Maladaptive emotion regulation traits predict altered corticolimbic recovery from psychosocial stress MURRAY, Ryan, et al.

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(1)Article. Maladaptive emotion regulation traits predict altered corticolimbic recovery from psychosocial stress MURRAY, Ryan, et al.. Abstract Background: Adaptive recovery from stress promotes healthy cognitive affective functioning, whereas maladaptive recovery is linked to poor psychological outcomes. Neural regions, like the anterior cingulate and hippocampus, play critical roles in psychosocial stress responding and serve as hubs in the corticolimbic neural system. To date, however, it is unknown how cognitive emotion regulation traits (cER), adaptive and maladaptive, influence corticolimbic stress recovery. Here, we examined acute psychosocial stress neural recovery, accounting for cER. Methods: Functional neuroimaging data were collected while forty-seven healthy participants performed blocks of challenging, time-sensitive, mental calculations. Participants immediately received performance feedback (positive/negative/neutral) and their ranking, relative to fictional peers. Participants rested for 90 seconds after each feedback, allowing for a neural stress recovery period. Collected before scanning, cER scores were correlated with neural activity during each recovery condition. Results: Negative feedback recovery yielded increased activity within the [...]. Reference MURRAY, Ryan, et al. Maladaptive emotion regulation traits predict altered corticolimbic recovery from psychosocial stress. Journal of Affective Disorders, 2020, vol. 280, no. Pt A, p. 54-63. PMID : 33202338 DOI : 10.1016/j.jad.2020.09.122. Available at: http://archive-ouverte.unige.ch/unige:144937 Disclaimer: layout of this document may differ from the published version..

(2) Journal Pre-proof. Maladaptive emotion regulation traits predict altered corticolimbic recovery from psychosocial stress Ryan J. Murray , Kalliopi Apazoglou , Zeynep Knight-Celen , Alexandre Dayer , Jean-Michel Aubry , Dimitri Van De Ville , Patrik Vuilleumier , Camille Piguet PII: DOI: Reference:. S0165-0327(20)32818-4 https://doi.org/10.1016/j.jad.2020.09.122 JAD 12525. To appear in:. Journal of Affective Disorders. Received date: Revised date: Accepted date:. 1 May 2020 14 August 2020 28 September 2020. Please cite this article as: Ryan J. Murray , Kalliopi Apazoglou , Zeynep Knight-Celen , Alexandre Dayer , Jean-Michel Aubry , Dimitri Van De Ville , Patrik Vuilleumier , Camille Piguet , Maladaptive emotion regulation traits predict altered corticolimbic recovery from psychosocial stress, Journal of Affective Disorders (2020), doi: https://doi.org/10.1016/j.jad.2020.09.122. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V..

(3) Highlights:     . 1. Adaptive neural stress recovery and regulation are key to mental health Neural recovery after psychosocial stress was assessed in fMRI Emotion regulation scores were correlated with positive/negative recovery Positive evaluation increased corticolimbic adjustment in poor emotion regulators Altered stress recovery and regulation may be risk factors for affective disorders.

(4) Maladaptive emotion regulation traits predict altered corticolimbic recovery from psychosocial stress. Ryan J. Murray, Kalliopi Apazoglou, Zeynep Knight-Celen, Alexandre Dayer, Jean-Michel Aubry, Dimitri Van De Ville, Patrik Vuilleumier, & Camille Piguet Corresponding Author: Ryan J. Murray Department of Psychiatry Faculty of Medicine, University of Geneva Synapsy, University of Geneva Campus Biotech Chemin des Mines 9 1202 Geneva, Switzerland [email protected]. 1. ABSTRACT Background: Adaptive recovery from stress promotes healthy cognitive affective functioning, whereas maladaptive recovery is linked to poor psychological outcomes. Neural regions, like the anterior cingulate and hippocampus, play critical roles in psychosocial stress responding and serve as hubs in the corticolimbic neural system. To date, however, it is unknown how cognitive emotion regulation traits (cER), adaptive and maladaptive, influence corticolimbic stress recovery. Here, we examined acute psychosocial stress neural recovery, accounting for cER. Methods: 2.

(5) Functional neuroimaging data were collected while forty-seven healthy participants performed blocks of challenging, time-sensitive, mental calculations. Participants immediately received performance feedback (positive/negative/neutral) and their ranking, relative to fictional peers. Participants rested for 90 seconds after each feedback, allowing for a neural stress recovery period. Collected before scanning, cER scores were correlated with neural activity during each recovery condition. Results: Negative feedback recovery yielded increased activity within the dorsomedial prefrontal cortex and amygdala, but this effect was ultimately explained by maladaptive cER (M-cER), like rumination. Isolating positive after-effects (i.e. positive > negative recovery) yielded a significant positive correlation between M-cER and the anterior cingulate, anterior insula, hippocampus, and striatum. Conclusions: We provide first evidence of M-cER to predict altered neural recovery from positive stress within corticolimbic regions. Positive feedback may be potentially threatening to individuals with poor stress regulation. Identifying positive stress-induced activation patterns in corticolimbic neural networks linked to M-cER creates the possibility to identify these neural responses as risk factors for social-emotional dysregulation subsequent to rewarding social information, often witnessed in affective disorders, like depression.. Keywords: Evaluation; Social Evaluative Threat; Emotion Dysregulation; Stress-related disorders. 3.

(6) 2. Introduction Adaptive recovery from acute and chronic stressors is crucial to maintaining healthy cognitive and affective functioning. Post-stress recovery periods allow for the return to physiological homeostatic states, via allostatic processes (Karatsoreos and McEwen, 2011), as well as the cognitive processing of prior situational elements relevant for future adaptive behavior. Adaptive cognitive emotion regulation (cER) during this period thus requires the employment of appropriate attentional and appraisal strategies to overcome aversive emotional experiences and to gain a sense of control post-stress exposure (Miklosi et al., 2014). The inability to employ such strategies adequately, however, can lead to prolonged negative moods and persistent states of arousal, often resulting in stress-related disorders, such as posttraumatic stress (Chesney and Gordon, 2017, Kaczkurkin et al., 2017) as well as depression and anxiety (Disner et al., 2011, Jordan et al., 2018, Martin and Dahlen, 2005). In our daily life, we experience a relatively frequent degree of psychosocial stress, i.e. a neurobiological response to social situations deemed threatening to psychological aspects of the self (Pruessner et al., 2008). Contrary to physical stressors, psychosocial stressors are anticipatory, target cognitive-affective conceptual schema, and mostly comprise challenging and unfamiliar social and non-social situations (Dedovic et al., 2009b, Herman et al., 2003). Social evaluation, for instance, challenges self-esteem (Crocker et al., 2003, Leary et al., 2003) in addition to one's sense of belonging (Somerville, 2013) and thus, unsurprisingly, elicits reliable stress responses (Dickerson and Kemeny, 2004). In healthy individuals, negative social evaluation is particularly stress-inducing (Auer et al., 2015, Chapman et al., 2014), whereas positive social evaluation induces psychosocial stress in vulnerable individuals with low self-esteem (Somerville et al., 2010) or depression (Reichenberger et al., 2017).. 4.

(7) The past few decades of neuroscience stress research have shed light on the dedicated neural systems facilitating psychosocial stress reactivity (Dedovic et al., 2009a, Herman et al., 2003). These primarily include the hippocampus and anterior cingulate (Dedovic et al., 2009a), regions which also play critical roles in corticolimbic stress regulation (Bogdan et al., 2016). The corticolimbic network is a neural system that regulates both physiological (Lee and Gorzalka, 2012, Liu et al., 2012) and psychosocial (Birk et al., 2019, Eisenberger et al., 2011, Gao et al., 2018, Prevost et al., 2018, Somerville et al., 2006, Somerville et al., 2010, Yang et al., 2016) stress. It comprises ventral and dorsal regions, including subcortical structures of the amygdala and hippocampus, frontal cortical midline structures of the anterior cingulate cortex (ACC) and medial prefrontal cortex (MPFC), and lateral structures such as the insular cortex (Bogdan et al., 2016). Together, this circuitry allows for the efficient processing of affective information via ventral systems, e.g. amygdala, hippocampus or ventral MPFC (vMPFC), as well as the adaptive regulation of affective states via dorsal regions, e.g. ACC or dorsal MPFC (dMPFC) (Comte et al., 2016). Evidence suggests direct influences of cER on neural stress recovery, where adaptive cER (A-cER) typically improves stress responding and inversely for maladaptive cER (M-cER). Following negative stimuli exposure, rumination relates to impaired cardiac stress recovery, as measured via blood pressure (Glynn et al., 2002; Radstaack et al., 2011), and altered superficial cortical stress recovery, measured via fNIRS (Rosenbaum et al., 2018). Reappraisal, however, relates to improved cardiovascular stress recovery, measured via heart rate variability (Jentsch and Wolf, 2020). Cortisol stress recovery, however, may be less sensitive to M-cER, as Krkovic et al. (2018) illustrated M-CER (e.g. rumination) to predict altered cortisol stress responding, but not subsequent recovery. Thus, whereas evidence suggests cER impacts peripheral and central nervous system stress recovery, research has yet to provide investigations of such impact on neural stress recovery at the whole-brain level.. 5.

(8) The current study thus attempted to investigate whole-brain neural recovery to psychosocial stress and its association with cER traits, adaptive and maladaptive. We employed a modified version of the Montreal Imaging Stress Task (MIST), a validated tool to induce social evaluative threat (Dedovic et al., 2014, Dedovic et al., 2005, Dong et al., 2020, Ming et al., 2017, Pruessner et al., 2008). The MIST can be characterized as a motivated performance task (Dickerson and Kemeny, 2004), whereby participants perform challenging arithmetic calculations while being evaluated by the experimenter in an adjacent room. This task includes a degree of uncontrollability, through task difficulty, time constraints and mock feedback (Dimitrov et al., 2018). An earlier meta-analysis of stress studies showed that social evaluative threat, combined with uncontrollability, reliably elicits elevated stress responding and affects physiological recovery processes (Dickerson and Kemeny, 2004). Accordingly, participants performed mental mathematical calculations and were told their performance would be evaluated remotely (Dedovic et al., 2005, Dedovic et al., 2009b). They then received immediate performance feedback (positive, negative, and neutral) along with their ranking, with respect to fictional peers. Whereas prior studies have investigated neural recovery from psychosocial stress (e.g. Dimitrov et al., 2018, Maron-Katz et al., 2016, Quaedflieg et al., 2015, Vaisvaser et al., 2013, van Marle et al., 2009), they typically assess neural recovery at the end of the entire task (Dimitrov et al., 2018, Maron-Katz et al., 2016, Quaedflieg et al., 2015, van Marle et al., 2009), confounding effects from both experimental and control conditions, or simply without a control condition (Quaedflieg et al., 2015). Furthermore, analyses typically target specific regions of interest (Dimitrov et al., 2018, Quaedflieg et al., 2015, Vaisvaser et al., 2013, van Marle et al., 2009) instead of considering whole-brain dynamics. In the current study, therefore, participants rested for a 90s stress recovery period (cf. Eryilmaz et al., 2011, 2014) after each feedback type (positive, negative, neutral), thus allowing investigation of neural recovery dynamics during an unstructured period immediately following each acute stress condition. We measured whole-brain neural activity changes as a function of feedback type and cER. In addition to 6.

(9) measuring brain activity changes, we assessed heart rate (HR) during post-stress recovery to both measure physiological response to stress and to check for the main effects of the recovery period. Analyzing HR is advantageous as it affords MRI-compatible measures of stress-related cardiovascular responding that are sensitive to interindividual variability (Brindle et al., 2014). As psychosocial stress likely increases HR (e.g. Fechir et al., 2010, Wang et al., 2005), we hypothesized increased HR during negative feedback recovery. Finally, research suggests females experience a degree of stereotype threat when performing mathematical calculations (Gresky et al., 2005), and stress responses to the MIST task may differ according to sex (Chung et al., 2016). Additionally, literature illustrates sex differences in mathperformance anxiety (Cvencek et al., 2011, Tomasetto et al., 2011), which may ultimately influence behavioral and neurobiological responses. We, therefore, consider sex differences in our analyses. We hypothesized acute psychosocial stress induced by both positive and negative feedback creates significant and lingering stress responses in corticolimbic regions, predicted by cER traits. Given elevated math performance anxiety, we expected females to exhibit higher corticolimbic stress reactivity during positive and negative (vs. neutral) recovery. Across both groups, however, we expected recovery from negative feedback, in particular, to increase neural activity within the amygdala, hippocampus, and ACC. When correlated with cER traits, however, we expected decreasing amygdala activity with increasing AcER and decreasing M-cER.. 3. Materials & Methods 3.1. Participants. 7.

(10) Sample size was determined by selecting previously published fMRI studies employing the MIST task. Two exemplary MIST studies presenting fMRI results yielded an average minimal effect size of r=0.45 (Dedovic et al., 2014, Ming et al., 2017). We thus calculated the estimated sample size for an ANCOVA F-Test, considering fixed effects, main effects and interactions using G*Power (Faul et al., 2009). We defined our input parameters for effect size f=0.45, power=0.80 and a=0.05 to arrive at a recommended sample size of 51 total participants. We recruited an additional 4 participants, totaling fifty-five healthy participants aged 17-40 (31 females, 8 left-handed, average age 24.29 (+/- 6.66)) who were recruited from the general population through web announcements and local databases. All participants had normal or corrected-to-normal vision, reported no illicit drug use or history of neurological or psychiatric disease. All participants provided informed consent via a signed consent form. Ultimately, six participants were removed because of excessive movement (i.e. abrupt head movement of greater than 1 voxel (i.e. 3.2 mm)) and two were removed due to missing/invalid MRI data. This resulted in a final sample of 47 participants (27 females, 7 left-handed, mean age of 24.04 +/- 6.50 years, range 17-39 years). This study was approved by the University of Geneva research ethics committee (CER 13-081).. 3.1. Cognitive Emotion Regulation Questionnaires 3.1.1. Cognitive Emotion Regulation Questionnaire (CERQ; Garnefski et al., 2001). The CERQ is a multidimensional construct aimed at identifying the "self-regulatory, conscious, cognitive components of emotion regulation," (Garnefski et al., 2001, p. 1312) and contains two principle factors: adaptive and non-adaptive cER. Adaptive cER include acceptance, positive refocusing, focusing on 8.

(11) planning, positive reappraisal, and putting into perspective. Non-adaptive cER strategies comprise rumination, self-blame, catastrophizing, and blaming others. The CERQ shows high test-retest reliability as well as modest correlations with depressive and anxious symptoms (Garnefski and Kraaij, 2007). We included the two principal subscales of “adaptive cER” and “non-adaptive cER”.. 3.1.2. Ruminative Response Scale—(RRS; Treynor et al., 2003) We employed a short 10-item version of the RRS (Treynor et al., 2003) comprising reflection and brooding subscales. Reflection consists of intentional cognitive introspection aimed at alleviating one's depressed mood. Brooding reflects a "passive comparison of one’s current situation with some unachieved standard" (Treynor et al., 2003, p. 256). Brooding traits, in particular, may thus be quite relevant when recovering after receiving feedback on one's performance.. 3.2. Experimental task In order to induce psychosocial stress during fMRI, with positive and negative social feedback, and to study neural recovery following stress, we adapted the Montreal Imaging Stress Task - MIST (Dedovic et al., 2005). The MIST is a highly established behavioral task recognized as inducing social evaluative stress via remote observation, immediate performance feedback following challenging mental arithmetic calculations, and social ranking (e.g. Dedovic et al., 2014, Dedovic et al., 2005, Dong et al., 2020, Lederbogen et al., 2011, Ming et al., 2017). In alignment with prior studies, participants first performed a 9.

(12) block of five semi-complex calculations (Calculation Condition) or simple number matching (Control Condition) within a limited time (9s maximum), represented by a white bar within which a black bar moved from left to right to indicate the passage of time. Following the fifth trial in the respective block, participants received feedback, either positive, negative, or neutral (control condition). Reducing the cognitive load required during the control task (e.g. easier arithmetic calculations, number matching) has been employed before and validated in earlier modified MIST tasks (e.g. Dedovic et al., 2014, Dimitrov et al., 2018). As participants were told their performance was compared to that of all other participants studied so far, the feedback included a ranking that placed the participant amongst a group of his/her peers. Following the feedback, participants rested with eyes closed for 90s. The duration of this recovery period has been validated as adequate to capture neural stress recovery dynamics (cf. Eryilmaz et al., 2011, 2014). During recovery, participants were instructed to let their mind wander freely and wait for the next calculation block. After 90s, a sound indicated that the recovery period finished. During the control condition, performance feedback pertaining to ranking, percentage and response time was absent (see Figure 1, Supplementary Materials for details).. 10.

(13) Recovery (Eyes Closed). Feedback Alert to open eyes. Calculation (5 trials). 90s. 8s. 9s (maximum). Figure 1.Adapted Montreal Imaging Stress Task - MIST. In our adapted MIST task (Dedovic et al., 2005), participants were first asked to perform mental arithmetic calculations, in blocks of 5 trials. At the beginning of every trial, a response cursor appeared at a default position of 5, and participants used a button box to move the cursor in either direction. Response choices always ranged from 0 to 10. Time pressure was induced via the white bar, wherein the passage of time was indicated by a black line moving from left to right. Participants had a maximum of 9s to select their response. At the end of the fifth trial in the respective block, participants were presented with an 8-second feedback screen, which could be positive, negative, or neutral (control condition). Participants were also presented with their ranking, with respect to 34 fictious participants of the same age. This ranking could be high (positive condition), low (negative condition), or absent (control condition). This example shows the negative condition, where feedback was negative and ranking was low. Therefore, the participant figured 34 among 35 total participants, i.e. second to last. After receiving feedback, participants were instructed to close their eyes and rest for a short recovery period of 90s. They were then alerted to re-open their eyes by a loud signal noise projected from the scanner room speakers. s: seconds.. 11.

(14) When the experiment was completed, we debriefed the participants. Here, we posed participants open questions, asking them to indicate if they had any comments/observations regarding the task. Given the automatic generation of the MIST feedback, we anticipated some suspicion of the task. Statements of suspicion, explicit or implicit, included responses such as "my performance did not match the feedback", "I felt like I did better than my score", "the answers seemed predetermined". Participants giving such responses were categorized as "suspicious," whereas those stating no suspicion were categorized as "nonsuspicious". These data were used for our auxiliary fMRI data quality check analysis (see Supplementary Materials).. 3.3. Behavior During the MIST task, we collected behavioral data related to accuracy (i.e. percentage of correct answers for each condition) and reaction time (i.e. time (in ms) to answer). We then ran a 2x2 repeated-measures ANOVA, using within-subject factor Condition (calculation, control) and between-subject factor Sex (male, female). Statistical analyses were conducted using IBM SPSS Statistics 25 for Windows (Corp, 2017).. 3.4. Heart Rate Heart rate (HR) was recorded using an MRI-compatible pulse oximeter while participants lay supine in the scanner (see Supplementary Materials). Comparison of individual time points within group comparisons was conducted for stress recovery periods using a general linear model. Stress recovery periods lasted 90s, in alignment with previously validated designs (Eryilmaz et al., 2011, 2014). To reduce any lingering visuoperceptual effects from the preceding instructions slide, however, we removed 12.

(15) the first 5s, thus ultimately totaling 85s for each recovery period. In order to assess HR change over time, therefore, we separated the 85s recovery period into 3 separate bins of equal duration (~28 secs) and calculated mean HR in each bin, the three time points being 28.3s, 61.6s and 85s from the onset of the recovery condition. We then conducted a 3x2 repeated measures ANOVA measuring within-subject effects of Time (Time bins: T1, T2, T3), between-subject effects of Sex (male, female) and any effect of their interaction.. 3.5. fMRI data acquisition and analysis Functional brain images were acquired with a 3T Magnetom TIM Trio scanner (Siemens, Germany) and a 32-channel head coil using a standard echo-planar imaging sequence [36 transverse slices with 20% gap, 64 x 64 base resolution, voxel size: 3.2 mm × 3.2 mm × 3.2 mm, repetition time (TR): 2100 ms, echo time (TE): 30 ms, flip angle (FA): 80°, field of view (FOV): 192 mm]. See Supplementary Materials for technical acquisition parameters. MRI data were collected at the Brain and Behaviour Laboratory (BBL) at the University of Geneva medical school. Image preprocessing was implemented using standard procedures implemented in SPM12 (www.fil.ion.ucl.ac.uk/spm) and a careful quality check was conducted (see Supplementary Materials). At the first-level, we designed a general linear model of individual fMRI data with the following events: five screens of i) calculations (~45s) or ii) control arithmetic calculation (~45s); three different feedback (8s) of iii) positive, iv) negative, or v) neutral valence; and the corresponding 85s (see above) neural recovery periods after vi) positive, vii) negative, or viii) neutral feedback. We hence label the conditions of interest “positive recovery” (recovery after positive feedback), “negative recovery” (recovery after negative feedback) and “neutral recovery” (recovery after the control condition).. 13.

(16) At the second (group) level, we created an auxiliary 2x3 flexible factorial design testing for Condition (positive, negative, neutral) and Sex (female, male) with Subject as a random-effects factor (cf. Gläscher and Gitelman, 2008). This was used to assess main effects of Condition (i.e. positive<>negative, [positive+negative]>neutral), Sex (male<>female), and a possible Condition x Sex interaction. Secondlevel analyses and multiple comparison corrections were implemented using threshold-free cluster enhancement (TFCE) nonparametric permutation testing via the FSL randomize algorithm (Winkler et al., 2014) (see Supplementary Materials). Peak cluster locations of all analyses are reported using the Montreal Neurological Imaging (MNI) coordinates. Additionally, we conducted a preliminary quality check analysis to verify any effects of expectancy violation or suspicion on neural feedback during recovery. This analysis investigation revealed no significant effect with the principal results of this study presented below. We describe this analysis and its results in the Supplementary Materials.. 4. Results. 4.1. Behavior Performance accuracy was 58.88% (+/-18.94%) for the calculation condition and 97.55% (+/-4.15%) for the control condition. Average reaction time was 5,339.63ms (+/-1,150.05ms) for the calculation condition and 1,752.39ms (+/-337.51ms) for the control condition. These behavioral measures were examined using 2x2 repeated measures ANOVA with Condition (calculation, control) within-subject factor and Sex (male, female) as between-subject factor. We observed a significant main effect of Condition for both accuracy (F(1, 45)=183.56, p<.001, partial η2=.803) and reaction time (F(1, 14.

(17) 45)=450.09, p<.001, partial η2=.909). Post-hoc paired t-tests show accuracy was significantly lower (t(46) =-13.671, corrected p<.001) and reaction time significantly longer (t(46)=-13.671, corrected p<.001) in the calculation condition than in the control condition. There was no significant main effect of Sex for either accuracy (F(1,45)=3.00, p=.092) or reaction time (F(1,45)=0.026, p=.873), nor any significant interaction effect (ps>.05) (table 1).. Accuracy (%). Reaction Time (ms). Calculations. Control. Calculations. Control. Males. 64.13 (15.05). 97.25 (4.72). 5,301.36 (1,305.85). 1,745.20 (401.76). Females. 55.00 (20.79). 97.78 (3.76). 5,367.99 (1,044.95). 1,757.72 (289.00). Total Average. 58.88** (18.94). 97.55 (4.15). 5,339.63** (1,150.05). 1,752.39 (337.51). Table 1. Task-based performance variables for both males (n=20) and females (n=27). Accuracy was significantly lower and reaction time significantly longer in the calculation condition (total average) than in the control condition (total average). Accuracy is presented as a percentage whereas reaction time is presented as mean milliseconds subsequent to the onset of the calculation. Figures between parentheses represent the standard deviation. Within-subject differences: ** p<.001.. Results from psychological questionnaires are reported in table 2, showing scores in normal range for both the CERQ and RRS (see table 2). For the CERQ, we observed a main effect of Scale only (F(1,41)=210.21, corrected p<.001, partial η2=.837) whereby participants showed significantly greater tendencies toward adaptive versus non-adaptive cER strategies (see table 2). We thus observed no significant effect of Sex (p=.114) or Scale x Sex interaction (p=.119). For the RRS, we observed no main effect of Scale (p=.100) or an effect of Scale x Sex interaction (p=.252). We did, however, observe a. 15.

(18) significant effect of Sex (F(1,40)=5.945, p=.019, partial η2=.129), whereby females reported significantly greater brooding rumination than males.. Adaptive cER. NonAdaptive cER. Reflective Rumination. Brooding Rumination. Males. 63.07 (9.26). 30.17 (6.52). 10.12 (3.79). 8.59 (3.40). Females. 63.20 (12.53). 36.80 (7.66). 11.64 (2.94). 11.36* (3.16). Total Average. 63.15†† (11.15). 34.02 (7.85). 11.02 (3.35). 10.24 (3.50). Table 2. Task-independent psychological measures for both males. Significant differences were observed between females and males for nonadaptive cER (CERQ, n=43) and brooding rumination (RRS, n=42). cER: cognitive emotion regulation. Within-subject differences: †† p<.001; Between-subject differences: * p< .05. 4.2. Physiology We analyzed HR data of the 47 remaining healthy control participants. A further eight participants were ultimately discarded due to poor recordings, leaving n=39 participants (21 females, mean age of 24.85 (+/-6.77) years). We then conducted a 3x3x2 repeated-measures ANOVA, testing within-subject effects of Time (T1-T3 during recovery), Condition (positive, negative, neutral), and Sex (male, female). We observed a main effect of Time (F(1, 1.395)=41.006, p<.001, partial η2=.526, Greenhouse-Geisser corrected), with T1 showing significantly increased HR relative to both T2 & T3 (ps<.001, Bonferroni. 16.

(19) adjusted). We witnessed no significant effect of Condition. However, we did observe a significant Time x Condition interaction (F(1, 3.140)=2.886, p=.036, partial η2=.072, Greenhouse-Geisser corrected), whereby HR during negative recovery was significantly higher than control recovery during T1 (p=0.001, False Discovery Rate corrected). This indicates an increase in HR during the initial seconds of negative recovery, suggesting heightened arousal following negative feedback. Finally, we observed no significant effect of Sex as a main effect or as an interaction with Time or Condition (ps>.05). Our results thus showed increased HR during early stages of negative recovery. Given the absence of such an effect in the positive condition, increased HR may attest to increased levels of subjective stress and not effects of increased task demands as was suggested by (Fechir et al., 2010).. 4.3. fMRI Our fMRI data analysis focused on the effects of psychosocial stress on neural recovery as well as how cER traits affect such recovery. We thus investigated the effects of Condition and Sex on neural activity during recovery following each of the three feedback types. We observed a main effect of Condition, whereby both positive and negative (> neutral) recovery yielded significant cluster activity within the occipital cortex, specifically the intracalcarine gyrus (table S1A). Additionally, positive (> neutral) recovery, yielded increased neural activity within the occipital cortex, specifically the supracalcarine cortex (table S1B). Finally, we observed robust effects of negative (> neutral) recovery in the occipital cortex, hippocampus, amygdala, dorsolateral prefrontal cortex (dLPFC), dMPFC, mid cingulate gyrus, superior frontal gyrus, and angular gyrus (table S1C, figure 3). We observed no effect of negative > positive recovery, positive > negative recovery, or neutral > (positive + negative) recovery. We observed a significant main effect of neither Sex nor a Condition x Sex interaction.. 17.

(20) x = -3. y = -7. Mid Cingulate. SFG/ dMPFC. L. Amyg. R. vMPFC. Intracalcarine Cortex/ Occipital Pole. p<.05. 0. 5.29. Recovery: Negative > Neutral. Figure 3. Neural recovery following negative feedback. Figure illustrates whole-brain BOLD activations during 85-second neural recovery period subsequent to negative feedback, as compared to neutral feedback (corrected p<.05). Abbreviations. Amyg: amygdala; dMPFC: dorsomedial prefrontal cortex; SFG: superior frontal gyrus; vMPFC: ventromedial prefrontal cortex.. 4.3.1. Covariate Analysis We next conducted a covariate analyses on neural dynamics during positive and negative recovery, correlating neural activity with the subscales of the two cER measures. We observed positive correlations in the positive (> negative) recovery condition only: (i) between non-adaptive cER (CERQ) and the dorsal ACC (dACC), pregenual ACC (pgACC, dorsal part), dMPFC, mid cingulate gyrus, insular cortex, posterior cingulate cortex (PCC), hippocampus, amygdala, precuneus, paracentral lobule (PCL), and cerebellum (corrected p<.01) (figure 4A) and (ii) between brooding rumination (RRS) and the dACC, pgACC (ventral part), hippocampus, insular cortex, thalamus, cuneus, precuneus, and PCL (corrected p<.01) (figure 4B). 18.

(21) x = -3. y = -6. PCL/ Precun Insular Cortex. pCC. A. Cerebellum. Amyg. L. MCG. R. dACC dMPFC. p<.01. 0. 6.39. Recovery: Pos > Neg ~ Non-Adaptive cER x = -3. y=0. dACC PCL/ Precun. B. L. Insular Cortex/ Striatum. R. pgACC. p<.01. 0. 6.30. Recovery: Pos > Neg ~ Brooding Rumination. Figure 4. Positive correlations in the positive > negative recovery condition. Figure illustrates positive correlation of whole-brain BOLD activity between positive > negative recovery condition and (A) Non-adaptive cognitive emotion regulation traits (CERQ; corrected p<.01) and (B) brooding rumination traits (RSS; corrected p<.01). Abbreviations. Amyg: amygdala; dACC: dorsal anterior cingulate cortex; dMPFC: dorsomedial prefrontal cortex; MCG: mid cingulate gyrus, pCC: posterior cingulate cortex; PCL: paracentral lobule; Precun: precuneus; pgACC: pregenual anterior cingulate.. 19.

(22) 4.3.2. Post-hoc analyses As an initial post-hoc analysis, we wished to determine if maladaptive cER (M-cER), as per the CERQ or RRS, could ultimately account for the significant effect of emotion we observed during recovery, particularly when comparing negative (> neutral) recovery. We therefore measured recovery, using two 3x2 flexible factorial designs, each with factors Condition (positive, negative, neutral) and Sex (male, female), and either non-adaptive cER (CERQ) or brooding rumination (RRS) as nuisance regressors. We thus investigated the main effect of Condition when controlling for each of these variables. When accounting for M-cER, we no longer witnessed a significant effect of Condition, signaling a likely effect of M-cER during negative recovery. Next, we wished to determine which neural regions consistently overlapped across the two correlation maps highlighted in figure 4 (i.e. non-adaptive cER and brooding rumination), accounting for any neural activity that could be linked to suspicion during positive recovery (see Supplementary Materials). We thus conducted a conjunction analyses using AFNI's 3dcalc algorithm (Cox, 1996) and spatially excluded activations potentially linked to participant suspicion (see figure S1). The resulting conjunction analyses between non-adaptive cER and brooding rumination during positive > negative recovery yielded shared activations within the PCL, precuneus (anterior part), midcingulate gyrus, dACC, pgACC (dorsal part), insular cortex, precentral gyrus, striatum (putamen), hippocampus and bilateral orbitofrontal cortex (see figure 5).. 20.

(23) Conjunction: CERQ Non-Adapt ∩ RRS Brood x = 31. x = -3. x = 39. MCG dACC. Ins Cortex. pgACC. Hipp. ps <. 01. Recovery: Pos > Neg. Figure 5. Conjunction of non-adaptive emotion regulation traits and brooding rumination traits during positive (> negative) recovery. Figure displays conjunction analysis of activation maps resulting from a positive correlation between CERQ non-adaptive emotion regulation traits and positive > negative recovery and RRS brooding rumination traits and positive > negative recovery (corrected ps<0.01), and excluding for any activations likely linked to suspicion during positive recovery (see figure S1). Conjunctions were created statistically using AFNI's 3dcalc algorithm (Cox, 1996). Not shown in the figure are also shared activations within the left striatum (putamen) and bilateral orbitofrontal cortices. Abbreviations. dACC: dorsal anterior cingulate cortex; Hipp: hippocampus; Ins Cortex: Insular Cortex; MCG: midcingulate gyrus; pgACC: pregenual anterior cingulate.. Finally, we wished to determine if BOLD response correlated with HR as a function of M-cER (CERQ or RRS) for the four principal recovery contrasts (positive, negative, positive<>negative). These analyses yielded no significant results (see Supplementary Materials, Methods).. 5. Discussion The principal goal of this study was to investigate the effects of cER, adaptive and maladaptive, traits on whole-brain neural recovery from psychosocial stress induced by both positive and negative feedback. We witnessed no significant main effect during positive recovery. Although negative (> neutral) recovery yielded increased activity in the amygdala, hippocampus, dMPFC, and vMPFC, this effect was ultimately explained by maladaptive cER (M-cER) traits, which include non-adaptive cER (as per the CERQ) and brooding rumination (RRS). Contrary to our hypotheses, however, we observed no significant correlation 21.

(24) with adaptive cER (A-cER) during positive or negative recovery. However, we did observe a significant correlation with M-cER during positive (> negative) recovery, which yielded increased dACC, pgACC (dorsal part), striatum (putamen), hippocampus, orbitofrontal cortices, insula, precuneus (anterior part) and PCL activity with increasing M-cER. Importantly, these increased activations were consistent across both non-adaptive cER (CERQ) and brooding rumination (RRS) traits. Furthermore, this effect was observed uniquely when isolating the positive valence during the feedback recovery condition, suggesting that positive feedback induces increased and lingering corticolimbic stress responding in individuals with poorer regulatory tendencies. Finally, our neuroimaging results did not support our hypotheses concerning sex differences, however we did observe that females reported significantly higher brooding rumination traits. To our knowledge, this study is the first to present results from whole-brain analyses examining neural recovery from psychosocial stress and considering the role of M-cER. Moreover, we provide first evidence of altered neural functioning in corticolimbic regions when recovering from positive stress, with increased activity in both ventral (e.g. hippocampus) and dorsal regions (e.g. dACC, pgACC) as M-cER increases. In an adaptive stress response, coupling typically occurs between affective ventral and executive dorsal corticolimbic regions, whereby increased neural activity in ventral regions signal dorsal regions to enhance neural functioning, the result of which moderates excitatory affective responding of the respective ventral regions (e.g. Bogdan et al., 2016, Del Rio-Casanova et al., 2016, Phillips et al., 2008). Increased dorsal and ventral activity thus suggests higher M-cER requires increased volitional executive control (dorsal) over preponderant automatic affective responding (ventral) when resting after self-affirming feedback. This strengthens the claim that positive feedback can induce elevated stress responding in psychologically vulnerable individuals (Birk et al., 2019, Reichenberger and Blechert, 2018, Somerville et al., 2010). Within individuals prone to M-cER, positive self-affirmations may potentially elicit important self22.

(25) discrepancies, or conflicts between idealized self-goals (primed by positive feedback) and current negative semantic self-representations (cf. Higgins, 1987). As questionnaires were administered prior to scanning, we observed M-cER to predict subsequent neural activations within regions facilitating selfprocessing and performance error monitoring (pgACC and dACC; Berkman et al., 2014, Boehler et al., 2014, Murray et al., 2012, Northoff, 2005, Northoff et al., 2006), affective processing (insula; Craig, 2009), and semantic processing (hippocampus; Manns et al., 2003). Together, these data suggest enhanced self- and affect-regulatory neural reactivity during positive recovery in individuals with poorer stress regulation tendencies. This discrepancy between external and self-representations seem particularly stressful for vulnerable individuals and could be a marker of potential psychopathological mechanisms. Presently, a debate exists whether stress coping is facilitated more by increased adaptive ER (Gray and Tully, 2020, Lea et al., 2019) or decreased maladaptive ER (Nasso et al., 2019). Results remain equivocal; however, our data support the model that favors decreased maladaptive ER (Compas et al., 2017, Schafer et al., 2017, van den Heuvel et al., 2020). Therefore, our findings encourage more direct neuroimaging of M-cER, similar to extant A-cER studies (e.g. Moodie et al., 2020). This could further confirm if increased self-processing is the common underlying deficit as suggested by our data.. 5.1. Clinical implications Our results suggest increased M-cER to consistently recruit pgACC, striatum, and hippocampus when resting briefly after socially rewarding information. There are several overlaps that implicate important vulnerabilities for depression in our findings. First, emotion dysregulation in face of positive social information may be an important predictor for depression (Jordan et al., 2018, Martin and Dahlen, 2005). Second, altered reward processing in depressed patients is associated with hyperactive striatal activity with co-activating increased pgACC (Goya-Maldonado et al., 2015), in alignment with our M-cER 23.

(26) correlations, thus suggesting neurobiological parallels with our high M-cER individuals. Third, both stress regulation and major depression share overlapping corticolimbic structures, particularly the hippocampus and pgACC (cf. Belleau et al., 2019), implying a relationship between altered stress responding and depressive symptoms. Finally, altered neural activity within resting pgACC is directly linked to depression (Pizzagalli, 2011), which suggests post-stress pgACC dynamics may provide key insights into the relationship between M-cER and depression during neural recovery. Future research is nonetheless needed to test corticolimbic network functioning in light of depressive symptoms and M-cER following positive evaluation. Implications of these results extend to mood and personality disorders showing important emotion dysregulation, such as bipolar (BD) and borderline personality (BPD) disorder. Indeed, both BD and BPD are characterized as exhibiting significant affective lability and volatility (Reich et al., 2012) as well as dysregulated affect during stress responding (Phillips et al., 2008, Putnam and Silk, 2005). Importantly, current literature reveals altered corticolimbic structural and functional integrity in both BPD (Amad and Radua, 2017) and BD (e.g. Phillips et al., 2008, Torrisi et al., 2013, Versace et al., 2010). Considering social evaluative threat responding in light of mood and personality disorder dimensions as well might allow using neural recovery to such threat as a transdiagnostic vulnerability factor.. 5.2. Limitations A main limitation of the study is the lack of control of cER strategies during recovery. That is, we cannot entirely control for the type of cognitive-affective processing occurring in our participants during recovery. As participants were neither asked to recount their thought processes nor directed toward any type of mental strategy, identifying the underlying psychological processes occurring during this time remains the subject of future experimentation. Still, our current investigation aimed to identify lingering 24.

(27) neural activity occurring during brief resting-state activity after psychosocial stress, that which was satisfied by the task instructions. Furthermore, both our HR and fMRI analyses suggest that participants illustrate signature physiological and neurobiological activity, respectively, that would be indicative of a resting-state, given the extant literature. Nonetheless, HR is limited in determining physiological stress recovery. For instance, it cannot allow for analyses of HR variability, which may be more sensitive to stress-related autonomic nervous system fluctuations (Kim et al., 2018). Furthermore, it may be less effective in detecting effects from cER traits than other peripheral measures, like blood pressure (cf. Radstaak et al., 2011).. 5.3. Conclusion In conclusion, we show psychosocial stress impacts neural recovery within corticolimbic structures, dorsal and ventral, and may be predicted by maladaptive cognitive emotion regulation traits. In particular, individual emotion dysregulation traits significantly increase corticolimbic activity during neural stress recovery subsequent to positive, relative to negative, feedback. This elevated neural activity may present evidence for altered self- and emotion regulatory processing when faced with rewarding self-affirming information in individuals more prone to self-doubt and negative self-beliefs. These findings not only highlight the multiple cognitive and affective components of psychosocial stress responding, but also bear important clinical implications for populations suffering from emotion regulation disorders, such as depression, but also bipolar and borderline personality disorder. Potential alterations in corticolimbic recovery from psychosocial stress with respect to emotion dysregulation traits might inform transdiagnostic models of vulnerability to these disorders.. 25.

(28) Author Statement. Contributors CP and PV conceptualized the design of the study. CP, PV, DV, and KA contributed to the statistical framework with which RM conducted the analysis. CP, AD and JMA contributed to the theoretical framework of the manuscript. RM conducted the neuroimaging and behavioral analyses and drafted the manuscript. ZNC conducted the physiological analyses. RM, KA, AD, JMA, DV, PV and CP have reviewed and edited the manuscript. All authors have approved the final manuscript.. Role of funding source The funding source had no role in the study design, data collection, data analysis, data interpretations, or composition of the manuscript.. Declarations of Competing Interest None. Acknowledgments The authors would like to thank Dr. Sylvain Delplanque, Dr. Léonardo Ceravolo, Dr. Ben Meuleman, Prof. Corrado Corradi-Dell'Acqua and Mr. Yann Sagon for their critical methodological and technical support. We would also like to thank Mr. Bruno Bonet and Dr. Frédéric Grouiller of the BBL for their tireless operational support facilitating the acquisition of our MRI and physiological data. The study is supported by the Swiss National Center of Competence in Research; “Synapsy: the Synaptic Basis of Mental Diseases” (No. 51NF40-185897), as well as a grant from the Swiss National Foundation to J.M.A (No 32003B_156914). 26.

(29) 6. References Amad, A. & Radua, J. (2017). Resting-state meta-analysis in Borderline Personality Disorder: Is the fronto-limbic hypothesis still valid? J Affect Disord 212, 7-9. 10.1016/j.jad.2017.01.018. Auer, B. J., Byrd-Craven, J., Grant, D. M. & Granger, D. A. (2015). Common oxytocin receptor gene variant interacts with rejection sensitivity to influence cortisol reactivity during negative evaluation. Horm Behav 75, 64-9. 10.1016/j.yhbeh.2015.07.023. Belleau, E. L., Treadway, M. T. & Pizzagalli, D. A. (2019). The Impact of Stress and Major Depressive Disorder on Hippocampal and Medial Prefrontal Cortex Morphology. Biol Psychiatry 85, 443-453. 10.1016/j.biopsych.2018.09.031. Berkman, E. T., Kahn, L. E. & Merchant, J. S. (2014). Training-induced changes in inhibitory control network activity. J Neurosci 34, 149-57. 10.1523/JNEUROSCI.3564-13.2014. Birk, S. L., Horenstein, A., Weeks, J., Olino, T., Heimberg, R., Goldin, P. R. & Gross, J. J. (2019). Neural responses to social evaluation: The role of fear of positive and negative evaluation. J Anxiety Disord 67, 102114. 10.1016/j.janxdis.2019.102114. Boehler, C. N., Schevernels, H., Hopf, J. M., Stoppel, C. M. & Krebs, R. M. (2014). Reward prospect rapidly speeds up response inhibition via reactive control. Cogn Affect Behav Neurosci 14, 593-609. 10.3758/s13415-014-0251-5. Bogdan, R., Pagliaccio, D., Baranger, D. A. & Hariri, A. R. (2016). Genetic Moderation of Stress Effects on Corticolimbic Circuitry. Neuropsychopharmacology 41, 275-96. 10.1038/npp.2015.216. Chapman, A. L., Walters, K. N. & Gordon, K. L. (2014). Emotional reactivity to social rejection and negative evaluation among persons with borderline personality features. J Pers Disord 28, 720-33. 10.1521/pedi_2012_26_068. Chesney, S. A. & Gordon, N. S. (2017). Profiles of emotion regulation: Understanding regulatory patterns and the implications for posttraumatic stress. Cogn Emot 31, 598-606. 10.1080/02699931.2015.1126555. Chung, K. C., Springer, I., Kogler, L., Turetsky, B., Freiherr, J. & Derntl, B. (2016). The influence of androstadienone during psychosocial stress is modulated by gender, trait anxiety and subjective stress: An fMRI study. Psychoneuroendocrinology 68, 126-39. 10.1016/j.psyneuen.2016.02.026. Compas, B. E., Jaser, S. S., Bettis, A. H., Watson, K. H., Gruhn, M. A., Dunbar, J. P., Williams, E. & Thigpen, J. C. (2017). Coping, emotion regulation, and psychopathology in childhood and adolescence: A meta-analysis and narrative review. Psychol Bull 143, 939-991. 10.1037/bul0000110. Comte, M., Schon, D., Coull, J. T., Reynaud, E., Khalfa, S., Belzeaux, R., Ibrahim, E. C., Guedj, E., Blin, O., Weinberger, D. R. & Fakra, E. (2016). Dissociating Bottom-Up and Top-Down Mechanisms in the Cortico-Limbic System during Emotion Processing. Cereb Cortex 26, 144-55. 10.1093/cercor/bhu185. Corp, I. (2017). IBM SPSS Statistics for Windows. (ed. I. Corp.): Armonk, NY. Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research 29, 162-173. DOI 10.1006/cbmr.1996.0014. Craig, A. D. (2009). How do you feel--now? The anterior insula and human awareness. Nat Rev Neurosci 10, 59-70. 10.1038/nrn2555. 27.

(30) Crocker, J., Karpinski, A., Quinn, D. M. & Chase, S. K. (2003). When grades determine self-worth: consequences of contingent self-worth for male and female engineering and psychology majors. J Pers Soc Psychol 85, 507-16. 10.1037/0022-3514.85.3.507. Cvencek, D., Meltzoff, A. N. & Greenwald, A. G. (2011). Math-gender stereotypes in elementary school children. Child Dev 82, 766-79. 10.1111/j.1467-8624.2010.01529.x. Dedovic, K., Duchesne, A., Andrews, J., Engert, V. & Pruessner, J. C. (2009a). The brain and the stress axis: the neural correlates of cortisol regulation in response to stress. Neuroimage 47, 864-71. 10.1016/j.neuroimage.2009.05.074. Dedovic, K., Duchesne, A., Engert, V., Lue, S. D., Andrews, J., Efanov, S. I., Beaudry, T. & Pruessner, J. C. (2014). Psychological, endocrine and neural responses to social evaluation in subclinical depression. Social Cognitive and Affective Neuroscience 9, 1632-1644. 10.1093/scan/nst151. Dedovic, K., Renwick, R., Mahani, N. K., Engert, V., Lupien, S. J. & Pruessner, J. C. (2005). The Montreal Imaging Stress Task: using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain. J Psychiatry Neurosci 30, 319-25. Dedovic, K., Rexroth, M., Wolff, E., Duchesne, A., Scherling, C., Beaudry, T., Lue, S. D., Lord, C., Engert, V. & Pruessner, J. C. (2009b). Neural correlates of processing stressful information: an eventrelated fMRI study. Brain Res 1293, 49-60. 10.1016/j.brainres.2009.06.044. Del Rio-Casanova, L., Gonzalez, A., Paramo, M., Van Dijke, A. & Brenlla, J. (2016). Emotion regulation strategies in trauma-related disorders: pathways linking neurobiology and clinical manifestations. Rev Neurosci 27, 385-95. 10.1515/revneuro-2015-0045. Dickerson, S. S. & Kemeny, M. E. (2004). Acute stressors and cortisol responses: A theoretical integration and synthesis of laboratory research. Psychological Bulletin 130, 355-391. 10.1037/00332909.130.3.355. Dimitrov, A., Demin, K., Fehlner, P., Walter, H., Erk, S. & Veer, I. M. (2018). Differences in Neural Recovery From Acute Stress Between Cortisol Responders and Non-responders. Front Psychiatry 9, 631. 10.3389/fpsyt.2018.00631. Disner, S. G., Beevers, C. G., Haigh, E. A. & Beck, A. T. (2011). Neural mechanisms of the cognitive model of depression. Nat Rev Neurosci 12, 467-77. 10.1038/nrn3027. Dong, D., Li, C., Zhong, X., Gao, Y., Cheng, C., Sun, X., Xiong, G., Ming, Q., Zhang, X., Wang, X. & Yao, S. (2020). Neuroticism modulates neural activities of posterior cingulate cortex and thalamus during psychosocial stress processing. J Affect Disord 262, 223-228. 10.1016/j.jad.2019.11.003. Eisenberger, N. I., Inagaki, T. K., Muscatell, K. A., Byrne Haltom, K. E. & Leary, M. R. (2011). The neural sociometer: brain mechanisms underlying state self-esteem. J Cogn Neurosci 23, 3448-55. 10.1162/jocn_a_00027. Eryilmaz, H., Van De Ville, D., Schwartz, S. & Vuilleumier, P. (2011). Impact of transient emotions on functional connectivity during subsequent resting state: a wavelet correlation approach. Neuroimage 54, 2481-91. 10.1016/j.neuroimage.2010.10.021. Eryilmaz, H., Van De Ville, D., Schwartz, S. & Vuilleumier, P. (2014). Lasting impact of regret and gratification on resting brain activity and its relation to depressive traits. J Neurosci 34, 7825-35. 10.1523/JNEUROSCI.0065-14.2014.. 28.

(31) Faul, F., Erdfelder, E., Buchner, A. & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods 41, 1149-60. 10.3758/BRM.41.4.1149. Fechir, M., Gamer, M., Blasius, I., Bauermann, T., Breimhorst, M., Schlindwein, P., Schlereth, T. & Birklein, F. (2010). Functional imaging of sympathetic activation during mental stress. Neuroimage 50, 847-54. 10.1016/j.neuroimage.2009.12.004. Gao, S., Geng, Y., Li, J., Zhou, Y. & Yao, S. (2018). Encoding Praise and Criticism During Social Evaluation Alters Interactive Responses in the Mentalizing and Affective Learning Networks. Front Neurosci 12, 611. 10.3389/fnins.2018.00611. Garnefski, N. & Kraaij, V. (2007). The Cognitive Emotion Regulation Questionnaire - Psychometric features and prospective relationships with depression and anxiety in adults. European Journal of Psychological Assessment 23, 141-149. 10.1027/1015-5759.23.3.141. Garnefski, N., Kraaij, V. & Spinhoven, P. (2001). Negative life events, cognitive emotion regulation and emotional problems. Personality and Individual Differences 30, 1311-1327. Doi 10.1016/S01918869(00)00113-6. Gläscher, J. & Gitelman, D. (2008). Contrast weights in flexible factorial design with multiple groups of subjects. SPM@ JISCMAIL. AC. UK) Sml, editor, 1-12. Goya-Maldonado, R., Weber, K., Trost, S., Diekhof, E., Keil, M., Dechent, P. & Gruber, O. (2015). Dissociating pathomechanisms of depression with fMRI: bottom-up or top-down dysfunctions of the reward system. Eur Arch Psychiatry Clin Neurosci 265, 57-66. 10.1007/s00406-014-0552-2. Gray, J. M. & Tully, E. C. (2020). Cognitive reappraisal moderates the quadratic association between heart rate variability and negative affectivity. Psychophysiology 57, e13584. 10.1111/psyp.13584. Gresky, D. M., Eyck, L. L. T., Lord, C. G. & McIntyre, R. B. (2005). Effects of salient multiple identities on women's performance under mathematics stereotype threat. Sex Roles 53, 703-716. 10.1007/s11199-005-7735-2. Herman, J. P., Figueiredo, H., Mueller, N. K., Ulrich-Lai, Y., Ostrander, M. M., Choi, D. C. & Cullinan, W. E. (2003). Central mechanisms of stress integration: hierarchical circuitry controlling hypothalamo-pituitary-adrenocortical responsiveness. Front Neuroendocrinol 24, 151-80. 10.1016/j.yfrne.2003.07.001. Higgins, E. T. (1987). Self-discrepancy: a theory relating self and affect. Psychol Rev 94, 319-40. Jentsch, V. L. & Wolf, O. T. (2020). The impact of emotion regulation on cardiovascular, neuroendocrine and psychological stress responses. Biol Psychol 154, 107893. 10.1016/j.biopsycho.2020.107893. Jordan, D. G., Winer, E. S., Salem, T. & Kilgore, J. (2018). Longitudinal evaluation of anhedonia as a mediator of fear of positive evaluation and other depressive symptoms. Cogn Emot 32, 1437-1447. 10.1080/02699931.2017.1289895. Kaczkurkin, A. N., Zang, Y., Gay, N. G., Peterson, A. L., Yarvis, J. S., Borah, E. V., Dondanville, K. A., Hembree, E. A., Litz, B. T., Mintz, J., Young-McCaughan, S., Foa, E. B. & Consortium, S. S. (2017). Cognitive Emotion Regulation Strategies Associated With the DSM-5 Posttraumatic Stress Disorder Criteria. J Trauma Stress 30, 343-350. 10.1002/jts.22202. Karatsoreos, I. N. & McEwen, B. S. (2011). Psychobiological allostasis: resistance, resilience and vulnerability. Trends Cogn Sci 15, 576-84. 10.1016/j.tics.2011.10.005. 29.

(32) Kim, H. G., Cheon, E. J., Bai, D. S., Lee, Y. H. & Koo, B. H. (2018). Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature. Psychiatry Investig 15, 235-245. 10.30773/pi.2017.08.17. Krkovic, K., Clamor, A. & Lincoln, T. M. (2018). Emotion regulation as a predictor of the endocrine, autonomic, affective, and symptomatic stress response and recovery. Psychoneuroendocrinology 94, 112-120. 10.1016/j.psyneuen.2018.04.028. Lea, R. G., Davis, S. K., Mahoney, B. & Qualter, P. (2019). Does Emotional Intelligence Buffer the Effects of Acute Stress? A Systematic Review. Front Psychol 10, 810. 10.3389/fpsyg.2019.00810. Leary, M. R., Gallagher, B., Fors, E., Buttermore, N., Baldwin, E., Kennedy, K. & Mills, A. (2003). The invalidity of disclaimers about the effects of social feedback on self-esteem. Pers Soc Psychol Bull 29, 623-36. 10.1177/0146167203029005007. Lederbogen, F., Kirsch, P., Haddad, L., Streit, F., Tost, H., Schuch, P., Wust, S., Pruessner, J. C., Rietschel, M., Deuschle, M. & Meyer-Lindenberg, A. (2011). City living and urban upbringing affect neural social stress processing in humans. Nature 474, 498-501. 10.1038/nature10190. Lee, T. T. & Gorzalka, B. B. (2012). Timing is everything: evidence for a role of corticolimbic endocannabinoids in modulating hypothalamic-pituitary-adrenal axis activity across developmental periods. Neuroscience 204, 17-30. 10.1016/j.neuroscience.2011.10.006. Liu, J., Chaplin, T. M., Wang, F., Sinha, R., Mayes, L. C. & Blumberg, H. P. (2012). Stress reactivity and corticolimbic response to emotional faces in adolescents. J Am Acad Child Adolesc Psychiatry 51, 304-12. 10.1016/j.jaac.2011.12.014. Manns, J. R., Hopkins, R. O. & Squire, L. R. (2003). Semantic memory and the human hippocampus. Neuron 38, 127-33. 10.1016/s0896-6273(03)00146-6. Maron-Katz, A., Vaisvaser, S., Lin, T., Hendler, T. & Shamir, R. (2016). A large-scale perspective on stress-induced alterations in resting-state networks. Sci Rep 6, 21503. 10.1038/srep21503. Martin, R. C. & Dahlen, E. R. (2005). Cognitive emotion regulation in the prediction of depression, anxiety, stress, and anger. Personality and Individual Differences 39, 1249-1260. 10.1016/j.paid.2005.06.004. Miklosi, M., Martos, T., Szabo, M., Kocsis-Bogar, K. & Forintos, D. P. (2014). Cognitive emotion regulation and stress: a multiple mediation approach. Translational Neuroscience 5, 64-71. 10.2478/s13380-014-0207-9. Ming, Q., Zhong, X., Zhang, X., Pu, W., Dong, D., Jiang, Y., Gao, Y., Wang, X., Detre, J. A., Yao, S. & Rao, H. (2017). State-Independent and Dependent Neural Responses to Psychosocial Stress in Current and Remitted Depression. Am J Psychiatry 174, 971-979. 10.1176/appi.ajp.2017.16080974. Moodie, C. A., Suri, G., Goerlitz, D. S., Mateen, M. A., Sheppes, G., McRae, K., Lakhan-Pal, S., Thiruchselvam, R. & Gross, J. J. (2020). The neural bases of cognitive emotion regulation: The roles of strategy and intensity. Cogn Affect Behav Neurosci 20, 387-407. 10.3758/s13415-020-00775-8. Murray, R. J., Schaer, M. & Debbane, M. (2012). Degrees of separation: a quantitative neuroimaging meta-analysis investigating self-specificity and shared neural activation between self- and otherreflection. Neurosci Biobehav Rev 36, 1043-59. 10.1016/j.neubiorev.2011.12.013. Nasso, S., Vanderhasselt, M. A., Demeyer, I. & De Raedt, R. (2019). Autonomic regulation in response to stress: The influence of anticipatory emotion regulation strategies and trait rumination. Emotion 19, 443-454. 10.1037/emo0000448. 30.

(33) Northoff, G. (2005). Is emotion regulation self-regulation? Trends Cogn Sci 9, 408-9; author reply 40910. 10.1016/j.tics.2005.06.005. Northoff, G., Heinzel, A., de Greck, M., Bermpohl, F., Dobrowolny, H. & Panksepp, J. (2006). Selfreferential processing in our brain--a meta-analysis of imaging studies on the self. Neuroimage 31, 440-57. S1053-8119(05)02515-2 [pii] 10.1016/j.neuroimage.2005.12.002. Phillips, M. L., Ladouceur, C. D. & Drevets, W. C. (2008). A neural model of voluntary and automatic emotion regulation: implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Mol Psychiatry 13, 829, 833-57. 10.1038/mp.2008.65. Pizzagalli, D. A. (2011). Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology 36, 183-206. 10.1038/npp.2010.166. Prevost, C., Lau, H. & Mobbs, D. (2018). How the Brain Converts Negative Evaluation into Performance Facilitation. Cereb Cortex 28, 602-611. 10.1093/cercor/bhw400. Pruessner, J. C., Dedovic, K., Khalili-Mahani, N., Engert, V., Pruessner, M., Buss, C., Renwick, R., Dagher, A., Meaney, M. J. & Lupien, S. (2008). Deactivation of the limbic system during acute psychosocial stress: evidence from positron emission tomography and functional magnetic resonance imaging studies. Biol Psychiatry 63, 234-40. 10.1016/j.biopsych.2007.04.041. Putnam, K. M. & Silk, K. R. (2005). Emotion dysregulation and the development of borderline personality disorder. Dev Psychopathol 17, 899-925. 10.1017/s0954579405050431. Quaedflieg, C. W., van de Ven, V., Meyer, T., Siep, N., Merckelbach, H. & Smeets, T. (2015). Temporal dynamics of stress-induced alternations of intrinsic amygdala connectivity and neuroendocrine levels. PLoS One 10, e0124141. 10.1371/journal.pone.0124141. Radstaak, M., Geurts, S. A., Brosschot, J. F., Cillessen, A. H. & Kompier, M. A. (2011). The role of affect and rumination in cardiovascular recovery from stress. Int J Psychophysiol 81, 237-44. 10.1016/j.ijpsycho.2011.06.017. Reich, D. B., Zanarini, M. C. & Fitzmaurice, G. (2012). Affective lability in bipolar disorder and borderline personality disorder. Compr Psychiatry 53, 230-7. 10.1016/j.comppsych.2011.04.003. Reichenberger, J. & Blechert, J. (2018). Malaise with praise: A narrative review of 10 years of research on the concept of Fear of Positive Evaluation in social anxiety. Depress Anxiety 35, 1228-1238. 10.1002/da.22808. Reichenberger, J., Wiggert, N., Agroskin, D., Wilhelm, F. H. & Blechert, J. (2017). No praise, please: Depressive symptoms, reactivity to positive social interaction, and fear of positive evaluation. J Behav Ther Exp Psychiatry 54, 186-194. 10.1016/j.jbtep.2016.08.007. Rosenbaum, D., Thomas, M., Hilsendegen, P., Metzger, F. G., Haeussinger, F. B., Nuerk, H. C., Fallgatter, A. J., Nieratschker, V. & Ehlis, A. C. (2018). Stress-related dysfunction of the right inferior frontal cortex in high ruminators: An fNIRS study. Neuroimage Clin 18, 510-517. 10.1016/j.nicl.2018.02.022. Schafer, J. O., Naumann, E., Holmes, E. A., Tuschen-Caffier, B. & Samson, A. C. (2017). Emotion Regulation Strategies in Depressive and Anxiety Symptoms in Youth: A Meta-Analytic Review. J Youth Adolesc 46, 261-276. 10.1007/s10964-016-0585-0. Somerville, L. H. (2013). Special issue on the teenage brain: Sensitivity to social evaluation. Curr Dir Psychol Sci 22, 121-127. 10.1177/0963721413476512. 31.

(34) Somerville, L. H., Heatherton, T. F. & Kelley, W. M. (2006). Anterior cingulate cortex responds differentially to expectancy violation and social rejection. Nat Neurosci 9, 1007-8. 10.1038/nn1728. Somerville, L. H., Kelley, W. M. & Heatherton, T. F. (2010). Self-esteem modulates medial prefrontal cortical responses to evaluative social feedback. Cereb Cortex 20, 3005-13. 10.1093/cercor/bhq049. Tomasetto, C., Alparone, F. R. & Cadinu, M. (2011). Girls' math performance under stereotype threat: the moderating role of mothers' gender stereotypes. Dev Psychol 47, 943-9. 10.1037/a0024047. Torrisi, S., Moody, T. D., Vizueta, N., Thomason, M. E., Monti, M. M., Townsend, J. D., Bookheimer, S. Y. & Altshuler, L. L. (2013). Differences in resting corticolimbic functional connectivity in bipolar I euthymia. Bipolar Disord 15, 156-66. 10.1111/bdi.12047. Treynor, W., Gonzalez, R. & Nolen-Hoeksema, S. (2003). Rumination reconsidered: A psychometric analysis. Cognitive Therapy and Research 27, 247-259. Doi 10.1023/A:1023910315561. Vaisvaser, S., Lin, T., Admon, R., Podlipsky, I., Greenman, Y., Stern, N., Fruchter, E., Wald, I., Pine, D. S., Tarrasch, R., Bar-Haim, Y. & Hendler, T. (2013). Neural traces of stress: cortisol related sustained enhancement of amygdala-hippocampal functional connectivity. Front Hum Neurosci 7, 313. 10.3389/fnhum.2013.00313. van den Heuvel, M. W. H., Stikkelbroek, Y. A. J., Bodden, D. H. M. & van Baar, A. L. (2020). Coping with stressful life events: Cognitive emotion regulation profiles and depressive symptoms in adolescents. Dev Psychopathol 32, 985-995. 10.1017/S0954579419000920. van Marle, H. J., Hermans, E. J., Qin, S. & Fernandez, G. (2009). From specificity to sensitivity: how acute stress affects amygdala processing of biologically salient stimuli. Biol Psychiatry 66, 649-55. 10.1016/j.biopsych.2009.05.014. Versace, A., Almeida, J. R., Quevedo, K., Thompson, W. K., Terwilliger, R. A., Hassel, S., Kupfer, D. J. & Phillips, M. L. (2010). Right orbitofrontal corticolimbic and left corticocortical white matter connectivity differentiate bipolar and unipolar depression. Biol Psychiatry 68, 560-7. 10.1016/j.biopsych.2010.04.036. Wang, J., Rao, H., Wetmore, G. S., Furlan, P. M., Korczykowski, M., Dinges, D. F. & Detre, J. A. (2005). Perfusion functional MRI reveals cerebral blood flow pattern under psychological stress. Proc Natl Acad Sci U S A 102, 17804-9. 10.1073/pnas.0503082102. Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M. & Nichols, T. E. (2014). Permutation inference for the general linear model. Neuroimage 92, 381-97. 10.1016/j.neuroimage.2014.01.060. Yang, J., Xu, X., Chen, Y., Shi, Z. & Han, S. (2016). Trait self-esteem and neural activities related to selfevaluation and social feedback. Sci Rep 6, 20274. 10.1038/srep20274.. 32.

(35)