavior under anxiety

In humans, patients suffering from anxiety disorders have impaired decision making and behavioral flexibility ( Park and Moghaddam, 2017a ). For example, high levels of anxiety are accompanied by difficulty of shifting between strategies in the presence of changes in task demand, and/or are easily distracted by irrelevant stimuli ( Eysenck et al., 2007 ). That inability to change strategies during a task can have detrimental consequences in a person, by affecting personal life and professional performance. The prefrontal cortex (PFC) is a pivotal structure in organizing behavior in a context dependent-manner ( Bechara et al., 2000 ; Miller and Cohen, 2001 ). Thus, during stress, the PFC controls high order adaptive responses such as choosing optimal behavioral output with an online evaluation of the situation ( Moghaddam, 2016 ).

In rodents, anxiety levels are also related to decreased cognitive flexibility ( Park and Moghaddam, 2017a ). Recent studies have revealed the functional consequence of anxiety upon PFC activity, by identifying that a negative emotional state elicits sustained reduction of spontaneous firing rate in the dorso-medial PFC (dmPFC) and orbitofrontal cortex (OFC, Park et al., 2016b ). This hypofrontality, and specifically the reduced activity in the dmPFC, is linked to decreased behavioral flexibility in the set-shifting task ( Park et al., 2016b ). In this task the subjects learn an instrumental behavioral response based on two different rules that sit on two different dimensions (for example: shape and color). The ability to switch between rules to maximize the profit (number of rewards) is PFC-dependent. The ventral tegmental area (VTA) is also a critical component of reward-guided behavior, and together with the PFC, has been proposed as a circuit underlying decisions during reward-seeking under punishment. VTA neurons are a key component of the reward circuit ( Morales and Margolis, 2017 ; Wood et al., 2017 ) and their projections to the mPFC are involved in regulating mood and emotional states ( Lammel et al., 2014 ).

Park and Moghaddam designed and developed a task to study the dynamics of reward-based behavior while risking potential punishment ( Park and Moghaddam, 2017b ). The task was designed to link instrumental action to reward, but at the same time, the same action will be followed by a punishment with varying probabilities. This task aims to recreate an environment where uncertainty of a negative outcome induces anxiety. The probability of getting punished affects behavioral performance by increasing the variability of the time to react, suggesting a transitory anxiety state caused by the possibility of being punished. Single-unit recordings from both dopaminergic (DA) VTA neurons, and mPFC neurons, during this task revealed that their firing rate around the motor response (time surrounding the action execution) is correlated with the punishment risk, suggesting both neural populations encode the risk probability. Even though both VTA-DA and non-DA neurons, as well as mPFC neurons, showed punishment risk encoding responses, VTA-DA neurons showed a higher temporal resolution around the action time than mPFC neurons, which showed a more diffuse response around the action window. Interestingly, the single-unit recordings did not correlate with the behavioral changes during the task, and a more detailed analysis revealed that the variability in the reaction time was related to a circuit dynamic. The correlation of the firing of VTA and mPFC neurons with the reaction time was evident only in the riskiest part of the session, when the probability to be punished was higher.

At the network level, the synchronicity of the theta oscillations is important to coordinate groups of neurons to complete a behavioral response ( Akam and Kullman, 2010 ; Buschman et al., 2012 ). Interestingly, during non-punished trials, the oscillations that emerged in the VTA and mPFC are in the theta range (around 8 Hz, Park and Moghaddam, 2017b ). Moreover, increased probability of punishment decreases the oscillations in both areas and weakens synchrony within and between both structures.

These studies revealed that synchrony between the VTA and PFC decreases with punishment probability, suggesting that VTA-PFC network encodes punishment risk ( Fig. 2 C). Under normal conditions, VTA drives oscillation synchronicity between both regions exerting a bottom-up control of the network. When there is risk of punishment, this bottom-up network control is diminished. Transient anxiety may, therefore, affect behavior in a reward-based task by disrupting the VTA-PFC functional circuit.

5. Role of hypothalamic corticotropin (PVN-CRH) neurons in stress-related behaviors

Although the relationship between stress and anxiety is bidirectional, the influence of stress as a risk factor for anxiety disorders has been extensively studied ( Armario et al., 2008 ; Tye and Deisseroth, 2012 ). Mapping of neural circuits involved in stress-induced anxiety have mostly revolved around structures of the amygdala and extended amygdala ( Davis et al., 2010 ; Gross and Canteras, 2012 ). Surprisingly, relatively less attention has been directed towards the paraventricular nucleus of the hypothalamus (PVN), which is a crucial component of the visceral stress response. The PVN receives and integrates information about the stressor and controls the endocrine, behavioral and autonomic response to stress ( Denver, 2009 ; Herman et al., 2003 ; Ulrich-Lai and Herman, 2009 ). Specifically, parvocellular neurosecretory cells release corticotropin-releasing hormone (CRH) into the anterior pituitary that stimulates the synthesis and release in the blood stream of adrenocorticotropic hormone (ACTH). Once ACTH reaches the adrenal glands it stimulates the release of glucocorticoid ( Denver, 2009 ; Herman et al., 2003 ; Ulrich-Lai and Herman, 2009 ). In the past five years, the classical view of PVN-CRH neurons has been challenged by demonstrations that these neurons also control multiple and complex behaviors that are linked to stress ( De Marco et al., 2016 ; Füzesi et al., 2016 ; Sterley et al., 2018 ; Zhang et al., 2017 ). These observations open an exciting line of research to investigate how this specific set of neurons can drive stress-induced behavioral alterations.

The standard methods to study anxiety in rodents have relied on established laboratory tests such as the elevated plus maze. However, over the last decade, an increasing number of studies have focused on developing new tools of behavioral analysis that are less invasive, require less subjective interpretations, and harken back to original ethological approaches ( Lezak et al., 2017 ). One of these approaches relies on monitoring freely-behaving mice in their home-cage environment without any intervention ( Fig. 3 A). Multiple behaviors such as grooming, freezing, rearing or surveying can be detected. Importantly, these behaviors show a specific but flexible temporal distribution. Using this approach, it was shown that a single stressful experience results in the emergence of an organized and structured behavioral pattern where self-directed behaviors such as grooming become predominant, taking over exploratory behaviors such as locomotion or rearing ( Fig. 3 B, Füzesi et al., 2016 ). The role of brainstem in generating stress-induced behavioral responses such as freezing ( Deng et al., 2016 ; Silva and McNaughton, 2019 ), grooming ( Kalueff et al., 2016 ), defensive behaviors or even anxiety-like behavior ( McCall et al., 2015 ), has been extensively studied over the years ( Myers et al., 2017 ). A recent study from Füzesi et al. (2016) proposes the contribution of a different structure, by revealing a new role for PVN-CRH neurons in coordinating the spontaneous behavioral patterns that emerge after acute stress. Specifically, optogenetic silencing of PVN-CRH activity during the post-stress period reduces grooming in a safe environment. On the contrary, activating PVN-CRH neurons modifies the intensity, but not necessarily the temporal sequence of context appropriate behaviors ( Fig. 3 C). The behavioral profile that arises after an acute stress experience is context dependent and related to PVN-CRH neuron activity.

PVN-CRH neurons coordinate behavioral patterns after stress (adapted from Füzesi et al., 2016 ) . A. Behavioral quantification of mice in their home-cage, before (naïve) and immediately after a footshock (stress). Eight distinct behaviors are prominent. Each row represents one mouse. B. Histogram of grooming behavior at each time-point for naïve and stress mice. C. Schematic of in vivo photostimulation of PVN-CRH to LH projections (20 Hz, 5 min) increases grooming time and stimulated CORT release. PVN: paraventricular nucleus of the hypothalamus CRH: corticotropin-releasing hormone LH: lateral hypothalamus ME: median eminence CORT: corticosterone.

Further work on PVN-CRH has revealed that these cells are necessary and sufficient for stress-induced social investigation that is required for the transmission of stress from one individual to another ( Sterley et al., 2018 ). This social transmission of stress results in changes in synaptic function and metaplasticity in the recipient mice, which mirror the synaptic changes observed in stressed mice. This study positions PVN-CRH neurons as a critical node for alarm signal processing and offers a potential explanation for how individuals who have not had a first-hand traumatic experience can develop symptomology consistent with post-traumatic stress disorders ( Haugen et al., 2012 ; Sial et al., 2016 ).

Recent studies have shown that PVN-CRH neurons are able to respond differentially to stimuli with positive or negative valence ( Kim et al., 2019 ; Yuan et al., 2019 ). Using in vivo fiber photometry to monitor population calcium dynamics, Kim et al. (2019) have shown that the activity of PVN-CRH neurons rapidly increases in response to stimuli with negative valence, and decreases when an appetitive stimulus such as food or social interaction is presented. Moreover, a reward presentation during stress can buffer PVN-CRH activation, and, as a consequence, modify stress-related behaviors, such as grooming, correlate with the activity of those neurons ( Yuan et al., 2019 ). The bidirectional response of PVN-CRH neurons based on stimulus valence opens a new perspective to study stress coping and relieve.

Beyond CRH neurons, a distinct neuronal population in the PVN containing CRH1R has been recently described. This population is 90% GABAergic and has direct synaptic contact with PVN-CRH neurons, and CRH, released from PVN-CRH neurons, modulates signaling between both populations ( Jiang et al., 2018 ; Ramot et al., 2017 ). This intra-PVN network of CRH1R+ and CRF + neurons has been associated with anxiety-like behavior, as knocking out CRH1R from PVN neurons has anxiolytic effects ( Ramot et al., 2017 ; Zhang et al., 2017 ).

All these new data are contributing to update the role of hypothalamic neural populations in stress-related behaviors. Specifically, growing evidence implicates PVN-CRH neurons in modulating specific behaviors in a stress-related context. From controlling how an individual responds to stress exposure ( Füzesi et al., 2016 ; Kim et al., 2019 ; Yuan et al., 2019 ) to regulating anxiety-like behaviors ( Ramot et al., 2017 ; Zhang et al., 2017 ), or driving social transmission of stress ( Sterley et al., 2018 ), the PVN-CRH neuron population appears to be a central player linking stress and anxiety.

6. From research to clinics: new categorization of post-traumatic stress disorder (PTSD)

The idea that stress and anxiety have segregated neurobiological substrates despite their reciprocal influence has now moved beyond the research field to reach the clinical practice, resulting in changes in diagnostic tools. Specifically, according to the DSM-5 ( American Psychiatric Association, 2013 ) PTSD is not classified as an anxiety disorder anymore, and the new manual categorizes it among the trauma or stressor-related disorders . This new category requires explicitly an exposure to a traumatic or stressful event as a diagnostic criterion, and in particular, the PTSD requires a life threatening experience. Reclassifying PTSD from anxiety disorders to this newly created trauma or stressor-related disorders category has helped to move the focus away from anxiety, which is now rather considered as a comorbid pathology. The new classification in DSM-5 also emphasizes the abnormal reactivity to a stimulus. Interestingly, an altered “flight” response has been observed in patients with PTSD ( Park et al., 2017 ) and, in the last five years, the interest in how microcircuits controlling threat processing are altered in PTSD has grown considerably. This emergent literature supports the idea that aberrant subconscious threat-related processes are underlying part of the PTSD symptomatology ( Lanius et al., 2017 ). Current data indicates an increased connectivity between areas involved in the innate alarm system, such as the LC, amygdala, hypothalamus, and PFC in PTSD patients ( Rabellino et al., 2016 ). In humans, when a challenge is not life-threating, the subjects choose the most suitable cost-effective strategy to overcome that situation by engaging the vmPFC, medial orbitofrontal cortex (mOFC) and other non-cortical structures including the BLA which is causally involved in valence processing. However, when the level of danger is life threatening and the organism must prepare for a defensive response, subcortical structures, such as the periaqueductal gray area (PAG) and CeA overrule the cognitive control, and guide the behavioral response ( Mobbs et al., 2007 ). The balance between cortical and subcortical systems could also cast out key points to understand some anxiety disorders. Negative emotional states such as anxiety may lead a person to overestimate the possibility of danger, leading to a shift in the balance of these two systems. The dynamics between the cognitive and innate circuitry in response to a challenge may reveal a path to a better comprehension of how stressful experiences influence our emotional state, and in turn, shape our future behaviors.

7. Perspective

Although the dissection of multiple brain circuits controlling anxiety- and stress-related behaviors has made substantial advances, our understanding of the neural substrates underlying the interplay between these two psychophysiological responses remains fragmented. Here, we described specific roles of neural populations in the amygdala, nucleus accumbens, prefrontal cortex, hypothalamus and locus coeruleus in emotional valence, stress and anxiety. The work described here is providing foundational knowledge, however future investigation is necessary to unravel the contribution of specific circuits to stress and/or anxiety. Specifically, future work should use activity dependent mapping and recordings of genetically or anatomically defined neural populations during stress exposure and at different levels of anxiety within the same animal. Beyond the technical advances catalyzing our understanding of the neural circuits underpinning stress and anxiety, disentangling them will require the development of new behavioral paradigms in pre-clinical models in order to finely capture the changes of neural coding in these two conditions.

Conflicts of interest

The authors have no conflict of interest to declare.

Acknowledgments

We thank Matt Hill and Jaideep Bains for organizing the 2018 Stress Neurobiology Workshop, and for their invitation to write this review to summarize the major findings presented during the sessions including the authors of this review. ND is supported by Fellowships from Alberta Innovates-Health Solutions. We acknowledge the support of the Région Nouvelle-Aquitaine and the INSERM-Avenir program of the French NIH to the Beyeler Lab and of the Brain and Behavior Research Foundation NARSAD young investigator grant to AB.

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Post-Pandemic Depression, Anxiety, and Stress: A Look at the Mental Health of Nursing and Administrative Staff

14 Pages Posted: 25 Jun 2024

Pacheco-Flores Laura Iraís

Universidad de las Americas Puebla

Pineda-Téllez Magno

affiliation not provided to SSRN

Erika Ramos-Tovar

Some research has shown how mental health was affected during the COVID-19 pandemic in hospital staff worldwide. However, there is little evidence of the physiological status of healthcare and administrative workers at the first level of medical care. Therefore, it is necessary to identify mental health problems among hospital staff once this pandemic has passed. This study aims to determine the prevalence of depression, anxiety, and post-pandemic stress in personnel in the medical and administrative workers.This research is an observational and cross-sectional study of the medical and administrative workers of the clinic who participated voluntarily through the application of the DASS-21 questionnaire to determine the prevalence and severity of depression, anxiety, and stress. The 190 participants had a mean age of 49 ± 11 years. The prevalence of depression was 20% with a predominantly moderate severity index (38.46%), 33% of anxiety with a predominant extremely severe index (36.51%), and 30% of stress with a predominantly moderate severity index (36.84%), related to the healthcare personnel and particularly in the nursing staff and administrative area. In conclusion, it was identified that post-pandemic depression, anxiety, and stress in the population studied, nursing staff showed an intensity of extremely severe anxiety, the administrative area had depression with severe stress, and workers with different responsibilities but focused on the operation of the hospital. However, additional studies are required to evaluate appropriate management strategies to diagnose, treat, and prevent mental health disorders among hospital staff.

Note: Funding Information: None declared. Declaration of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics Approval Statement: This study did not represent a bioethical risk and was carried out with the approval of the unit authorities after evaluation and ruling by the Research and Research Ethics Committee of the ISSSTE Puebla Regional Hospital, with registration number 490.2023. Written informed consent was obtained.

Keywords: Depression, anxiety, stress, medical area, administrative area, nursing staff

Suggested Citation: Suggested Citation

Universidad de las Americas Puebla ( email )

Affiliation not provided to ssrn ( email ).

No Address Available

Erika Ramos-Tovar (Contact Author)

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Do you often find yourself worrying about everyday issues for no obvious reason? Are you always waiting for disaster to strike or excessively worried about things such as health, money, family, work, or school?

If so, you may have a type of anxiety disorder called generalized anxiety disorder (GAD). GAD can make daily life feel like a constant state of worry, fear, and dread. The good news is GAD is treatable. Learn more about the symptoms of GAD and how to find help.

What is generalized anxiety disorder?

Occasional anxiety is a normal part of life. Many people may worry about things such as health, money, or family problems. But people with GAD feel extremely worried or nervous more frequently about these and other things—even when there is little or no reason to worry about them. GAD usually involves a persistent feeling of anxiety or dread that interferes with how you live your life. It is not the same as occasionally worrying about things or experiencing anxiety due to stressful life events. People living with GAD experience frequent anxiety for months, if not years.

GAD develops slowly. It often starts around age 30, although it can occur in childhood. The disorder is more common in women than in men.

What are the signs and symptoms of generalized anxiety disorder?

People with GAD may:

• Worry excessively about everyday things
• Have trouble controlling their worries or feelings of nervousness
• Know that they worry much more than they should
• Feel restless and have trouble relaxing
• Have a hard time concentrating
• Startle easily
• Have trouble falling asleep or staying asleep
• Tire easily or feel tired all the time
• Have headaches, muscle aches, stomachaches, or unexplained pains
• Have a hard time swallowing
• Tremble or twitch
• Feel irritable or "on edge"
• Sweat a lot, feel lightheaded, or feel out of breath
• Have to go to the bathroom frequently

Children and teens with GAD often worry excessively about:

• Their performance in activities such as school or sports
• Catastrophes, such as earthquakes or war
• The health of others, such as family members

Adults with GAD are often highly nervous about everyday circumstances, such as:

• Job security or performance
• The health and well-being of their children or other family members
• Completing household chores and other responsibilities

Both children and adults with GAD may experience physical symptoms such as pain, fatigue, or shortness of breath that make it hard to function and that interfere with daily life.

Symptoms may fluctuate over time and are often worse during times of stress—for example—with a physical illness, during school exams, or during a family or relationship conflict.

What causes generalized anxiety disorder?

Risk for GAD can run in families. Several parts of the brain and biological processes play a key role in fear and anxiety. By learning more about how the brain and body function in people with anxiety disorders, researchers may be able to develop better treatments. Researchers have also found that external causes, such as experiencing a traumatic event or being in a stressful environment, may put you at higher risk for developing GAD.

How is generalized anxiety disorder treated?

If you think you’re experiencing symptoms of GAD, talk to a health care provider. After discussing your history, a health care provider may conduct a physical exam to ensure that an unrelated physical problem is not causing your symptoms. A health care provider may refer you to a mental health professional, such as a psychiatrist, psychologist, or clinical social worker. The first step to effective treatment is to get a diagnosis, usually from a mental health professional.

GAD is generally treated with psychotherapy (sometimes called “talk therapy”), medication, or both. Speak with a health care provider about the best treatment for you.

Psychotherapy

Cognitive behavioral therapy (CBT), a research-supported type of psychotherapy, is commonly used to treat GAD. CBT teaches you different ways of thinking, behaving, and reacting to situations that help you feel less anxious and worried. CBT has been well studied and is the gold standard for psychotherapy.

Another treatment option for GAD is acceptance and commitment therapy (ACT). ACT takes a different approach than CBT to negative thoughts and uses strategies such as mindfulness and goal setting to reduce your discomfort and anxiety. Compared to CBT, ACT is a newer form of psychotherapy treatment, so less data are available on its effectiveness. However, different therapies work for different types of people, so it can be helpful to discuss what form of therapy may be right for you with a mental health professional.

For more information on psychotherapy, visit the National Institute of Mental Health (NIMH) psychotherapies webpage .

Health care providers may prescribe medication to treat GAD. Different types of medication can be effective, including:

• Antidepressants, such as selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs)
• Anti-anxiety medications, such as benzodiazepines

SSRI and SNRI antidepressants are commonly used to treat depression, but they also can help treat the symptoms of GAD. They may take several weeks to start working. These medications also may cause side effects, such as headaches, nausea, or difficulty sleeping. These side effects are usually not severe for most people, especially if the dose starts off low and is increased slowly over time. Talk to your health care provider about any side effects that you may experience.

Benzodiazepines, which are anti-anxiety sedative medications, also can be used to manage severe forms of GAD. These medications can be very effective in rapidly decreasing anxiety, but some people build up a tolerance to them and need higher and higher doses to get the same effect. Some people even become dependent on them. Therefore, a health care provider may prescribe them only for brief periods of time if you need them.

Buspirone is another anti-anxiety medication that can be helpful in treating GAD. Unlike benzodiazepines, buspirone is not a sedative and has less potential to be addictive. Buspirone needs to be taken for 3–4 weeks for it to be fully effective.

Both psychotherapy and medication can take some time to work. Many people try more than one medication before finding the best one for them. A health care provider can work with you to find the best medication, dose, and duration of treatment for you.

For basic information about these and other mental health medications, visit NIMH’s Mental Health Medications webpage . Visit the U.S. Food and Drug Administration (FDA) website  for the latest warnings, patient medication guides, and information on newly approved medications. 

Support Groups

Some people with anxiety disorders might benefit from joining a self-help or support group and sharing their problems and achievements with others. Support groups are available both in person and online. However, any advice you receive from a support group member should be used cautiously and does not replace treatment recommendations from a health care provider.

Healthy Habits

Practicing a healthy lifestyle also can help combat anxiety, although this alone cannot replace treatment. Researchers have found that implementing certain healthy choices in daily life—such as reducing caffeine intake and getting enough sleep—can reduce anxiety symptoms when paired with standard care—such as psychotherapy and medication.

Stress management techniques, such as exercise, mindfulness, and meditation, also can reduce anxiety symptoms and enhance the effects of psychotherapy. You can learn more about how these techniques benefit your treatment by talking with a health care provider.

To learn more ways to take care of your mental health, visit NIMH’s Caring for Your Mental Health webpage .

How can I support myself and others with generalized anxiety disorder?

Educate yourself.

A good way to help yourself or a loved one who may be struggling with GAD is to seek information. Research the warning signs, learn about treatment options, and keep up to date with current research.

Communicate

If you are experiencing GAD symptoms, have an honest conversation about how you’re feeling with someone you trust. If you think that a friend or family member may be struggling with GAD, set aside a time to talk with them to express your concern and reassure them of your support.

Know When to Seek Help

If your anxiety, or the anxiety of a loved one, starts to cause problems in everyday life—such as at school, at work, or with friends and family — it’s time to seek professional help. Talk to a health care provider about your mental health.

Are there clinical trials studying generalized anxiety disorder?

NIMH supports a wide range of research, including clinical trials that look at new ways to prevent, detect, or treat diseases and conditions—including GAD. Although individuals may benefit from being part of a clinical trial, participants should be aware that the primary purpose of a clinical trial is to gain new scientific knowledge so that others may be better helped in the future.

Researchers at NIMH and around the country conduct clinical trials with patients and healthy volunteers. Talk to a health care provider about clinical trials, their benefits and risks, and whether one is right for you. For more information, visit NIMH's clinical trials webpage .

Finding Help

Behavioral health treatment services locator.

This online resource, provided by the Substance Abuse and Mental Health Services Administration (SAMHSA), helps you locate mental health treatment facilities and programs. Find a facility in your state by searching SAMHSA’s online Behavioral Health Treatment Services Locator  . For additional resources, visit NIMH's Help for Mental Illnesses webpage .

Talking to a Health Care Provider About Your Mental Health

Communicating well with a health care provider can improve your care and help you both make good choices about your health. Find tips to help prepare for and get the most out of your visit at Taking Control of Your Mental Health: Tips for Talking With Your Health Care Provider . For additional resources, including questions to ask a provider, visit the Agency for Healthcare Research and Quality website  .

If you or someone you know is in immediate distress or is thinking about hurting themselves, call the National Suicide Prevention Lifeline toll-free at 1-800-273-TALK (8255). You also can text the Crisis Text Line (HELLO to 741741) or use the Lifeline Chat on the National Suicide Prevention Lifeline website   .

The information in this publication is in the public domain and may be reused or copied without permission. However, you may not reuse or copy images. Please cite the National Institute of Mental Health as the source. Read our copyright policy to learn more about our guidelines for reusing NIMH content.

For More Information

MedlinePlus  (National Library of Medicine) ( en español  )

ClinicalTrials.gov  ( en español  )

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health NIH Publication No. 22-MH-8090 Revised 2022