Functions of the ventromedial prefrontal cortex in emotion regulation under stress

Recent neuroimaging studies suggest that the ventromedial prefrontal cortex (vmPFC) contributes to regulation of emotion. However, the adaptive response of the vmPFC under acute stress is not understood. We used fMRI to analyse brain activity of people viewing and rating the emotional strength of emotional images after acute social stress. Here, we show that the vmPFC is strongly activated by highly emotional images, indicating its involvement in emotional regulation, and that the midbrain is activated as a main effect of stress during the emotional response. vmPFC activation also exhibits individual differences in behavioural scores reflecting individual reactions to stress. Moreover, functional connectivity between the vmPFC and midbrain under stress reflects stress-induced emotion regulation. Those results suggest that the functions of the network including the vmPFC in emotion regulation is affected by stress depending on the individuals' level of reaction to the stress.


Results
Twenty-one young males (mean ± SD, 23.10 ± 1.81 years) with no psychiatric disorders, claustrophobia, or neuroendocrine disorders were recruited for two experimental days. Since female stress responses are altered by menstrual cycles 25 (Kajantie et al., 2006), we enrolled only male participants in this study, as in previous studies 26 (see the "Discussion" for this limitation). Each day comprised a stress induction test and an fMRI task (Fig. 1A). On the first day (stress condition), all participants completed the Trier Social Stress Test (TSST) 27 , which induces acute social stress and comprises three parts: preparation of an interview about future work (5 min), an interview about future work (5 min), and a difficult arithmetic task. After the TSST, participants conducted an emotion task in the MRI scanner. Participants looked at emotional photographic images and evaluated the strengths of their six basic emotions (happiness, sadness, fear, anger, disgust, surprise) and preferences for the images (Fig. 1B, see "Methods"). On the second day (control condition), participants underwent the same procedure as on the first day, except for the stress induction test. Consistent with a previous study 28 , participants performed free speech and easy arithmetic tasks. We also evaluated the physiological and psychological responses to stress by measuring the State-Trait Anxiety Inventory-JYZ (STAI-JYZ; a self-reported assessment) 29 and salivary alpha-amylase levels, respectively, multiple times before and after stress induction.
Physiological and psychological responses to stress. To test whether our experimental procedure could provoke stress in participants, we measured salivary alpha-amylase several times during the experiment, because salivary alpha-amylase levels are increased by acute stress 30 . We set the onset (± 0 min) as the start of the TSST and measured saliva at − 10 min, + 5 min, + 10 min, + 20 min and + 30 min (Fig. 1A). We calculated the percentage change in salivary alpha-amylase between − 10 min (before the TSST) and + 10 min (after the final speech during the TSST) under both stress and control conditions. Under stress, salivary alpha-amylase was significantly increased compared with the control condition (t(20) = 3.09, P < 0.01 on paired t-test; Fig. 2A). Thus, stress was thus successfully induced under the test conditions.
To assess the effect of stress on psychological aspects, we measured the STAI-JYZ at − 40 min (before the TSST), + 13 min (after the TSST) and + 80 min (after the fMRI task) ( Fig. 1A and "Methods"). STAI Y-1 (state anxiety) showed significant interactions between condition (stress, control) × time (− 40 min, + 13 min, + 80 min) (F (2,114) = 8.68, P < 0.001 on two-way repeated measures ANOVA; Fig. 2B), but no significant interaction or main effects were found in STAI-Y2 (trait anxiety). In addition, Tukey's HSD multiple comparison test revealed a difference in state anxiety immediately after the TSST between stress and control conditions. This indicated that state anxiety was induced only by acute stress, but did not persist. Therefore, a brief stress state was provoked by the TSST (acute social stress). Both stress and control conditions had the same schedule, but the content of the TSST differed between the two conditions. Onset (± 0 min) was set as the start of the stress test. (B) The emotion evaluation task in MRI. Participants underwent an emotion evaluation on a 7-point scale for photographic images displayed on the screen. Evaluations comprised a preference and six basic emotions (happiness, sadness, fear, anger, disgust and surprise). There were four sessions, and 19 images were evaluated in each session.  Fig. 4A,B) and left pre-supplementary motor area (SMA) (peak at x, y, z = − 6, − 28, 72; F-value = 10.91; cluster size = 162 mm 3 ; Fig. 4A,B) were identified as a main effect of valence when participants looked at a picture for 3 s (all result maps were tested with a cluster-defining threshold of P < 0.001 and cluster probability of P < 0.05, and were FWEcorrected). We performed correlation analysis between parameter estimates (mean regression coefficients of all

ROI-to-ROI analysis of functional ROIs. To evaluate the relationship between regions involved in emo-
tion evaluation under stress, we conducted functional connectivity analysis of functional ROIs extracted from the imaging results with ANOVA, which were the right vmPFC, left pre-SMA, and left midbrain. ROI-to-ROI correlation analysis was performed to calculate the Z-transformed correlation coefficient to the average BOLD signal in the ROI voxels between time-series signals. We tested the contrast for both stress and control conditions, in which functional connectivity between targets was significantly larger than 0. We found significant functional connectivity between the vmPFC and the midbrain in the stress condition, but not in the control condition (r = − 0.45, P = 0.048; Fig. 6A). We applied correlation analysis using the change in STAI Y-1 (state anxiety) before and after the TSST and the functional connectivity between the vmPFC and midbrain. The functional connectivity in the stress condition showed a significant negative correlation with the change in STAI Y-1, unlike that in the control condition (r = − 0.1, P = 0.67; Fig. 6B).

Discussion
In an fMRI task after acute social stress, activity of the prefrontal region differed according to image valence category and responded to emotional stimuli, but activity of the midbrain differed according to stress condition and as an emotional response. In addition, activity and connectivity of the vmPFC exhibited individual differences in anxiety in response to stress. Our findings reveal an important mechanism of emotion regulation in response to stress and reflect the level of modulation induced by a stressor.
Consistent with our hypothesis, activity of the vmPFC under stress showed a negative correlation with the STAI-Y2 score (trait anxiety). This individual difference (higher trait anxiety) may reflect a vulnerability to stress. Previous studies showed that stress also potentiates anxiety and impairs emotion regulation [31][32][33] and that higher trait anxiety contributes to individual differences in vulnerability to stress 34 . Thus, it seems that this result indicates vulnerability of emotion regulation to stress. However, the vmPFC was detected as a main effect of valence, regardless of stress. Although the vmPFC encodes emotional stimuli or subjective value and regulates negative emotion 9,11,[35][36][37] , emotion regulation of the vmPFC is thought to be related to fear extinction and suppression and to respond to the stimulus itself 22 . Thus, it is not related to the stress per se. Taken together, this suggests that under stress, the vmPFC expresses vulnerability as a result of negative emotion regulation when responding to emotional stimuli.
Here, the pre-SMA showed a main effect of valence. This is consistent with a previous meta-analysis of cognitive emotion regulation showing that the pre-SMA may be involved in execution of regulation 38 . We also observed that the pre-SMA only exhibited a positive correlation with subjective evaluation scores of negative emotions (sadness, fear, and anger) in the stress condition. Previous studies indicated that the pre-SMA reflected the success of emotion regulation for limiting negative context 39 . The pre-SMA was activated in reappraising the emotional evaluation of aversive images, and activation of the pre-SMA predicted the success of reappraisal (i.e., more strongly pre-SMA activated, less negative evaluation). Even though participants in our experiment were In terms of emotional strength, activity of the midbrain decreased under stress compared with the control condition. The midbrain is involved in not only homeostasis but also emotion 40 .Previous findings that acute stress deactivated limbic regions and that the midbrain was related to the initiation of stress 41 supports our results. It seems that stress affects our emotional reactivity, deactivates the midbrain, and influences our emotion regulation in homeostasis. Even more interestingly, the MNI coordinates of the peak value of the midbrain, shown as a main effect of stress, nearly corresponded with those of the locus coeruleus (LC) in a recent meta-analysis study 42 . The LC is the main source of noradrenaline and projects to various brain regions 43 . Activity of the LC is independent of affective valence and is associated with acquisition of emotional inputs, storing of emotional memory, and maintenance of the cognitive process 44,45 . In addition, the reduced response of the LC implies resilience promotion, which is consistent with our whole-brain analytical findings for the midbrain 46 . Taken together, one possible interpretation is that the part of the brainstem in which we found a main effect of stress, may collaborate with other brain regions and to help mediate the psychological response to stress. However, we could not identify the peak region accurately, because it is difficult to detect the LC with standard MRI methods 47 .
Finally, in the stress condition, functional connectivity between the vmPFC and midbrain showed individual differences in state anxiety. State anxiety reflects a high stress condition 48 , which supports our results. In addition, cooperation of the vmPFC with limbic regions and the brain stem is important for emotion regulation and stress resilience 18,49 . Indeed, these circuits contribute to negative emotion processing, motivation in behaviour, and fear extinction [50][51][52] . Recent human MRS studies suggest neurochemical functions of the vmPFC in emotion processing with these circuits. Local GABA levels in the vmPFC exhibited this relation in functional connectivity between vmPFC and the amygdala and individual's anxiety level 15,16 . However, our findings could not detect effects of valence and stress on limbic regions, especially the amygdala, which is involved in emotion processing. Previous studies indicated that the connection between the vmPFC and amygdala involves negative emotion 12,50 . In addition, brain regions involved in negative emotion and stress responses can overlap 53 . Although the amygdala is thought to be involved in emotion processing, it responds strongly to emotion processing stimulated by facial expressions 54 . The stimulus sets used in our experiment were not specific to facial expressions. They included various categories of stimuli, and stimulus valence scores were balanced. That may be why activity of the amygdala has not been observed (Table 1).
Our finding that functional connectivity between the vmPFC and midbrain shows individual differences in emotion regulation in response to stress provides new insight into treatments aimed at improving stress vulnerability and suppressing psychiatric disorders, such as major depressive disorder. From the standpoint of emotion regulation, some psychiatric disorders are thought to be characterised by a vulnerability in emotion regulation, and emotion regulation is considered essential for mental health 55,56 . Our findings will help to measure stress In addition, functional connectivity real-time neurofeedback has been applied to some psychiatric disorders 58,59 . Future research is needed to establish new biomarkers for certain psychiatric disorders using neurofeedback and DBS. The generalizability of our results is limited by the sequence of experimental conditions. Participants first completed the stress condition, which was followed by the control condition. As a result, a framing effect may have occurred in our experiment. However, participants were exposed to acute stress in our study and they completed the control condition at least 2 days after the stress test. In terms of the brain network and neuroendocrine level in response to stress, recovery from acute stressors occurs over several hours 60 . Thus, we believe that prior stress did not affect the control condition. However, we cannot completely exclude the possibility that it may have had a framing effect. Accordingly, future studies that randomly separate stress and control conditions are needed. Another limitation is that we enrolled only male participants in this study. Female stress responses are altered by menstrual cycles and oral contraceptive use 25,26 . Previous studies measuring stress biomarkers have also carefully examined their effects. For example, in a recent systematic review of TSST studies that tested stress biomarkers in saliva 26 , 12 of 35 studies reviewed included only males. However, previous studies showed that the PFC response to stressors differs between male and female rats 61 . The PFC is involved in emotion regulation, but its activity during emotion regulation differs between men and women 62 . Taken together, sex differences may cause a specific emotion regulation effect in response to stress. Thus, the generalizability of our results to females remains an open question.

Conclusion
To summarise, under acute social stress conditions, prefrontal regions, for example, the vmPFC and pre-SMA, showed individual differences in emotion regulation, such as subjective evaluation and trait anxiety. In addition, functional connectivity between the vmPFC and the midbrain LC showed a negative correlation with variation in the acute social stress effect. Our results suggest that the functions of the network including the vmPFC in emotion regulation is affected by stress depending on the individuals' level of reaction to the stress.

Methods
Participants. Twenty-one right-handed healthy adult males (mean age ± SD, 23.10 ± 1.81 years; range, 21-28 years) participated in our experiments. Since female stress responses are altered by menstrual cycles 25,63 , only males were recruited. People without psychiatric disorders, claustrophobia, or neuroendocrine disorders were recruited. Participants were asked if they were taking medication (2 weeks before experiments). They were requested not to consume caffeine or citrus juice for at least 4 h before experiments. They also refrained from heavy exercise, eating/drinking and smoking for at least 2 h before experiments, and from alcohol for 24 h before experiments. Due to an omission in the State-Trait Anxiety Inventory-JYZ (STAI-JYZ), one participant was excluded from correlation and statistical analyses involving the STAI-JYZ. All participants provided informed consent prior to the experiment. All experimental procedures were approved by ATR Review Board Ethics Committee. All procedures were performed in accordance with relevant guidelines and regulations.
Experimental procedure. All participants underwent the stress condition on the first experimental day and the control condition on the second experimental day. The maximum interval between the first and second experimental days was 5 months. On the first experimental day, an experimenter explained the ethical and safety aspects of the study to all participants. After informed consent was obtained, participants rested for 20 min for both stress and control conditions. They then underwent the stress induction test for 13 min (see 'Stress induction test' for details). After the stress induction test, participants entered the fMRI scanner and performed the emotional task (comprising 76 trials separated into four blocks). We repeatedly measured the STAI-JYZ and salivary alpha-amylase before, during and after the stress induction test and fMRI task (see Fig. 1A for the exact timing of measurements). In keeping with previous work 64 , salivary alpha-amylase was measured between 14:10 and 16:40 to minimise diurnal variation of alpha-amylase secretion. Thus, we conducted all experiments between 13:30 and 17:30. www.nature.com/scientificreports/ Stress induction test. All participants underwent the TSST according to a previously published protocol 27,28 . On the first day, participants prepared for a job interview (5 min), completed the job interview speech (5 min), and performed a difficult arithmetic task (3 min) (stress condition). On the second day, participants completed a placebo version of the TSST comprising free speech (5 min), speaking (5 min) and an easy arithmetic task (3 min) (control condition). Both tests were conducted in a small room that was separated from the fMRI scanning room. During the stress condition, participants were interviewed by clinical psychologists (certified in Japan).
Emotion evaluation in the fMRI task. The fMRI task started 33 min after the start of the TSST. In the MRI scanner, participants looked at natural photographic images and evaluated the strengths of their emotions and preferences for the images (Fig. 1B) 65,66 . Valence scores of NAPS datasets are set from 1 to 9. Images were selected from three valence categories by reference to a previous study 65 of the NAPS (1-4 points, negative; 4-6 points, medium; 6-9 points, positive). In addition, to prevent the serial position effect, which is the tendency of participants to learn when continuously shown the same category and image valences, the number of continuous presentations was limited to two for the same valence and three for the same category. Numbers of images for each valence and category are detailed in Table 1. In both the stress and control conditions, the experimental procedure was the same, and image stimuli used in the task are shown in Table 1.
The analytical design comprised within-participant factors of two conditions (stress and control conditions) and three image valences (negative, medium and positive), and the fMRI and evaluation analyses used the factors in a full factorial design and two-way repeated measures ANOVA. An overview of each trial is shown in Fig. 1B (see 'fMRI procedure' in "Methods" for fMRI analysis information).

Measurement and analysis of salivary alpha-amylase.
To evaluate the effect of stress on physiological responses, we measured alpha-amylase in saliva with a salivary amylase monitor (NIPRO, Japan). Saliva can easily be collected with this device and it only requires 1 min to determine alpha-amylase levels. With onset (± 0 min) set as the start of the TSST, saliva was measured in all participants at − 10 min, + 5 min, + 10 min, + 20 min and + 30 min. A paired t-test was used to calculate the percent change between − 10 min and + 10 min.

Measurement and analysis of the STAI.
To evaluate effects of stress on psychological responses, we used the STAI, which comprises a 20-item scale for measuring state and trait levels of anxiety 67 . Participants selected a score from 1 to 4 (where 1 = not at all, 4 = very much). We added scores for each item. We used the STAI-JYZ, an official Japanese translated version of the STAI (form-Y). STAI Y-1 (state level) is used to examine acute responses to anxiety, whereas STAI Y-2 (trait level) can measure a stable tendency of the response to a fear stimulus 29 . Measurement times were before the TSST (− 40 min), after the TSST (+ 13 min), and after the fMRI task (+ 80 min). We applied two-way repeated measures ANOVA to test for effects of time on the measure and on stress. MRI data pre-processing and statistical analysis. All MRI data were converted from DICOM format to NIfTI format. During the process, for the T2* image data of the fMRI task (and not for the T1-weighted image data), the first five scans were excluded as dummy scans. Data were pre-processed using Statistical Parametric Mapping 12 (SPM12, update revision number 7487, Wellcome Department of Cognitive Neurology, London, UK, https:// www. fil. ion. ucl. ac. uk/ spm) toolbox and MATLAB 2018a (version 9.4.0, The MathWorks, Inc., Natick, Massachusetts, https:// matlab. mathw orks. com). Images were realigned to the intermediate image as a reference, and volumes underwent segmentation and normalisation to MNI space and were spatially smoothed with an 8-mm full-width at half-maximum Gaussian kernel. A general linear model (GLM) was applied to pre-processed data. We focused on BOLD signals during two periods: the 3-s image display and the 20-s evaluation. We conducted first-level GLM analyses with a separate designed matrix for those two periods. In each period, all trials were sorted based on image valence (negative, medium and positive) and were defined by three box-car regressors with a duration of 3 s (image display periods) or 20 s (evaluation periods). These factors were convolved with a hemodynamic response function (HRF). Realignment parameters (three translations and three rotations) were added as multiple regressors of head movement. In total, we defined four first-level design matrices for each participant: two periods (image display, evaluation) × two conditions (stress, control). For second-level analysis, we used a flexible factorial model with condition (stress, control) and valence (negative, medium, positive). The model was separately applied to each www.nature.com/scientificreports/ period of the image display (3 s) and evaluation (20 s). All results of the second-level analysis were corrected using cluster-level inference (a cluster-defining threshold of P < 0.001, cluster probability of P < 0.05, FWE-corrected).

MRI data acquisition.
Functional connectivity analysis. Functional connectivity analysis was performed using CONN (ver.17.f, https:// web. conn-toolb ox. org). From results of the flexible factorial model in fMRI analysis, three brain regions were used as functional ROIs, which comprised the vmPFC, pre-SMA and midbrain. ROIs of the pre-SMA and vmPFC were defined by clusters detected from the flexible factorial model, but the midbrain was extracted from a 5-mm radius area around the peak coordinates of the midbrain (MNI: x, y, z = − 4, − 32, − 24). First, pre-processing and denoising were applied. Pre-processing comprised realignment and unwarp, slice timing correction, segmentation, normalisation, outlier detection, and smoothing (smoothing kernel FWHM of 8 mm). Outlier detection was applied to identify scans with motion larger than the 95% percentile, and these outliers were scrubbed in denoising. Denoising was performed by two methods: 1) linear regression of potential confounding factors in the BOLD signal, including realignment parameters and scrubbing extracted from outlier detection, and 2) temporal band-pass filtering. Next, first-level analysis was performed using regressors of valence and stress conditions in each trial (analytical matrix settings were the same as those of the first-level analysis of SPM12). Regressors were convolved with HRF. ROI-to-ROI connectivity analysis was performed to calculate the Z-transformed correlation coefficient for the averaged BOLD signal across all voxels in the ROI. The duration of the analysis was set to that of the evaluation periods (20 s in each trial). We tested whether functional connectivities between target ROIs were significantly larger than 0 under each of the stress and control conditions.