Acute stress-related aberrant prefrontal based functional connectivity in high ruminators: An fNIRS study

Acute stress-related aberrant prefrontal based functional connectivity in high ruminators: An fNIRS study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Acute stress-related aberrant prefrontal based functional connectivity in high ruminators: An fNIRS study Lanxin Peng, Jixin Long, Qian Li, Lijing Niu, Haowei Dai, Jiayuan Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3842177/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Rumination, thought to be induced by stressful events, is a pivotal factor contributing to cognitive vulnerabilities in stress-related disorders. Previous studies have demonstrated an association between the prefrontal cortex and stress. However, the functional connectivity in the prefrontal of high ruminators during stress is not fully understood. Methods 28 high trait rumination group (HTR) and 22 low trait rumination group (LTR) were recruited. Each participant underwent both the Trier Social Stress Test (TSST) and control task in a long-arm crossover design, while collecting functional near-infrared spectroscopy data. We analyzed the static and dynamic FC (DFC) under two different conditions and then compared the difference between the HTR and the LTR. Results Stress induction procedure was highly successful in both HTR and LTR. Analysis on static FC (SFC) showed that LTR exhibited a marked increase in SFC during the TSST, while HTR showed a comparatively lesser increase. Further analysis on DFC, the prefrontal-based DFCs were higher in LTR during TSST compared with control condition, but these patterns were not in HTR. But higher variability of DFC between left IFG and left MFG related to higher state rumination. Conclusion Current study may shed light on the aberrant prefrontal functional connectivity pattern underlying rumination and its association with stress. Further research in this area may elucidate the specific cognitive control mechanisms that are impaired in high ruminators and their impact on emotional regulation and psychological well-being. fNIRS stress rumination functional connectivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Rumination is a mode of responding to distress involving repetitively and passively focusing on symptoms of distress and on the possible causes and consequences of these symptoms (Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008 ). Higher ruminative style is associated with sustained processing of negative information (Duque, Sanchez, & Vazquez, 2014 ), which can reinforce negative feelings. Previous studies have underscored the influence of rumination transcends specific mood disorders, with implications for a spectrum of psychopathologies including anxiety, binge eating, binge drinking and self-harm (Nolen-Hoeksema et al., 2008 ). Investigating rumination may provide valuable insights into the underlying factors of psychological disorders, leading to improved strategies for enhancing mental well-being. Many studies have illuminated the intricate relationship between stress and rumination. On one hand, stress can trigger or intensify rumination, as individuals often engage in rumination in response to stressful events (Smith & Alloy, 2009 ). This correlation suggests that rumination may be a cognitive response mechanism induced by acute stress. On the other hand, rumination itself can exacerbate the impact of life stressors, potentially creating a feedback loop (Hruska, Zelic, Dickson, & Ciesla, 2017 ). For instance, individuals with high levels of rumination may face challenges in generating adaptive responses to stressors, which in turn may further intensify their rumination(Nasso, Vanderhasselt, Demeyer, & De Raedt, 2019 ). Moreover, perseverative cognition, encompassing worry and rumination, has been shown to prolong stress-related affective and physiological responses, both before and after exposure to stressors (Brosschot, Gerin, & Thayer, 2006 ). Given these perspectives on the dynamic interplay between stress and rumination, an understanding of their neurological underpinnings becomes crucial. In the realm of neuroscience, previous research has demonstrated the significant role of the prefrontal cortex in processing stress (Arnsten, 2015 ; McKlveen et al., 2016 ; Ossewaarde et al., 2011 ; Worley, Hill, & Christianson, 2018 ). The prefrontal cortex assumes a crucial role in emotional regulation and stress coping, being influenced by different stress levels and individual responses(Kumar et al., 2014 ; Suzuki & Tanaka, 2021 ). A recent study also highlights the effect of functional connectivity (FC) between the ventrolateral prefrontal cortex (VLPFC) and dorsolateral prefrontal cortex (DLPFC) under stress (Al-Shargie et al., 2022 ). Importantly, recent study showed that connectivity between the prefrontal cortex and the limbic system plays a significant role in regulating how state rumination impacts stress recovery(Luo, Li, Zhang, & Pan, 2023 ). Herein, targeting prefrontal cortex may contribute to better understanding of the relationship between rumination and stress. Indeed, previous studies have linked prefrontal hypoactivation during stress with heightened state rumination (Int-Veen, Fallgatter, Ehlis, & Rosenbaum, 2023 ; Rosenbaum, Thomas, et al., 2018 ). For example,Rosenbaum, Thomas, et al. ( 2018 ) identified reduced cortical activity in the right inferior frontal gyrus (IFG) among individuals with high rumination levels during stress. However, there exist gaps in our understanding that need to be addressed. Firstly, previous common stress induction methods, such as trial-by-trial tasks(Dagher, Tannenbaum, Hayashi, Pruessner, & McBride, 2009 ; Sudimac, Sale, & Kühn, 2022 ; Wagels et al., 2017 ), might not accurately replicate the intricate and dynamic nature of real-life stress experiences. Unlike trial-by-trial tasks, the Trier Social Stress Test (TSST) effectively induces acute stress in a laboratory setting while maintaining real-life relevance(Skoluda et al., 2015 ). The application of TSST could enhance our comprehension of stress responses in real-life scenarios. Secondly, the analysis in previous studies was limited to examining brain activity in specific brain regions but understanding the relationship between rumination and stress requires consideration of the coordination among multiple brain regions. To address this limitation, we intend to employ FC analysis. FC, defined as temporal coincidence of spatially distant neurophysiological events (Friston, 1994 ), enables us to uncover synergies between brain regions. Furthermore, FC is especially well-suited for analyzing prolonged tasks. Unlike tasks involving blocks that last for less than 30 seconds, the TSST lacks this structural characteristic, rendering it unsuitable for typical task-state data analysis methods. Thus, FC is better suited for assessing the neural underpinnings of TSST compared to activation analysis. Despite extensive research on the brain mechanism under stress, there is a notable gap in understanding the functional connectivity associated with rumination under stress. fNIRS is an optical imaging method that has been proven to be compatible with the standard procedure of the TSST (Rosenbaum, Thomas, et al., 2018 ). To explore differences in inter-regional neural connectivity, we employed both static and dynamic FC analyses. Compared to static FC, dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior (Hutchison et al., 2013 ). The study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems (E. A. Allen et al., 2014 ). Compared with functional Magnetic Resonance Imaging (fMRI), fNIRS is less vulnerable to motion artefacts(Zhang, Wang, Leong, Mao, & Yuan, 2023 ). Therefore, the utilization of fNIRS-based functional brain connectivity enables us to investigate the interconnected activities among various brain regions involved in rumination under stress. In this study we used fNIRS to investigate the behavioral and neural response patterns of high ruminators under acute stress. At behavioral level, we hypothesized that high trait ruminators will show higher stress level and state rumination compared to those with low trait ruminators during stress context. At the brain level, we hypothesize dysfunctional prefrontal cortex functional connectivity in high trait ruminators, potentially impacting their stress response. Additionally, we anticipated that functional connectivity of individual would display distinct patterns during stressful contexts compared to non-stressful conditions. 2. Methods 2.1 Participants Participants from the Southern Medical University completed the Chinese Version of Ruminative Response Scale (RRS)(Han & Yang, 2009 ). Screening exclusion criteria included acute physical disease, neurological disease, substance abuse, chronic or acute disease affecting brain function, such as diabetes or kidney failure, arrhythmias, and other heart conditions. Out of the 296 subjects who completed the questionnaire, the top 30% scoring above 50 were identified as the high trait rumination group (HTR), totaling 30 subjects. Subsequently, 25 subjects were selected as the low trait rumination group (LTR) based on scores from the bottom 30% scoring below 30. Each subject underwent both the TSST and control condition experiment, during which fNIRS data was collected. A crossover design was employed to mitigate individual differences. Half of the subjects from both the HTR and LTR groups were randomly assigned to undergo the stress condition experiment initially, followed by the control condition experiment after 1–2 weeks. Conversely, the remaining participants started with the control condition experiment and subsequently underwent the stress condition experiment after the same interval. Five participants were unable to participate in the second experimental session, resulting in the retention of 28 HTR and 22 LTR (Detailed demographic information could refer to Table S1 in the Supplementary). This study was reviewed and approved by the Ethics Committee of the Southern Medical University. The study adhered to all relevant guidelines and regulations governing research involving human subjects and conducted in strict accordance with the principles outlined in the Declaration of Helsinki. All participants were given written informed consent after acknowledging the experimental procedure and received appropriate monetary compensation after the experiments. 2.2 Procedure On the day of the TSST task, subjects were led to the fNIRS laboratory and seated at a table where the fNIRS machine was behind them. The TSST protocol consisted of a 5-minute presentation task preparation period, a 5-minute presentation task, and a 5-minute math subtraction task. Prior to the test, the experimenter instructed the participants to prepare an interview: Describe what they would like to do in the future and why they would be qualified for it. Participants were informed that their presentations would be recorded on video. Following the preparation period, two judges, maintaining serious and neutral expressions, entered the laboratory for the formal presentation task. After the speech task, participants were directed to engage in a 5-minute mathematical calculation task. They were asked to take turns subtracting 13 from 1022, verbally stating the calculation formula and answering as quickly and accurately as possible. In case of errors or prolonged response times during the mathematical calculation task, participants were required to start over. The whole pipeline of the experiment could be referred to Fig. 1 a. Prior to the experiment, all judges received extensive training and were explicitly instructed not to exhibit any positive feedback, such as smiling or nodding, to the participants during the experiment. This measure was implemented to ensure that participants perceived social threat factors to the highest degree, thereby inducing social psychological stress. In the control task, prior to the preparation phase, participants were instructed to introduce a favorite book or movie to the experimenter within a 5-minute timeframe. During the introduction, the experimenter provided friendly responses to the participants' social signals. After the allocated 5 minutes, participants were asked to recite the multiplication table, which they had already memorized skillfully during their primary school education. For the stability of cortisol, participants were advised to abstain from alcohol consumption the day before the measurement, maintain their usual sleep duration, and avoid engaging in physical activities on the measurement day. Also, subjects were told not to eat 1 hour before the measurement started. Insert Fig. 1here. 2.3 Subjective scoring and cortisol sampling To evaluate the effects of acute stress induction, five subjective stress indicators (stressful, unpleasant, difficult, annoyed, and fearful), state rumination and salivary cortisol samples were collected from subjects at various time points during the experiment. We use visual analogue scale (VAS) to assess subjective stress scores at baseline (T0) and after the end of the TSST (or control task) (T2). Using the scores at T2 minus the scores at T0, we calculated the “change index”, which reflects the subjective changes following the experiment. The change index represents the extent of change in subjective pressure experienced by the participants following the experimental conditions. Salivary cortisol samples were obtained at various intervals: before the start of the experiment (T0), after the end of the learning phase (T1), after the end of the TSST (T2), after the end of the Think/No Think phase (T3) and 15 minutes after the end of the recall phase (T4). Notably, the investigation of the Think/No Think task is outlined in a separate research article within this study, which does not relate to the current research objectives and will not be revisited in subsequent sections. We used the Brief State Rumination Inventory (BSRI) to measure the state rumination at different time points. The Chinese version of the BSRI consists of eight forward-scoring items, and the items are scored on a 100 mm VAS ranging from 0 (“strongly disagree”) to 100 (“strongly agree”). The total score is obtained by summing up all items (Wang, Song, Lee, & Zhang, 2022 ), assessments were conducted only at T0, T2 and T4 (Fig. 1 a). To be noticed that salivary cortisol samples were collected at five different time points during the experiment to assess the effectiveness of acute stress induction. Saliva samples were collected using salivettes (Sarstedt AG & Co., REF51.1534.500) and stored at − 78°C. For analysis of cortisol levels, salivettes were thawed and centrifuged for 10 min at 3000 rmp to collect saliva. Further analysis was performed with electrochemiluminescence. 2.4 fNIRS acquisition and preprocessing Brain activity was measured with the NIRScout system (NIRX, USA) with a temporal resolution of 4.46 Hz and two different wavelengths (780nm and 830nm ). The photoconductive array is fixed on the subject's head through an electrode cap, comprising 26 channels, consisting of 13 emitters and 13 detector probes. In this study, the probes were positioned according to the international EEG electrode standard 10–20 system and placed at the reference point Fpz. The specific channel locations were determined by conducting a registration and comparison process with Table S2 in the Supplementary, which presents the Anatomical Automatic Labeling (AAL) template partitions and MNI coordinate systems. The raw fNIRS data (Fig. 1 b) underwent preprocessing using the Homer2 toolbox (homer-fnirs.org). Preprocessing steps included the following: Identifying motion artifacts by applying a standard deviation threshold in addition to the amplitude threshold; Conducting motion correction using principal component analysis; Applying bandpass filtering (0.01–0.1 Hz) to attenuate high- and low-frequency noise; and enhancing the signal quality through correlation-based signal improvement. For analysis purposes, only the initial five minutes of the speech task were retained for further calculations. 2.5 Data analysis Behavioral data Behavioral data was performed using IBM SPSS Statistics version 26. To measure the condition effect and group effect after TSST and control task, a 2×2 mixed-design analysis of variance (ANOVA) was employed to examine the change index of subjective stress indicators, which is calculated as the difference between the subjective pressure rating after TSST (or control task) and the subjective pressure rating before TSST (or control task). At the same time, to investigate the difference between state rumination, a 2×2×3 mixed-design ANOVA was used. The experimental conditions (the TSST and control task) and timepoint, were treated as within-subject variables, while the subject groups (HTR and LTR) were considered as the between-subject variables. Since T2 timepoint (after TSST or control task) revealed the immediate effect of TSST, a 2×2 ANOVA was conducted for the T2 on the BSRI. Subsequently, independent samples t-tests were performed to compare the scores of HTR and LTR separately under different experimental conditions. The salivary cortisol levels were analyzed using 2×2×5(five-time points) repeated ANOVA, considering the time points (5 levels) and experimental conditions (2 levels: stress and control) as within-subject variables. Then, independent sample t test was conducted on salivary cortisol levels at 5 time points under two experimental conditions. Functional connectivity Static functional connectivity (SFC) The SFCs were analyzed in MATLAB version2018. To visualize the FC in HTR and LTR, we generated the correlation matrix maps containing all possible channel pairs (Fig. 1 c). Specifically, we calculate the Pearson correlation data of Δ[HbO] between each channel and false discovery rate (FDR) was used to correct the retention of p values above 0.05. Then, different channels were divided according to brain regions, and the correlated values of each subject's brain regions were averaged (such as Channel 1 in the left MFG). Each subject was given a value for each brain region. As a conventional method, the strength of SFCs here was measured by correlation values. After that, repeated ANOVA was directly used for the SFCs of LTR and HTR in two conditions. Dynamic functional connectivity (DFC) We also calculated the DFC of the signals with a sliding sliding-window correlation (SWC) approach (Hutchison et al., 2013 ). The SWC is the most commonly used strategy for examining FC dynamics in previous neuroimaging studies using the fNIRS (Li et al., 2015 ). In our SWC analysis, based on the sampling rate of 4.464, about 20-s time window was selected and then shifted in an increment of 3 time points of times series along the entire time course. The FC within each time window (Fig. 1 d) was quantitatively calculated for each selected pair of brain regions using the Pearson correlation strategy. To compare with the variability of DFC, we first quantitatively estimated the variance in the DFC fluctuations for each pair of channels (Fig. 1 e). Then, we calculated the mean variance at ROI level. Finally, to compare the variability of the DFC, we used 2×2 mixed design ANOVA between different groups and conditions. Relationships between SFC/DFC and Behavior In order to explore and understand how the FC patterns between specific brain regions correlate with behavioral aspects, particularly subjective stress and rumination, we also investigated the relationship between SFC/DFC and behavior. After achieving the functional connectivity between brain regions showing significant effects, we calculated the correlation SFC/DFC and subjective stress, BSRI was analyzed in these regions. 3. Results 3.1 Successful stress induction As indicated by repeated measurement ANOVA (group×condition), in both HTR and LTR, the subjective stress change index showed an increase from the control task to the TSST, including stressful ( F = 64.057, p < 0.001, η 2 = 0.572), unpleasant ( F = 99.310, p < 0.001, η 2 = 0.674), difficult ( F = 69.248, p < 0.001, η 2 = 0.591), annoyed ( F = 63.341, p < 0.001, η 2 = 0.569) and fearful ( F = 24.404, p < 0.001, η 2 = 0.337). However, no significant differences were found between HTR and LTR ( F = 0.413, p = 0.522). No significant interaction effects between condition and group were observed (Fig. 2 ). Insert Fig. 2 here. For cortisol changes, results from repeated ANOVA showed that the main effect of the experimental conditions ( F = 420.05, p < 0.001, η 2 = 0.897) and the main effect of the time points ( F = 71.232, p < 0.001, η 2 = 0.597) were significant on the change of salivary cortisol levels. It was shown that salivary cortisol increased significantly after TSST but decreased gradually under control task. While the main effect of groups ( F = 0.4, p = 0.530) and the interaction effect were not significant, dependent sample t-tests were then performed on the level of salivary cortisol under two experimental conditions at five time points in high and low trait ruminators separately. The results of the t-test of condition effect indicated that there were no significant differences in baseline salivary cortisol levels (T0) before receiving the TSST and those before the control task. However, after undergoing the TSST (or control task) (T2) and 15 minutes after completing the TSST (or control task) (T3), salivary cortisol levels of the subjects were significantly higher in TSST compared to those under the control task. The LTR group exhibited a similar pattern of cortisol changes (Fig. 3 ). 3.2 State rumination under stress context As for state rumination, results of three-way ANOVA (condition × group × timepoint) indicated a generally higher state rumination level for HTR – reflected by a main effect of group ( F = 9.609, p = 0.003, η 2 = 0.167), which means state rumination of HTR was higher than LTR. The two-way ANOVA (condition×group) of T2 indicated group ( F = 9.798, p = 0.003, η 2 = 0.170) and condition main effect ( F = 9.744, p = 0.003, η 2 = 0.169) of state rumination. These findings implied an increase in state rumination following the TSST in comparison to the control task. The results of the t-test comparing HTR and LTR revealed higher state rumination in HTR at all time points on the TSST day compared to LTR (Fig. 3 f). At the same time, we found a group by condition interaction for state rumination ( F = 4.86, p = 0.032, η 2 = 0.92), reflecting higher overall state rumination and higher increases in state rumination after TSST for HTR. Insert Fig. 3 here. 3.3 Static functional connectivity Following the ANOVA of SFC between or within eight distinct brain regions, several brain regions showed significant condition or group effect (Group average SFC matrices were shown in Fig. 4 a-d). Therein, the SFC between the right IFG and right middle frontal gyrus (MFG) yielded particular results. Specifically, this SFC exhibited significant effects of both the condition ( F = 7.362, p = 0.009, η 2 = 0.133) and group ( F = 16.786, p = 0.000, η 2 = 0.259) evidence of interaction effect ( F = 4.175, p = 0.047, η 2 = 0.080) between the two factors. During the TSST, the SFC between the right IFG and right MFG was observed to be significantly higher than during the control task. Furthermore, in both TSST and control task, the SFC in LTR was found to be significantly higher than in HTR. Regarding the further simple effects analysis, SFC increased significantly in the LTR group during the TSST ( F = 10.100, p = 0.003, η 2 = 0.174), while the increase among the HTR group was not as pronounced (Fig. 4 e & f). Insert Fig. 4 here. 3.4 Dynamic functional connectivity As described in Fig. 5 a, some of region pairs exhibited significant main effect. Region pairs with asterisks showed a significant condition effect in the variability of DFC. Specifically, the variability of the DFC in TSST was significantly higher than that in control task. At the same time, there was no significant group effect in any region. Besides, a significant interaction effect in the variability of DFC was observed in some regions as shown in Fig. 5 b. Specifically, during the control task, the LTR group consistently exhibited lower DFC variability compared to the TSST across various region pairs. Additionally, within the right MFG, the variability was significantly higher in the LTR during TSST compared to the HTR. The variability was significantly higher in the LTR during TSST compared to the HTR. Insert Fig. 5 here. 3.5 Relationships between SFC/DFC and Behavior After correlation analysis, we only found that the correlation between variability of DFC between left MFG and left IFG (Fig. 5 c) and BSRI was significant ( r = 0.210, p = 0.036) (Fig. 5 d). 4. Discussion To our best knowledge, this is the first study aiming to investigate the causes of high trait rumination by exploring the different behavioral and neural patterns between high trait ruminators and low trait ruminators in a social stress context using functional connectivity. Our findings indicate successful stress induction, with higher levels of state rumination observed in high ruminators in both acute stress and non-stress contexts. Further analysis on the fNIRS data revealed special patterns in functional connectivity among high trait ruminators, particularly evident during periods of stress. Moreover, the higher variability of prefrontal based DFC positively correlated with the state rumination under stress context. Overall, the findings highlight that the prefrontal functional connectivity patterns of high ruminators in stressful situations may be crucial to understanding their behavioral patterns. 4.1 High state rumination in high trait ruminators. In subjective stress, although we observed effective stress arouse in TSST, we didn’t find significant group differences between HTR and LTR. This outcome is consistent with some previous results that there were no significant differences in subjective stress levels after TSST between LTR and HTR (Rosenbaum, Hilsendegen, et al., 2018 ). Although HTR and LTR showed similar subjective stress level, HTR were observed higher state rumination after stress, indicating that trait rumination will influence stress-induced rumination (Laicher et al., 2022 ). Besides, both HTR and LTR were observed higher state rumination in TSST, compared to control task. This supports the notion that our stress induction, the TSST, is effective in eliciting stress-reactive rumination (Allen, Kennedy, Cryan, Dinan, & Clarke, 2014 ; Rosenbaum, Thomas, et al., 2018 ; Shull et al., 2016 ). During both TSST day and the initial phase of the control task (T1, before the control task began), HTR consistently showed higher state rumination. That proved that the tendency to engage in a ruminative response style appears to be a reasonably stable trait(Ehring, 2021 ). In other words, HTR might exhibit a greater inclination towards excessive analysis or emotional reactions, making it more challenging for them to overcome rumination in daily life. In line with the inference, recent study found that daily and trait-level rumination were correlated(Kovács et al., 2023 ). Thus, it is common for high ruminators to frequently experience heightened levels of state rumination in their daily lives. 4.2 Aberrant prefrontal functional connectivity patterns in high ruminators. As expected, there were different SFC patterns in prefrontal between LTR and HTR under TSST. Unlike LTR, the increase of SFC between the right IFG and the right MFG of HTR is less noticeable when switching from the control task to TSST. Moreover, the variability of DFC was influenced by both group difference and task conditions, particularly in the prefrontal regions and their connectivity with other brain regions. And interaction was mainly driven by the variability of LTR under different conditions. As shown from our result, the primary functional connectivity differences between HTR and LTR are observed within the prefrontal cortex. The prefrontal cortex is a critical region for cognitive control. Specifically, the prefrontal has long been associated with inhibitory processes (Miller & Cohen, 2001 ). For example, the right IFG is crucial for inhibition and attentional control. In stop signal task, previous studies indicated that the right IFG is recruited when important cues are detected (Hampshire, Chamberlain, Monti, Duncan, & Owen, 2010 ), and it is more strongly activated during more difficult stopping (Hughes, Johnston, Fulham, Budd, & Michie, 2013 ). Additionally, a previous study also indicated that the right MFG plays an important role in redirecting attention from exogenous to endogenous attention control (Japee, Holiday, Satyshur, Mukai, & Ungerleider, 2015 ). In our study, the TSST imposes higher cognitive demands that require increased involvement of the prefrontal cortex. The heightened functional connectivity between the right MFG and IFG during TSST may result from increased prefrontal engagement. Meanwhile, cognitive inhibition deficits are associated with rumination(Hasegawa et al., 2022 ; Hasegawa, Somatori, Nishimura, Hattori, & Kunisato, 2021 ). The association of trait rumination and inhibition is further supported by a meta-analysis indicating significant negative associations between rumination and inhibition (Yang, Cao, Shields, Teng, & Liu, 2017 ). Additionally, cognitive abilities are known to be vulnerable to the detrimental effects of stress, leading to impairments in various cognitive domains, including working memory (Nitschke, Giorgio, Zaborowska, & Sheldon, 2020 ), decision making (Porcelli & Delgado, 2017 ) and cognitive flexibility (Shields, Sazma, & Yonelinas, 2016 ). Hence, the interplay between trait rumination and inhibitory control during stressful context could potentially further impair cognitive function. Considering the role of the prefrontal cortex alongside our findings, we propose that the abnormal functional connectivity observed in the prefrontal cortex of high ruminators during TSST may indicate an inhibitory deficit specifically under stress conditions. The weaker SFC observed between the right IFG and right MFG in HTR during stress context in our study could potentially contribute to impaired inhibitory control ability in individuals with high trait rumination. Alternatively, the impaired inhibitory control ability resulting from weakened SFC may contribute to increased levels of rumination. Our findings demonstrated an increase in SFC within the right prefrontal cortex during the TSST compared to the control task, suggesting that higher cognitive demands may enhance connectivity within the prefrontal cortex. It is worth noting that while the SFC in HTR also increased during the TSST, the increase of SFC was not as pronounced compared to LTR. This observation may indicate that the cognitive control of HTR is not as good as that of LTR within the context of stress. Notably, the DFC between left MFG and IFG and scores of BSRI showed positive association. Previous fMRI studies have provided insights into the neural mechanisms of rumination, rumination proposed as related to functional activations in the DMN (Zhou et al., 2020) and cognitive control regions. The neural correlates include the dorsolateral prefrontal cortex (DLPFC)(Cooney, Joormann, Eugène, Dennis, & Gotlib, 2010 ) and medial prefrontal cortex (Burkhouse et al., 2017 ). This association is associated with inhibitory control and can be explained neurologically by an antagonistic relationship between the DMN and FPN which include IFG(Song, Long, Wang, Zhang, & Lee, 2022 ). Although fNIRS could not explore deep brain regions, the results of the study are somewhat consistent with previous studies that the neural mechanism of rumination is related to the prefrontal. Herein, the prefrontal-based dysfunctional connectivity might explain the elevated ruminative tendency under stress context. 5. Strengths and Limitations There are several important distinctions between previous studies and our own. First, fNIRS tool is capable of long-period data acquisition because of less physical burden and body confinement on participants. Therefore, it is suitable for the measurement of TSST. Second, our employment of TSST effectively induced social stress, reflecting real-world scenarios more closely and offering insights into how individuals respond to stressors in genuine social settings. Additionally, applying both static and dynamic functional connectivity analyses offers a comprehensive view of dynamic network fluctuations and synergistic interactions between brain regions, thus providing a richer understanding of brain connectivity dynamics under stress. However, it should be noted that there is a limitation to this study. Although fNIRS is a convenient brain imaging technique, it primarily measures changes in blood oxygen levels on the surface of the cerebral cortex. Compared to other brain imaging techniques, such as fMRI, fNIRS cannot provide detailed information on deep brain regions. This restricted measurement depth may limit the ability to capture neural responses in deeper brain regions that are also involved in the complex processes of rumination and stress. In the future, alternative equipment could be employed to further explore the neural patterns of high ruminators under stressful conditions. 6. Conclusions In this study we aim to investigate the causes of high trait rumination by exploring the different behavioral and neural patterns between high trait ruminators and low trait ruminators in a social stress context. We found individuals with HTR showed aberrant prefrontal functional connectivity pattern in high ruminators under stress context, which might be the maladaptive reaction to stress. Our study contributes to the neural mechanisms underlying rumination and its association with stress. Further research in this area may elucidate the specific cognitive control mechanisms that are impaired in high ruminators and their impact on emotional regulation and psychological well-being. Declarations Acknowledgement This study was supported by National Key R & D Program of China (SIT2030-Major Projects 2022ZD0214300), Nature Science Foundation of China (ref: 32271139, 31900806), Guangdong Basic and Applied Basic Research Foundation (ref: 2023A1515011331), Science and Technology Program of Guangzhou, China (ref: 2023A04J1964), Guangzhou Philosophy and Social Science Project for 2022 Yangcheng Young Scholar during the fourteenth Five-year Plan Period (ref: 2022GZQN30). The funding organization played no further role in study design, data collection, analysis and interpretation, and paper writing. Declaration of Competing Interest 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. Declaration and verification This work described has not been published previously (except in the form of an abstract and a published lecture), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright-holder. Data availability The datasets generated and analysed during the current study are not publicly available due to the principles of confidentiality concerning human participant but are available from the corresponding author on reasonable request. Author contributions Peng Lanxin: Conceptualization, Formal analysis; Writing- Original draft preparation; Visualization Long Jixin: Data curation, Methodology, Writing - reviewing & editing. Qian Li: Writing - reviewing & editing. LijingNiu: Writing - reviewing & editing. Haowei Dai: Writing - reviewing & editing. Jiayuan Zhang: Writing - reviewing & editing. Keyin Chen: Writing - reviewing & editing. Huang Meiyan ： Resources; Software; Supervision; Validation. Zhang Ruibin : Conceptualization; methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; References Al-Shargie, F., Katmah, R., Tariq, U., Babiloni, F., Al-Mughairbi, F., & Al-Nashash, H. (2022). Stress management using fNIRS and binaural beats stimulation. Biomed Opt Express, 13 (6), 3552-3575. doi:10.1364/boe.455097 Allen, A. P., Kennedy, P. J., Cryan, J. F., Dinan, T. G., & Clarke, G. (2014). Biological and psychological markers of stress in humans: focus on the Trier Social Stress Test. Neurosci Biobehav Rev, 38 , 94-124. doi:10.1016/j.neubiorev.2013.11.005 Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014). Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex, 24 (3), 663-676. doi:10.1093/cercor/bhs352 Arnsten, A. F. (2015). Stress weakens prefrontal networks: molecular insults to higher cognition. Nat Neurosci, 18 (10), 1376-1385. doi:10.1038/nn.4087 Brosschot, J. F., Gerin, W., & Thayer, J. F. (2006). The perseverative cognition hypothesis: a review of worry, prolonged stress-related physiological activation, and health. J Psychosom Res, 60 (2), 113-124. doi:10.1016/j.jpsychores.2005.06.074 Burkhouse, K. L., Jacobs, R. H., Peters, A. T., Ajilore, O., Watkins, E. R., & Langenecker, S. A. (2017). Neural correlates of rumination in adolescents with remitted major depressive disorder and healthy controls. Cogn Affect Behav Neurosci, 17 (2), 394-405. doi:10.3758/s13415-016-0486-4 Cooney, R. E., Joormann, J., Eugène, F., Dennis, E. L., & Gotlib, I. H. (2010). Neural correlates of rumination in depression. Cogn Affect Behav Neurosci, 10 (4), 470-478. doi:10.3758/cabn.10.4.470 Dagher, A., Tannenbaum, B., Hayashi, T., Pruessner, J. C., & McBride, D. (2009). An acute psychosocial stress enhances the neural response to smoking cues. Brain Res, 1293 , 40-48. doi:10.1016/j.brainres.2009.07.048 Duque, A., Sanchez, A., & Vazquez, C. (2014). Gaze-fixation and pupil dilation in the processing of emotional faces: the role of rumination. Cogn Emot, 28 (8), 1347-1366. doi:10.1080/02699931.2014.881327 Ehring, T. (2021). Thinking too much: rumination and psychopathology. World Psychiatry, 20 (3), 441-442. doi:10.1002/wps.20910 Friston, K. J. (1994). Functional and effective connectivity in neuroimaging: A synthesis. Human Brain Mapping, 2 (1-2), 56-78. doi:10.1002/hbm.460020107 Hampshire, A., Chamberlain, S. R., Monti, M. M., Duncan, J., & Owen, A. M. (2010). The role of the right inferior frontal gyrus: inhibition and attentional control. Neuroimage, 50 (3), 1313-1319. doi:10.1016/j.neuroimage.2009.12.109 Han, X., & Yang, H.-f. (2009). Chinese Version of Nolen-Hoeksema Ruminative Responses Scale (RRS) used in 912 college students: Reliability and validity. Chinese Journal of Clinical Psychology, 17 (5), 550-551. Hasegawa, A., Matsumoto, N., Yamashita, Y., Tanaka, K., Kawaguchi, J., & Yamamoto, T. (2022). Response inhibition deficits are positively associated with trait rumination, but attentional inhibition deficits are not: aggressive behaviors and interpersonal stressors as mediators. Psychol Res, 86 (3), 858-870. doi:10.1007/s00426-021-01537-y Hasegawa, A., Somatori, K., Nishimura, H., Hattori, Y., & Kunisato, Y. (2021). Depression, Rumination, and Impulsive Action: A Latent Variable Approach to Behavioral Impulsivity. J Psychol, 155 (8), 717-737. doi:10.1080/00223980.2021.1956871 Hruska, L. C., Zelic, K. J., Dickson, K. S., & Ciesla, J. A. (2017). Adolescents' co-rumination and stress predict affective changes in a daily-diary paradigm. Int J Psychol, 52 (5), 372-380. doi:10.1002/ijop.12227 Hughes, M. E., Johnston, P. J., Fulham, W. R., Budd, T. W., & Michie, P. T. (2013). Stop-signal task difficulty and the right inferior frontal gyrus. Behav Brain Res, 256 , 205-213. doi:10.1016/j.bbr.2013.08.026 Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., . . . Chang, C. (2013). Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage, 80 , 360-378. doi:10.1016/j.neuroimage.2013.05.079 Int-Veen, I., Fallgatter, A. J., Ehlis, A. C., & Rosenbaum, D. (2023). Prefrontal hypoactivation induced via social stress is more strongly associated with state rumination than depressive symptomatology. Sci Rep, 13 (1), 15147. doi:10.1038/s41598-023-41403-y Japee, S., Holiday, K., Satyshur, M. D., Mukai, I., & Ungerleider, L. G. (2015). A role of right middle frontal gyrus in reorienting of attention: a case study. Front Syst Neurosci, 9 , 23. doi:10.3389/fnsys.2015.00023 Kovács, L. N., Kocsel, N., Tóth, Z., Smahajcsik-Szabó, T., Karsai, S., & Kökönyei, G. (2023). Associations between daily affective experiences, trait and daily rumination on negative and positive affect: a diary study. J Pers . doi:10.1111/jopy.12897 Kumar, S., Hultman, R., Hughes, D., Michel, N., Katz, B. M., & Dzirasa, K. (2014). Prefrontal cortex reactivity underlies trait vulnerability to chronic social defeat stress. Nat Commun, 5 , 4537. doi:10.1038/ncomms5537 Laicher, H., Int-Veen, I., Torka, F., Kroczek, A., Bihlmaier, I., Storchak, H., . . . Rosenbaum, D. (2022). Trait rumination and social anxiety separately influence stress-induced rumination and hemodynamic responses. Sci Rep, 12 (1), 5512. doi:10.1038/s41598-022-08579-1 Li, Z., Liu, H., Liao, X., Xu, J., Liu, W., Tian, F., . . . Niu, H. (2015). Dynamic functional connectivity revealed by resting-state functional near-infrared spectroscopy. Biomed Opt Express, 6 (7), 2337-2352. doi:10.1364/boe.6.002337 Luo, Y., Li, J., Zhang, Y., & Pan, W. (2023). The scalp prefrontal-limbic functional connectivity moderates stress-related rumination effects on stress recovery. Psychophysiology , e14462. doi:10.1111/psyp.14462 McKlveen, J. M., Morano, R. L., Fitzgerald, M., Zoubovsky, S., Cassella, S. N., Scheimann, J. R., . . . Herman, J. P. (2016). Chronic Stress Increases Prefrontal Inhibition: A Mechanism for Stress-Induced Prefrontal Dysfunction. Biol Psychiatry, 80 (10), 754-764. doi:10.1016/j.biopsych.2016.03.2101 Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annu Rev Neurosci, 24 , 167-202. doi:10.1146/annurev.neuro.24.1.167 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 (3), 443-454. doi:10.1037/emo0000448 Nitschke, J. P., Giorgio, L. M., Zaborowska, O., & Sheldon, S. (2020). Acute psychosocial stress during retrieval impairs pattern separation processes on an episodic memory task. Stress, 23 (4), 437-443. doi:10.1080/10253890.2020.1724946 Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008). Rethinking Rumination. Perspect Psychol Sci, 3 (5), 400-424. doi:10.1111/j.1745-6924.2008.00088.x Ossewaarde, L., Qin, S., Van Marle, H. J., van Wingen, G. A., Fernández, G., & Hermans, E. J. (2011). Stress-induced reduction in reward-related prefrontal cortex function. Neuroimage, 55 (1), 345-352. doi:10.1016/j.neuroimage.2010.11.068 Porcelli, A. J., & Delgado, M. R. (2017). Stress and Decision Making: Effects on Valuation, Learning, and Risk-taking. Curr Opin Behav Sci, 14 , 33-39. doi:10.1016/j.cobeha.2016.11.015 Rosenbaum, D., Hilsendegen, P., Thomas, M., Haeussinger, F. B., Metzger, F. G., Nuerk, H. C., . . . Ehlis, A. C. (2018). Cortical hemodynamic changes during the Trier Social Stress Test: An fNIRS study. Neuroimage, 171 , 107-115. doi:10.1016/j.neuroimage.2017.12.061 Rosenbaum, D., Thomas, M., Hilsendegen, P., Metzger, F. G., Haeussinger, F. B., Nuerk, H. C., . . . Ehlis, A. C. (2018). Stress-related dysfunction of the right inferior frontal cortex in high ruminators: An fNIRS study. Neuroimage Clin, 18 , 510-517. doi:10.1016/j.nicl.2018.02.022 Shields, G. S., Sazma, M. A., & Yonelinas, A. P. (2016). The effects of acute stress on core executive functions: A meta-analysis and comparison with cortisol. Neurosci Biobehav Rev, 68 , 651-668. doi:10.1016/j.neubiorev.2016.06.038 Shull, A., Mayer, S. E., McGinnis, E., Geiss, E., Vargas, I., & Lopez-Duran, N. L. (2016). Trait and state rumination interact to prolong cortisol activation to psychosocial stress in females. Psychoneuroendocrinology, 74 , 324-332. doi:10.1016/j.psyneuen.2016.09.004 Skoluda, N., Strahler, J., Schlotz, W., Niederberger, L., Marques, S., Fischer, S., . . . Nater, U. M. (2015). Intra-individual psychological and physiological responses to acute laboratory stressors of different intensity. Psychoneuroendocrinology, 51 , 227-236. doi:10.1016/j.psyneuen.2014.10.002 Smith, J. M., & Alloy, L. B. (2009). A roadmap to rumination: a review of the definition, assessment, and conceptualization of this multifaceted construct. Clin Psychol Rev, 29 (2), 116-128. doi:10.1016/j.cpr.2008.10.003 Song, X., Long, J., Wang, C., Zhang, R., & Lee, T. M. C. (2022). The inter-relationships of the neural basis of rumination and inhibitory control: neuroimaging-based meta-analyses. Psychoradiology, 2 (1), 11-22. doi:10.1093/psyrad/kkac002 %J Psychoradiology Sudimac, S., Sale, V., & Kühn, S. (2022). How nature nurtures: Amygdala activity decreases as the result of a one-hour walk in nature. Mol Psychiatry, 27 (11), 4446-4452. doi:10.1038/s41380-022-01720-6 Suzuki, Y., & Tanaka, S. C. (2021). Functions of the ventromedial prefrontal cortex in emotion regulation under stress. Sci Rep, 11 (1), 18225. doi:10.1038/s41598-021-97751-0 Wagels, L., Bergs, R., Clemens, B., Bauchmüller, M., Gur, R. C., Schneider, F., . . . Kohn, N. (2017). Contextual exclusion processing: an fMRI study of rejection in a performance-related context. Brain Imaging Behav, 11 (3), 874-886. doi:10.1007/s11682-016-9561-2 Wang, C., Song, X., Lee, T. M. C., & Zhang, R. (2022). Psychometric Properties of the Chinese Version of the Brief State Rumination Inventory. Front Public Health, 10 , 824744. doi:10.3389/fpubh.2022.824744 Worley, N. B., Hill, M. N., & Christianson, J. P. (2018). Prefrontal endocannabinoids, stress controllability and resilience: A hypothesis. Prog Neuropsychopharmacol Biol Psychiatry, 85 , 180-188. doi:10.1016/j.pnpbp.2017.04.004 Yang, Y., Cao, S., Shields, G. S., Teng, Z., & Liu, Y. (2017). The relationships between rumination and core executive functions: A meta-analysis. Depress Anxiety, 34 (1), 37-50. doi:10.1002/da.22539 Zhang, J., Wang, Y., Leong, C., Mao, Y., & Yuan, Z. (2023). Bridging Stories and Science: An fNIRS-based hyperscanning investigation into child learning in STEM. Neuroimage, 285 , 120486. doi:10.1016/j.neuroimage.2023.120486 Additional Declarations No competing interests reported. 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(\u003cstrong\u003ea)\u003c/strong\u003e Design and measurements of the experiment. (\u003cstrong\u003eb)\u003c/strong\u003e The fNIRS signal of 26 channels during TSST or control task. (\u003cstrong\u003ec)\u003c/strong\u003e The static functional connectivity matrix. (\u003cstrong\u003ed) \u003c/strong\u003eThe dynamic functional connectivity matrix. (\u003cstrong\u003ee)\u003c/strong\u003e The variability of DFC matrix. BSRI, Brief State Rumination Inventory; VAS, Visual Analogue Scale; TNT, Think No Think; DFC, Dynamic Functional Connectivity; SFC, Static Functional Connectivity.\u003c/p\u003e","description":"","filename":"fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3842177/v1/6d2fafd3ae29241178cd56e0.png"},{"id":49894546,"identity":"efa1a015-ea8d-45bd-8356-14a27931920f","added_by":"auto","created_at":"2024-01-19 21:32:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":881490,"visible":true,"origin":"","legend":"\u003cp\u003eResponses in subjective stress ratings. The change index of \u003cstrong\u003e(a) \u003c/strong\u003estressful, \u003cstrong\u003e(b) \u003c/strong\u003edifficult, \u003cstrong\u003e(c) \u003c/strong\u003eunpleasant, \u003cstrong\u003e(d) \u003c/strong\u003eannoyed and \u003cstrong\u003e(e) \u003c/strong\u003efearful feeling of HTR and LTR during control and stress tasks. ns: no significant result. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3842177/v1/3a46de869e00f9ebe50ff3b5.png"},{"id":49895315,"identity":"f1997c95-a243-4763-8679-17aaa5a41958","added_by":"auto","created_at":"2024-01-19 21:40:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":987724,"visible":true,"origin":"","legend":"\u003cp\u003eResponses in salivary cortisol and state rumination. Salivary cortisol of \u003cstrong\u003e(a) \u003c/strong\u003eLTR, \u003cstrong\u003e(b) \u003c/strong\u003eHTR, and \u003cstrong\u003e(c) \u003c/strong\u003estate rumination during different time points. HTR, high trait rumination group; LTR, low trait rumination group. Asterisks mean the result of dependent t-test. * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3842177/v1/ae2b3d4cc1e70c3229bd0124.png"},{"id":49895317,"identity":"53a59dfa-f8bb-4346-8b99-d88af8a85d4a","added_by":"auto","created_at":"2024-01-19 21:40:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3710976,"visible":true,"origin":"","legend":"\u003cp\u003eStatic functional connectivity patterns in high and low trait ruminators under stress and control conditions. Group-averaged static correlation matrix maps of high trait rumination group under \u003cstrong\u003e(a) \u003c/strong\u003estress and \u003cstrong\u003e(b) \u003c/strong\u003econtrol conditions. Similarly, maps of low trait rumination group are shown under \u003cstrong\u003e(c) \u003c/strong\u003estress and \u003cstrong\u003e(d) \u003c/strong\u003econtrol conditions. \u003cstrong\u003e(e)\u003c/strong\u003e SFC of right MFG and IFG in HTR and LTR in control task and TSST. \u003cstrong\u003e(f)\u003c/strong\u003e The position of right MFG and IFG\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3842177/v1/f30bd5389fba9cdd20d689ae.png"},{"id":49895316,"identity":"39862134-f421-4b77-8a0c-16c4e725f802","added_by":"auto","created_at":"2024-01-19 21:40:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1557266,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic functional connectivity analysis results. \u003cstrong\u003e(a)\u003c/strong\u003e The ROI pairs with significant condition results. (\u003cstrong\u003eb) \u003c/strong\u003eThe ROI pairs with significant interaction results. The result retains four decimals. (\u003cstrong\u003ec)\u003c/strong\u003eThe position of left MFG and IFG. (Green: left MFG, yellow: left IFG) (\u003cstrong\u003ed)\u003c/strong\u003e The correlation between variability of DFC and BSRI left MFG and left IFG\u003cstrong\u003e. \u003c/strong\u003e*\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3842177/v1/6c7fa0ad6e9d6ac42dbf31c8.png"},{"id":61928930,"identity":"29fe5497-0a65-4ec8-ab36-6af66f40778b","added_by":"auto","created_at":"2024-08-07 07:46:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14056826,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3842177/v1/5df2113a-f1ed-4e48-93f9-f4cf43e8f38e.pdf"},{"id":49894548,"identity":"793e7d02-9a09-4d6b-9770-3c637ec55f2f","added_by":"auto","created_at":"2024-01-19 21:32:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":522861,"visible":true,"origin":"","legend":"","description":"","filename":"supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-3842177/v1/ca0f4258b2eefe723ace6e62.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Acute stress-related aberrant prefrontal based functional connectivity in high ruminators: An fNIRS study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRumination is a mode of responding to distress involving repetitively and passively focusing on symptoms of distress and on the possible causes and consequences of these symptoms (Nolen-Hoeksema, Wisco, \u0026amp; Lyubomirsky, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Higher ruminative style is associated with sustained processing of negative information (Duque, Sanchez, \u0026amp; Vazquez, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which can reinforce negative feelings. Previous studies have underscored the influence of rumination transcends specific mood disorders, with implications for a spectrum of psychopathologies including anxiety, binge eating, binge drinking and self-harm (Nolen-Hoeksema et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Investigating rumination may provide valuable insights into the underlying factors of psychological disorders, leading to improved strategies for enhancing mental well-being.\u003c/p\u003e \u003cp\u003eMany studies have illuminated the intricate relationship between stress and rumination. On one hand, stress can trigger or intensify rumination, as individuals often engage in rumination in response to stressful events (Smith \u0026amp; Alloy, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This correlation suggests that rumination may be a cognitive response mechanism induced by acute stress. On the other hand, rumination itself can exacerbate the impact of life stressors, potentially creating a feedback loop (Hruska, Zelic, Dickson, \u0026amp; Ciesla, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, individuals with high levels of rumination may face challenges in generating adaptive responses to stressors, which in turn may further intensify their rumination(Nasso, Vanderhasselt, Demeyer, \u0026amp; De Raedt, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, perseverative cognition, encompassing worry and rumination, has been shown to prolong stress-related affective and physiological responses, both before and after exposure to stressors (Brosschot, Gerin, \u0026amp; Thayer, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Given these perspectives on the dynamic interplay between stress and rumination, an understanding of their neurological underpinnings becomes crucial.\u003c/p\u003e \u003cp\u003eIn the realm of neuroscience, previous research has demonstrated the significant role of the prefrontal cortex in processing stress (Arnsten, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; McKlveen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ossewaarde et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Worley, Hill, \u0026amp; Christianson, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The prefrontal cortex assumes a crucial role in emotional regulation and stress coping, being influenced by different stress levels and individual responses(Kumar et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Suzuki \u0026amp; Tanaka, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A recent study also highlights the effect of functional connectivity (FC) between the ventrolateral prefrontal cortex (VLPFC) and dorsolateral prefrontal cortex (DLPFC) under stress (Al-Shargie et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Importantly, recent study showed that connectivity between the prefrontal cortex and the limbic system plays a significant role in regulating how state rumination impacts stress recovery(Luo, Li, Zhang, \u0026amp; Pan, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Herein, targeting prefrontal cortex may contribute to better understanding of the relationship between rumination and stress.\u003c/p\u003e \u003cp\u003eIndeed, previous studies have linked prefrontal hypoactivation during stress with heightened state rumination (Int-Veen, Fallgatter, Ehlis, \u0026amp; Rosenbaum, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rosenbaum, Thomas, et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example,Rosenbaum, Thomas, et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) identified reduced cortical activity in the right inferior frontal gyrus (IFG) among individuals with high rumination levels during stress. However, there exist gaps in our understanding that need to be addressed. Firstly, previous common stress induction methods, such as trial-by-trial tasks(Dagher, Tannenbaum, Hayashi, Pruessner, \u0026amp; McBride, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sudimac, Sale, \u0026amp; K\u0026uuml;hn, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wagels et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), might not accurately replicate the intricate and dynamic nature of real-life stress experiences. Unlike trial-by-trial tasks, the Trier Social Stress Test (TSST) effectively induces acute stress in a laboratory setting while maintaining real-life relevance(Skoluda et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The application of TSST could enhance our comprehension of stress responses in real-life scenarios. Secondly, the analysis in previous studies was limited to examining brain activity in specific brain regions but understanding the relationship between rumination and stress requires consideration of the coordination among multiple brain regions. To address this limitation, we intend to employ FC analysis. FC, defined as temporal coincidence of spatially distant neurophysiological events (Friston, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), enables us to uncover synergies between brain regions. Furthermore, FC is especially well-suited for analyzing prolonged tasks. Unlike tasks involving blocks that last for less than 30 seconds, the TSST lacks this structural characteristic, rendering it unsuitable for typical task-state data analysis methods. Thus, FC is better suited for assessing the neural underpinnings of TSST compared to activation analysis. Despite extensive research on the brain mechanism under stress, there is a notable gap in understanding the functional connectivity associated with rumination under stress.\u003c/p\u003e \u003cp\u003efNIRS is an optical imaging method that has been proven to be compatible with the standard procedure of the TSST (Rosenbaum, Thomas, et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To explore differences in inter-regional neural connectivity, we employed both static and dynamic FC analyses. Compared to static FC, dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior (Hutchison et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems (E. A. Allen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Compared with functional Magnetic Resonance Imaging (fMRI), fNIRS is less vulnerable to motion artefacts(Zhang, Wang, Leong, Mao, \u0026amp; Yuan, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the utilization of fNIRS-based functional brain connectivity enables us to investigate the interconnected activities among various brain regions involved in rumination under stress.\u003c/p\u003e \u003cp\u003eIn this study we used fNIRS to investigate the behavioral and neural response patterns of high ruminators under acute stress. At behavioral level, we hypothesized that high trait ruminators will show higher stress level and state rumination compared to those with low trait ruminators during stress context. At the brain level, we hypothesize dysfunctional prefrontal cortex functional connectivity in high trait ruminators, potentially impacting their stress response. Additionally, we anticipated that functional connectivity of individual would display distinct patterns during stressful contexts compared to non-stressful conditions.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eParticipants from the Southern Medical University completed the Chinese Version of Ruminative Response Scale (RRS)(Han \u0026amp; Yang, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Screening exclusion criteria included acute physical disease, neurological disease, substance abuse, chronic or acute disease affecting brain function, such as diabetes or kidney failure, arrhythmias, and other heart conditions. Out of the 296 subjects who completed the questionnaire, the top 30% scoring above 50 were identified as the high trait rumination group (HTR), totaling 30 subjects. Subsequently, 25 subjects were selected as the low trait rumination group (LTR) based on scores from the bottom 30% scoring below 30.\u003c/p\u003e \u003cp\u003eEach subject underwent both the TSST and control condition experiment, during which fNIRS data was collected. A crossover design was employed to mitigate individual differences. Half of the subjects from both the HTR and LTR groups were randomly assigned to undergo the stress condition experiment initially, followed by the control condition experiment after 1\u0026ndash;2 weeks. Conversely, the remaining participants started with the control condition experiment and subsequently underwent the stress condition experiment after the same interval. Five participants were unable to participate in the second experimental session, resulting in the retention of 28 HTR and 22 LTR (Detailed demographic information could refer to Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in the Supplementary). This study was reviewed and approved by the Ethics Committee of the Southern Medical University. The study adhered to all relevant guidelines and regulations governing research involving human subjects and conducted in strict accordance with the principles outlined in the Declaration of Helsinki. All participants were given written informed consent after acknowledging the experimental procedure and received appropriate monetary compensation after the experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Procedure\u003c/h2\u003e \u003cp\u003eOn the day of the TSST task, subjects were led to the fNIRS laboratory and seated at a table where the fNIRS machine was behind them. The TSST protocol consisted of a 5-minute presentation task preparation period, a 5-minute presentation task, and a 5-minute math subtraction task. Prior to the test, the experimenter instructed the participants to prepare an interview: Describe what they would like to do in the future and why they would be qualified for it. Participants were informed that their presentations would be recorded on video. Following the preparation period, two judges, maintaining serious and neutral expressions, entered the laboratory for the formal presentation task. After the speech task, participants were directed to engage in a 5-minute mathematical calculation task. They were asked to take turns subtracting 13 from 1022, verbally stating the calculation formula and answering as quickly and accurately as possible. In case of errors or prolonged response times during the mathematical calculation task, participants were required to start over. The whole pipeline of the experiment could be referred to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea. Prior to the experiment, all judges received extensive training and were explicitly instructed not to exhibit any positive feedback, such as smiling or nodding, to the participants during the experiment. This measure was implemented to ensure that participants perceived social threat factors to the highest degree, thereby inducing social psychological stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the control task, prior to the preparation phase, participants were instructed to introduce a favorite book or movie to the experimenter within a 5-minute timeframe. During the introduction, the experimenter provided friendly responses to the participants' social signals. After the allocated 5 minutes, participants were asked to recite the multiplication table, which they had already memorized skillfully during their primary school education.\u003c/p\u003e \u003cp\u003eFor the stability of cortisol, participants were advised to abstain from alcohol consumption the day before the measurement, maintain their usual sleep duration, and avoid engaging in physical activities on the measurement day. Also, subjects were told not to eat 1 hour before the measurement started.\u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert Fig.\u0026nbsp;1here.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3 Subjective scoring and cortisol sampling\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo evaluate the effects of acute stress induction, five subjective stress indicators (stressful, unpleasant, difficult, annoyed, and fearful), state rumination and salivary cortisol samples were collected from subjects at various time points during the experiment. We use visual analogue scale (VAS) to assess subjective stress scores at baseline (T0) and after the end of the TSST (or control task) (T2). Using the scores at T2 minus the scores at T0, we calculated the \u0026ldquo;change index\u0026rdquo;, which reflects the subjective changes following the experiment. The change index represents the extent of change in subjective pressure experienced by the participants following the experimental conditions. Salivary cortisol samples were obtained at various intervals: before the start of the experiment (T0), after the end of the learning phase (T1), after the end of the TSST (T2), after the end of the Think/No Think phase (T3) and 15 minutes after the end of the recall phase (T4). Notably, the investigation of the Think/No Think task is outlined in a separate research article within this study, which does not relate to the current research objectives and will not be revisited in subsequent sections. We used the Brief State Rumination Inventory (BSRI) to measure the state rumination at different time points. The Chinese version of the BSRI consists of eight forward-scoring items, and the items are scored on a 100 mm VAS ranging from 0 (\u0026ldquo;strongly disagree\u0026rdquo;) to 100 (\u0026ldquo;strongly agree\u0026rdquo;). The total score is obtained by summing up all items (Wang, Song, Lee, \u0026amp; Zhang, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), assessments were conducted only at T0, T2 and T4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eTo be noticed that salivary cortisol samples were collected at five different time points during the experiment to assess the effectiveness of acute stress induction. Saliva samples were collected using salivettes (Sarstedt AG \u0026amp; Co., REF51.1534.500) and stored at \u0026minus;\u0026thinsp;78\u0026deg;C. For analysis of cortisol levels, salivettes were thawed and centrifuged for 10 min at 3000 rmp to collect saliva. Further analysis was performed with electrochemiluminescence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 fNIRS acquisition and preprocessing\u003c/h2\u003e \u003cp\u003eBrain activity was measured with the NIRScout system (NIRX, USA) with a temporal resolution of 4.46 Hz and two different wavelengths (780nm and 830nm\u003cb\u003e).\u003c/b\u003e The photoconductive array is fixed on the subject's head through an electrode cap, comprising 26 channels, consisting of 13 emitters and 13 detector probes. In this study, the probes were positioned according to the international EEG electrode standard 10\u0026ndash;20 system and placed at the reference point Fpz. The specific channel locations were determined by conducting a registration and comparison process with Table S2 in the Supplementary, which presents the Anatomical Automatic Labeling (AAL) template partitions and MNI coordinate systems.\u003c/p\u003e \u003cp\u003eThe raw fNIRS data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) underwent preprocessing using the Homer2 toolbox (homer-fnirs.org). Preprocessing steps included the following: Identifying motion artifacts by applying a standard deviation threshold in addition to the amplitude threshold; Conducting motion correction using principal component analysis; Applying bandpass filtering (0.01\u0026ndash;0.1 Hz) to attenuate high- and low-frequency noise; and enhancing the signal quality through correlation-based signal improvement. For analysis purposes, only the initial five minutes of the speech task were retained for further calculations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eBehavioral data\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBehavioral data was performed using IBM SPSS Statistics version 26. To measure the condition effect and group effect after TSST and control task, a 2\u0026times;2 mixed-design analysis of variance (ANOVA) was employed to examine the change index of subjective stress indicators, which is calculated as the difference between the subjective pressure rating after TSST (or control task) and the subjective pressure rating before TSST (or control task). At the same time, to investigate the difference between state rumination, a 2\u0026times;2\u0026times;3 mixed-design ANOVA was used. The experimental conditions (the TSST and control task) and timepoint, were treated as within-subject variables, while the subject groups (HTR and LTR) were considered as the between-subject variables. Since T2 timepoint (after TSST or control task) revealed the immediate effect of TSST, a 2\u0026times;2 ANOVA was conducted for the T2 on the BSRI. Subsequently, independent samples t-tests were performed to compare the scores of HTR and LTR separately under different experimental conditions.\u003c/p\u003e \u003cp\u003eThe salivary cortisol levels were analyzed using 2\u0026times;2\u0026times;5(five-time points) repeated ANOVA, considering the time points (5 levels) and experimental conditions (2 levels: stress and control) as within-subject variables. Then, independent sample \u003cem\u003et\u003c/em\u003e test was conducted on salivary cortisol levels at 5 time points under two experimental conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional connectivity\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStatic functional connectivity (SFC)\u003c/b\u003e The SFCs were analyzed in MATLAB version2018. To visualize the FC in HTR and LTR, we generated the correlation matrix maps containing all possible channel pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Specifically, we calculate the Pearson correlation data of Δ[HbO] between each channel and false discovery rate (FDR) was used to correct the retention of \u003cem\u003ep\u003c/em\u003e values above 0.05. Then, different channels were divided according to brain regions, and the correlated values of each subject's brain regions were averaged (such as Channel 1 in the left MFG). Each subject was given a value for each brain region. As a conventional method, the strength of SFCs here was measured by correlation values. After that, repeated ANOVA was directly used for the SFCs of LTR and HTR in two conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDynamic functional connectivity (DFC)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe also calculated the DFC of the signals with a sliding sliding-window correlation (SWC) approach (Hutchison et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The SWC is the most commonly used strategy for examining FC dynamics in previous neuroimaging studies using the fNIRS (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In our SWC analysis, based on the sampling rate of 4.464, about 20-s time window was selected and then shifted in an increment of 3 time points of times series along the entire time course. The FC within each time window (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) was quantitatively calculated for each selected pair of brain regions using the Pearson correlation strategy. To compare with the variability of DFC, we first quantitatively estimated the variance in the DFC fluctuations for each pair of channels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Then, we calculated the mean variance at ROI level. Finally, to compare the variability of the DFC, we used 2\u0026times;2 mixed design ANOVA between different groups and conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRelationships between SFC/DFC and Behavior\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn order to explore and understand how the FC patterns between specific brain regions correlate with behavioral aspects, particularly subjective stress and rumination, we also investigated the relationship between SFC/DFC and behavior. After achieving the functional connectivity between brain regions showing significant effects, we calculated the correlation SFC/DFC and subjective stress, BSRI was analyzed in these regions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Successful stress induction\u003c/h2\u003e \u003cp\u003eAs indicated by repeated measurement ANOVA (group\u0026times;condition), in both HTR and LTR, the subjective stress change index showed an increase from the control task to the TSST, including stressful (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;64.057, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.572), unpleasant (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;99.310, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.674), difficult (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;69.248, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.591), annoyed (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;63.341, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.569) and fearful (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24.404, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.337). However, no significant differences were found between HTR and LTR (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.413, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.522). No significant interaction effects between condition and group were observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003ehere.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFor cortisol changes, results from repeated ANOVA showed that the main effect of the experimental conditions (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;420.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.897) and the main effect of the time points (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;71.232, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.597) were significant on the change of salivary cortisol levels. It was shown that salivary cortisol increased significantly after TSST but decreased gradually under control task. While the main effect of groups (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.530) and the interaction effect were not significant, dependent sample t-tests were then performed on the level of salivary cortisol under two experimental conditions at five time points in high and low trait ruminators separately. The results of the t-test of condition effect indicated that there were no significant differences in baseline salivary cortisol levels (T0) before receiving the TSST and those before the control task. However, after undergoing the TSST (or control task) (T2) and 15 minutes after completing the TSST (or control task) (T3), salivary cortisol levels of the subjects were significantly higher in TSST compared to those under the control task. The LTR group exhibited a similar pattern of cortisol changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 State rumination under stress context\u003c/h2\u003e \u003cp\u003eAs for state rumination, results of three-way ANOVA (condition \u0026times; group \u0026times; timepoint) indicated a generally higher state rumination level for HTR \u0026ndash; reflected by a main effect of group (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.609, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.167), which means state rumination of HTR was higher than LTR. The two-way ANOVA (condition\u0026times;group) of T2 indicated group (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.798, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.170) and condition main effect (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.744, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.169) of state rumination. These findings implied an increase in state rumination following the TSST in comparison to the control task. The results of the t-test comparing HTR and LTR revealed higher state rumination in HTR at all time points on the TSST day compared to LTR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). At the same time, we found a group by condition interaction for state rumination (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.92), reflecting higher overall state rumination and higher increases in state rumination after TSST for HTR.\u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003ehere.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Static functional connectivity\u003c/h2\u003e \u003cp\u003eFollowing the ANOVA of SFC between or within eight distinct brain regions, several brain regions showed significant condition or group effect (Group average SFC matrices were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-d). Therein, the SFC between the right IFG and right middle frontal gyrus (MFG) yielded particular results. Specifically, this SFC exhibited significant effects of both the condition (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.362, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.133) and group (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.786, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.259) evidence of interaction effect (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.175, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.080) between the two factors. During the TSST, the SFC between the right IFG and right MFG was observed to be significantly higher than during the control task. Furthermore, in both TSST and control task, the SFC in LTR was found to be significantly higher than in HTR. Regarding the further simple effects analysis, SFC increased significantly in the LTR group during the TSST (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.100, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.174), while the increase among the HTR group was not as pronounced (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee \u0026amp; f).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cem\u003ehere.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Dynamic functional connectivity\u003c/h2\u003e \u003cp\u003eAs described in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, some of region pairs exhibited significant main effect. Region pairs with asterisks showed a significant condition effect in the variability of DFC. Specifically, the variability of the DFC in TSST was significantly higher than that in control task. At the same time, there was no significant group effect in any region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBesides, a significant interaction effect in the variability of DFC was observed in some regions as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb. Specifically, during the control task, the LTR group consistently exhibited lower DFC variability compared to the TSST across various region pairs. Additionally, within the right MFG, the variability was significantly higher in the LTR during TSST compared to the HTR. The variability was significantly higher in the LTR during TSST compared to the HTR.\u003c/p\u003e \u003cp\u003e \u003cem\u003eInsert\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cem\u003ehere.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.5 Relationships between SFC/DFC and Behavior\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eAfter correlation analysis, we only found that the correlation between variability of DFC between left MFG and left IFG (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec) and BSRI was significant (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.210, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our best knowledge, this is the first study aiming to investigate the causes of high trait rumination by exploring the different behavioral and neural patterns between high trait ruminators and low trait ruminators in a social stress context using functional connectivity. Our findings indicate successful stress induction, with higher levels of state rumination observed in high ruminators in both acute stress and non-stress contexts. Further analysis on the fNIRS data revealed special patterns in functional connectivity among high trait ruminators, particularly evident during periods of stress. Moreover, the higher variability of prefrontal based DFC positively correlated with the state rumination under stress context. Overall, the findings highlight that the prefrontal functional connectivity patterns of high ruminators in stressful situations may be crucial to understanding their behavioral patterns.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 High state rumination in high trait ruminators.\u003c/h2\u003e \u003cp\u003eIn subjective stress, although we observed effective stress arouse in TSST, we didn\u0026rsquo;t find significant group differences between HTR and LTR. This outcome is consistent with some previous results that there were no significant differences in subjective stress levels after TSST between LTR and HTR (Rosenbaum, Hilsendegen, et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although HTR and LTR showed similar subjective stress level, HTR were observed higher state rumination after stress, indicating that trait rumination will influence stress-induced rumination (Laicher et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Besides, both HTR and LTR were observed higher state rumination in TSST, compared to control task. This supports the notion that our stress induction, the TSST, is effective in eliciting stress-reactive rumination (Allen, Kennedy, Cryan, Dinan, \u0026amp; Clarke, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rosenbaum, Thomas, et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shull et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring both TSST day and the initial phase of the control task (T1, before the control task began), HTR consistently showed higher state rumination. That proved that the tendency to engage in a ruminative response style appears to be a reasonably stable trait(Ehring, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In other words, HTR might exhibit a greater inclination towards excessive analysis or emotional reactions, making it more challenging for them to overcome rumination in daily life. In line with the inference, recent study found that daily and trait-level rumination were correlated(Kov\u0026aacute;cs et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, it is common for high ruminators to frequently experience heightened levels of state rumination in their daily lives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Aberrant prefrontal functional connectivity patterns in high ruminators.\u003c/h2\u003e \u003cp\u003eAs expected, there were different SFC patterns in prefrontal between LTR and HTR under TSST. Unlike LTR, the increase of SFC between the right IFG and the right MFG of HTR is less noticeable when switching from the control task to TSST. Moreover, the variability of DFC was influenced by both group difference and task conditions, particularly in the prefrontal regions and their connectivity with other brain regions. And interaction was mainly driven by the variability of LTR under different conditions.\u003c/p\u003e \u003cp\u003eAs shown from our result, the primary functional connectivity differences between HTR and LTR are observed within the prefrontal cortex. The prefrontal cortex is a critical region for cognitive control. Specifically, the prefrontal has long been associated with inhibitory processes (Miller \u0026amp; Cohen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). For example, the right IFG is crucial for inhibition and attentional control. In stop signal task, previous studies indicated that the right IFG is recruited when important cues are detected (Hampshire, Chamberlain, Monti, Duncan, \u0026amp; Owen, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and it is more strongly activated during more difficult stopping (Hughes, Johnston, Fulham, Budd, \u0026amp; Michie, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, a previous study also indicated that the right MFG plays an important role in redirecting attention from exogenous to endogenous attention control (Japee, Holiday, Satyshur, Mukai, \u0026amp; Ungerleider, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In our study, the TSST imposes higher cognitive demands that require increased involvement of the prefrontal cortex. The heightened functional connectivity between the right MFG and IFG during TSST may result from increased prefrontal engagement.\u003c/p\u003e \u003cp\u003eMeanwhile, cognitive inhibition deficits are associated with rumination(Hasegawa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hasegawa, Somatori, Nishimura, Hattori, \u0026amp; Kunisato, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The association of trait rumination and inhibition is further supported by a meta-analysis indicating significant negative associations between rumination and inhibition (Yang, Cao, Shields, Teng, \u0026amp; Liu, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, cognitive abilities are known to be vulnerable to the detrimental effects of stress, leading to impairments in various cognitive domains, including working memory (Nitschke, Giorgio, Zaborowska, \u0026amp; Sheldon, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), decision making (Porcelli \u0026amp; Delgado, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and cognitive flexibility (Shields, Sazma, \u0026amp; Yonelinas, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Hence, the interplay between trait rumination and inhibitory control during stressful context could potentially further impair cognitive function.\u003c/p\u003e \u003cp\u003eConsidering the role of the prefrontal cortex alongside our findings, we propose that the abnormal functional connectivity observed in the prefrontal cortex of high ruminators during TSST may indicate an inhibitory deficit specifically under stress conditions. The weaker SFC observed between the right IFG and right MFG in HTR during stress context in our study could potentially contribute to impaired inhibitory control ability in individuals with high trait rumination. Alternatively, the impaired inhibitory control ability resulting from weakened SFC may contribute to increased levels of rumination. Our findings demonstrated an increase in SFC within the right prefrontal cortex during the TSST compared to the control task, suggesting that higher cognitive demands may enhance connectivity within the prefrontal cortex. It is worth noting that while the SFC in HTR also increased during the TSST, the increase of SFC was not as pronounced compared to LTR. This observation may indicate that the cognitive control of HTR is not as good as that of LTR within the context of stress.\u003c/p\u003e \u003cp\u003eNotably, the DFC between left MFG and IFG and scores of BSRI showed positive association. Previous fMRI studies have provided insights into the neural mechanisms of rumination, rumination proposed as related to functional activations in the DMN (Zhou et al., 2020) and cognitive control regions. The neural correlates include the dorsolateral prefrontal cortex (DLPFC)(Cooney, Joormann, Eug\u0026egrave;ne, Dennis, \u0026amp; Gotlib, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and medial prefrontal cortex (Burkhouse et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This association is associated with inhibitory control and can be explained neurologically by an antagonistic relationship between the DMN and FPN which include IFG(Song, Long, Wang, Zhang, \u0026amp; Lee, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although fNIRS could not explore deep brain regions, the results of the study are somewhat consistent with previous studies that the neural mechanism of rumination is related to the prefrontal. Herein, the prefrontal-based dysfunctional connectivity might explain the elevated ruminative tendency under stress context.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Strengths and Limitations","content":"\u003cp\u003eThere are several important distinctions between previous studies and our own. First, fNIRS tool is capable of long-period data acquisition because of less physical burden and body confinement on participants. Therefore, it is suitable for the measurement of TSST. Second, our employment of TSST effectively induced social stress, reflecting real-world scenarios more closely and offering insights into how individuals respond to stressors in genuine social settings. Additionally, applying both static and dynamic functional connectivity analyses offers a comprehensive view of dynamic network fluctuations and synergistic interactions between brain regions, thus providing a richer understanding of brain connectivity dynamics under stress.\u003c/p\u003e \u003cp\u003eHowever, it should be noted that there is a limitation to this study. Although fNIRS is a convenient brain imaging technique, it primarily measures changes in blood oxygen levels on the surface of the cerebral cortex. Compared to other brain imaging techniques, such as fMRI, fNIRS cannot provide detailed information on deep brain regions. This restricted measurement depth may limit the ability to capture neural responses in deeper brain regions that are also involved in the complex processes of rumination and stress. In the future, alternative equipment could be employed to further explore the neural patterns of high ruminators under stressful conditions.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eIn this study we aim to investigate the causes of high trait rumination by exploring the different behavioral and neural patterns between high trait ruminators and low trait ruminators in a social stress context. We found individuals with HTR showed aberrant prefrontal functional connectivity pattern in high ruminators under stress context, which might be the maladaptive reaction to stress. Our study contributes to the neural mechanisms underlying rumination and its association with stress. Further research in this area may elucidate the specific cognitive control mechanisms that are impaired in high ruminators and their impact on emotional regulation and psychological well-being.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Key R \u0026amp; D Program of China (SIT2030-Major Projects 2022ZD0214300), Nature Science Foundation of China (ref: 32271139, 31900806), Guangdong Basic and Applied Basic Research Foundation (ref: 2023A1515011331), Science and Technology Program of Guangzhou, China (ref: 2023A04J1964), Guangzhou Philosophy and Social Science Project for 2022 Yangcheng Young Scholar during the fourteenth Five-year Plan Period (ref: 2022GZQN30). The funding organization played no further role in study design, data collection, analysis and interpretation, and paper writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration and verification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work described has not been published previously (except in the form of an abstract and a published lecture), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright-holder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to the principles of confidentiality concerning human participant but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePeng Lanxin:\u003c/strong\u003e Conceptualization, Formal analysis; Writing- Original draft preparation; Visualization\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLong Jixin:\u003c/strong\u003e Data curation, Methodology, Writing - reviewing \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQian Li:\u0026nbsp;\u003c/strong\u003eWriting\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e- reviewing \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLijingNiu:\u0026nbsp;\u003c/strong\u003eWriting - reviewing \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHaowei Dai:\u0026nbsp;\u003c/strong\u003eWriting - reviewing \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJiayuan Zhang:\u003c/strong\u003e Writing - reviewing \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKeyin Chen:\u0026nbsp;\u003c/strong\u003eWriting - reviewing \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuang Meiyan\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eResources; Software; Supervision; Validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZhang Ruibin\u003c/strong\u003e: Conceptualization; methodology; Project administration; Resources; Software; Supervision; Validation; \u0026nbsp; Visualization;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Shargie, F., Katmah, R., Tariq, U., Babiloni, F., Al-Mughairbi, F., \u0026amp; Al-Nashash, H. (2022). Stress management using fNIRS and binaural beats stimulation. \u003cem\u003eBiomed Opt Express, 13\u003c/em\u003e(6), 3552-3575. doi:10.1364/boe.455097\u003c/li\u003e\n\u003cli\u003eAllen, A. P., Kennedy, P. J., Cryan, J. F., Dinan, T. G., \u0026amp; Clarke, G. (2014). Biological and psychological markers of stress in humans: focus on the Trier Social Stress Test. \u003cem\u003eNeurosci Biobehav Rev, 38\u003c/em\u003e, 94-124. doi:10.1016/j.neubiorev.2013.11.005\u003c/li\u003e\n\u003cli\u003eAllen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., \u0026amp; Calhoun, V. D. (2014). Tracking whole-brain connectivity dynamics in the resting state. \u003cem\u003eCereb Cortex, 24\u003c/em\u003e(3), 663-676. doi:10.1093/cercor/bhs352\u003c/li\u003e\n\u003cli\u003eArnsten, A. F. (2015). Stress weakens prefrontal networks: molecular insults to higher cognition. \u003cem\u003eNat Neurosci, 18\u003c/em\u003e(10), 1376-1385. doi:10.1038/nn.4087\u003c/li\u003e\n\u003cli\u003eBrosschot, J. F., Gerin, W., \u0026amp; Thayer, J. F. (2006). The perseverative cognition hypothesis: a review of worry, prolonged stress-related physiological activation, and health. \u003cem\u003eJ Psychosom Res, 60\u003c/em\u003e(2), 113-124. doi:10.1016/j.jpsychores.2005.06.074\u003c/li\u003e\n\u003cli\u003eBurkhouse, K. L., Jacobs, R. H., Peters, A. T., Ajilore, O., Watkins, E. R., \u0026amp; Langenecker, S. A. (2017). Neural correlates of rumination in adolescents with remitted major depressive disorder and healthy controls. \u003cem\u003eCogn Affect Behav Neurosci, 17\u003c/em\u003e(2), 394-405. doi:10.3758/s13415-016-0486-4\u003c/li\u003e\n\u003cli\u003eCooney, R. E., Joormann, J., Eug\u0026egrave;ne, F., Dennis, E. L., \u0026amp; Gotlib, I. H. (2010). Neural correlates of rumination in depression. \u003cem\u003eCogn Affect Behav Neurosci, 10\u003c/em\u003e(4), 470-478. doi:10.3758/cabn.10.4.470\u003c/li\u003e\n\u003cli\u003eDagher, A., Tannenbaum, B., Hayashi, T., Pruessner, J. C., \u0026amp; McBride, D. (2009). An acute psychosocial stress enhances the neural response to smoking cues. \u003cem\u003eBrain Res, 1293\u003c/em\u003e, 40-48. doi:10.1016/j.brainres.2009.07.048\u003c/li\u003e\n\u003cli\u003eDuque, A., Sanchez, A., \u0026amp; Vazquez, C. (2014). Gaze-fixation and pupil dilation in the processing of emotional faces: the role of rumination. \u003cem\u003eCogn Emot, 28\u003c/em\u003e(8), 1347-1366. doi:10.1080/02699931.2014.881327\u003c/li\u003e\n\u003cli\u003eEhring, T. (2021). Thinking too much: rumination and psychopathology. \u003cem\u003eWorld Psychiatry, 20\u003c/em\u003e(3), 441-442. doi:10.1002/wps.20910\u003c/li\u003e\n\u003cli\u003eFriston, K. J. (1994). Functional and effective connectivity in neuroimaging: A synthesis. \u003cem\u003eHuman Brain Mapping, 2\u003c/em\u003e(1-2), 56-78. doi:10.1002/hbm.460020107\u003c/li\u003e\n\u003cli\u003eHampshire, A., Chamberlain, S. R., Monti, M. M., Duncan, J., \u0026amp; Owen, A. M. (2010). The role of the right inferior frontal gyrus: inhibition and attentional control. \u003cem\u003eNeuroimage, 50\u003c/em\u003e(3), 1313-1319. doi:10.1016/j.neuroimage.2009.12.109\u003c/li\u003e\n\u003cli\u003eHan, X., \u0026amp; Yang, H.-f. (2009). Chinese Version of Nolen-Hoeksema Ruminative Responses Scale (RRS) used in 912 college students: Reliability and validity. \u003cem\u003eChinese Journal of Clinical Psychology, 17\u003c/em\u003e(5), 550-551.\u003c/li\u003e\n\u003cli\u003eHasegawa, A., Matsumoto, N., Yamashita, Y., Tanaka, K., Kawaguchi, J., \u0026amp; Yamamoto, T. (2022). Response inhibition deficits are positively associated with trait rumination, but attentional inhibition deficits are not: aggressive behaviors and interpersonal stressors as mediators. \u003cem\u003ePsychol Res, 86\u003c/em\u003e(3), 858-870. doi:10.1007/s00426-021-01537-y\u003c/li\u003e\n\u003cli\u003eHasegawa, A., Somatori, K., Nishimura, H., Hattori, Y., \u0026amp; Kunisato, Y. (2021). Depression, Rumination, and Impulsive Action: A Latent Variable Approach to Behavioral Impulsivity. \u003cem\u003eJ Psychol, 155\u003c/em\u003e(8), 717-737. doi:10.1080/00223980.2021.1956871\u003c/li\u003e\n\u003cli\u003eHruska, L. C., Zelic, K. J., Dickson, K. S., \u0026amp; Ciesla, J. A. (2017). Adolescents\u0026apos; co-rumination and stress predict affective changes in a daily-diary paradigm. \u003cem\u003eInt J Psychol, 52\u003c/em\u003e(5), 372-380. doi:10.1002/ijop.12227\u003c/li\u003e\n\u003cli\u003eHughes, M. E., Johnston, P. J., Fulham, W. R., Budd, T. W., \u0026amp; Michie, P. T. (2013). Stop-signal task difficulty and the right inferior frontal gyrus. \u003cem\u003eBehav Brain Res, 256\u003c/em\u003e, 205-213. doi:10.1016/j.bbr.2013.08.026\u003c/li\u003e\n\u003cli\u003eHutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., . . . Chang, C. (2013). Dynamic functional connectivity: promise, issues, and interpretations. \u003cem\u003eNeuroimage, 80\u003c/em\u003e, 360-378. doi:10.1016/j.neuroimage.2013.05.079\u003c/li\u003e\n\u003cli\u003eInt-Veen, I., Fallgatter, A. J., Ehlis, A. C., \u0026amp; Rosenbaum, D. (2023). Prefrontal hypoactivation induced via social stress is more strongly associated with state rumination than depressive symptomatology. \u003cem\u003eSci Rep, 13\u003c/em\u003e(1), 15147. doi:10.1038/s41598-023-41403-y\u003c/li\u003e\n\u003cli\u003eJapee, S., Holiday, K., Satyshur, M. D., Mukai, I., \u0026amp; Ungerleider, L. G. (2015). A role of right middle frontal gyrus in reorienting of attention: a case study. \u003cem\u003eFront Syst Neurosci, 9\u003c/em\u003e, 23. doi:10.3389/fnsys.2015.00023\u003c/li\u003e\n\u003cli\u003eKov\u0026aacute;cs, L. N., Kocsel, N., T\u0026oacute;th, Z., Smahajcsik-Szab\u0026oacute;, T., Karsai, S., \u0026amp; K\u0026ouml;k\u0026ouml;nyei, G. (2023). Associations between daily affective experiences, trait and daily rumination on negative and positive affect: a diary study. \u003cem\u003eJ Pers\u003c/em\u003e. doi:10.1111/jopy.12897\u003c/li\u003e\n\u003cli\u003eKumar, S., Hultman, R., Hughes, D., Michel, N., Katz, B. M., \u0026amp; Dzirasa, K. (2014). Prefrontal cortex reactivity underlies trait vulnerability to chronic social defeat stress. \u003cem\u003eNat Commun, 5\u003c/em\u003e, 4537. doi:10.1038/ncomms5537\u003c/li\u003e\n\u003cli\u003eLaicher, H., Int-Veen, I., Torka, F., Kroczek, A., Bihlmaier, I., Storchak, H., . . . Rosenbaum, D. (2022). Trait rumination and social anxiety separately influence stress-induced rumination and hemodynamic responses. \u003cem\u003eSci Rep, 12\u003c/em\u003e(1), 5512. doi:10.1038/s41598-022-08579-1\u003c/li\u003e\n\u003cli\u003eLi, Z., Liu, H., Liao, X., Xu, J., Liu, W., Tian, F., . . . Niu, H. (2015). Dynamic functional connectivity revealed by resting-state functional near-infrared spectroscopy. \u003cem\u003eBiomed Opt Express, 6\u003c/em\u003e(7), 2337-2352. doi:10.1364/boe.6.002337\u003c/li\u003e\n\u003cli\u003eLuo, Y., Li, J., Zhang, Y., \u0026amp; Pan, W. (2023). The scalp prefrontal-limbic functional connectivity moderates stress-related rumination effects on stress recovery. \u003cem\u003ePsychophysiology\u003c/em\u003e, e14462. doi:10.1111/psyp.14462\u003c/li\u003e\n\u003cli\u003eMcKlveen, J. M., Morano, R. L., Fitzgerald, M., Zoubovsky, S., Cassella, S. N., Scheimann, J. R., . . . Herman, J. P. (2016). Chronic Stress Increases Prefrontal Inhibition: A Mechanism for Stress-Induced Prefrontal Dysfunction. \u003cem\u003eBiol Psychiatry, 80\u003c/em\u003e(10), 754-764. doi:10.1016/j.biopsych.2016.03.2101\u003c/li\u003e\n\u003cli\u003eMiller, E. K., \u0026amp; Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. \u003cem\u003eAnnu Rev Neurosci, 24\u003c/em\u003e, 167-202. doi:10.1146/annurev.neuro.24.1.167\u003c/li\u003e\n\u003cli\u003eNasso, S., Vanderhasselt, M. A., Demeyer, I., \u0026amp; De Raedt, R. (2019). Autonomic regulation in response to stress: The influence of anticipatory emotion regulation strategies and trait rumination. \u003cem\u003eEmotion, 19\u003c/em\u003e(3), 443-454. doi:10.1037/emo0000448\u003c/li\u003e\n\u003cli\u003eNitschke, J. P., Giorgio, L. M., Zaborowska, O., \u0026amp; Sheldon, S. (2020). Acute psychosocial stress during retrieval impairs pattern separation processes on an episodic memory task. \u003cem\u003eStress, 23\u003c/em\u003e(4), 437-443. doi:10.1080/10253890.2020.1724946\u003c/li\u003e\n\u003cli\u003eNolen-Hoeksema, S., Wisco, B. E., \u0026amp; Lyubomirsky, S. (2008). Rethinking Rumination. \u003cem\u003ePerspect Psychol Sci, 3\u003c/em\u003e(5), 400-424. doi:10.1111/j.1745-6924.2008.00088.x\u003c/li\u003e\n\u003cli\u003eOssewaarde, L., Qin, S., Van Marle, H. J., van Wingen, G. A., Fern\u0026aacute;ndez, G., \u0026amp; Hermans, E. J. (2011). Stress-induced reduction in reward-related prefrontal cortex function. \u003cem\u003eNeuroimage, 55\u003c/em\u003e(1), 345-352. doi:10.1016/j.neuroimage.2010.11.068\u003c/li\u003e\n\u003cli\u003ePorcelli, A. J., \u0026amp; Delgado, M. R. (2017). Stress and Decision Making: Effects on Valuation, Learning, and Risk-taking. \u003cem\u003eCurr Opin Behav Sci, 14\u003c/em\u003e, 33-39. doi:10.1016/j.cobeha.2016.11.015\u003c/li\u003e\n\u003cli\u003eRosenbaum, D., Hilsendegen, P., Thomas, M., Haeussinger, F. B., Metzger, F. G., Nuerk, H. C., . . . Ehlis, A. C. (2018). Cortical hemodynamic changes during the Trier Social Stress Test: An fNIRS study. \u003cem\u003eNeuroimage, 171\u003c/em\u003e, 107-115. doi:10.1016/j.neuroimage.2017.12.061\u003c/li\u003e\n\u003cli\u003eRosenbaum, D., Thomas, M., Hilsendegen, P., Metzger, F. G., Haeussinger, F. B., Nuerk, H. C., . . . Ehlis, A. C. (2018). Stress-related dysfunction of the right inferior frontal cortex in high ruminators: An fNIRS study. \u003cem\u003eNeuroimage Clin, 18\u003c/em\u003e, 510-517. doi:10.1016/j.nicl.2018.02.022\u003c/li\u003e\n\u003cli\u003eShields, G. S., Sazma, M. A., \u0026amp; Yonelinas, A. P. (2016). The effects of acute stress on core executive functions: A meta-analysis and comparison with cortisol. \u003cem\u003eNeurosci Biobehav Rev, 68\u003c/em\u003e, 651-668. doi:10.1016/j.neubiorev.2016.06.038\u003c/li\u003e\n\u003cli\u003eShull, A., Mayer, S. E., McGinnis, E., Geiss, E., Vargas, I., \u0026amp; Lopez-Duran, N. L. (2016). Trait and state rumination interact to prolong cortisol activation to psychosocial stress in females. \u003cem\u003ePsychoneuroendocrinology, 74\u003c/em\u003e, 324-332. doi:10.1016/j.psyneuen.2016.09.004\u003c/li\u003e\n\u003cli\u003eSkoluda, N., Strahler, J., Schlotz, W., Niederberger, L., Marques, S., Fischer, S., . . . Nater, U. M. (2015). Intra-individual psychological and physiological responses to acute laboratory stressors of different intensity. \u003cem\u003ePsychoneuroendocrinology, 51\u003c/em\u003e, 227-236. doi:10.1016/j.psyneuen.2014.10.002\u003c/li\u003e\n\u003cli\u003eSmith, J. M., \u0026amp; Alloy, L. B. (2009). A roadmap to rumination: a review of the definition, assessment, and conceptualization of this multifaceted construct. \u003cem\u003eClin Psychol Rev, 29\u003c/em\u003e(2), 116-128. doi:10.1016/j.cpr.2008.10.003\u003c/li\u003e\n\u003cli\u003eSong, X., Long, J., Wang, C., Zhang, R., \u0026amp; Lee, T. M. C. (2022). The inter-relationships of the neural basis of rumination and inhibitory control: neuroimaging-based meta-analyses. \u003cem\u003ePsychoradiology, 2\u003c/em\u003e(1), 11-22. doi:10.1093/psyrad/kkac002 %J Psychoradiology\u003c/li\u003e\n\u003cli\u003eSudimac, S., Sale, V., \u0026amp; K\u0026uuml;hn, S. (2022). How nature nurtures: Amygdala activity decreases as the result of a one-hour walk in nature. \u003cem\u003eMol Psychiatry, 27\u003c/em\u003e(11), 4446-4452. doi:10.1038/s41380-022-01720-6\u003c/li\u003e\n\u003cli\u003eSuzuki, Y., \u0026amp; Tanaka, S. C. (2021). Functions of the ventromedial prefrontal cortex in emotion regulation under stress. \u003cem\u003eSci Rep, 11\u003c/em\u003e(1), 18225. doi:10.1038/s41598-021-97751-0\u003c/li\u003e\n\u003cli\u003eWagels, L., Bergs, R., Clemens, B., Bauchm\u0026uuml;ller, M., Gur, R. C., Schneider, F., . . . Kohn, N. (2017). Contextual exclusion processing: an fMRI study of rejection in a performance-related context. \u003cem\u003eBrain Imaging Behav, 11\u003c/em\u003e(3), 874-886. doi:10.1007/s11682-016-9561-2\u003c/li\u003e\n\u003cli\u003eWang, C., Song, X., Lee, T. M. C., \u0026amp; Zhang, R. (2022). Psychometric Properties of the Chinese Version of the Brief State Rumination Inventory. \u003cem\u003eFront Public Health, 10\u003c/em\u003e, 824744. doi:10.3389/fpubh.2022.824744\u003c/li\u003e\n\u003cli\u003eWorley, N. B., Hill, M. N., \u0026amp; Christianson, J. P. (2018). Prefrontal endocannabinoids, stress controllability and resilience: A hypothesis. \u003cem\u003eProg Neuropsychopharmacol Biol Psychiatry, 85\u003c/em\u003e, 180-188. doi:10.1016/j.pnpbp.2017.04.004\u003c/li\u003e\n\u003cli\u003eYang, Y., Cao, S., Shields, G. S., Teng, Z., \u0026amp; Liu, Y. (2017). The relationships between rumination and core executive functions: A meta-analysis. \u003cem\u003eDepress Anxiety, 34\u003c/em\u003e(1), 37-50. doi:10.1002/da.22539\u003c/li\u003e\n\u003cli\u003eZhang, J., Wang, Y., Leong, C., Mao, Y., \u0026amp; Yuan, Z. (2023). Bridging Stories and Science: An fNIRS-based hyperscanning investigation into child learning in STEM. \u003cem\u003eNeuroimage, 285\u003c/em\u003e, 120486. doi:10.1016/j.neuroimage.2023.120486\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"fNIRS, stress, rumination, functional connectivity","lastPublishedDoi":"10.21203/rs.3.rs-3842177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3842177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRumination, thought to be induced by stressful events, is a pivotal factor contributing to cognitive vulnerabilities in stress-related disorders. Previous studies have demonstrated an association between the prefrontal cortex and stress. However, the functional connectivity in the prefrontal of high ruminators during stress is not fully understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e28 high trait rumination group (HTR) and 22 low trait rumination group (LTR) were recruited. Each participant underwent both the Trier Social Stress Test (TSST) and control task in a long-arm crossover design, while collecting functional near-infrared spectroscopy data. We analyzed the static and dynamic FC (DFC) under two different conditions and then compared the difference between the HTR and the LTR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eStress induction procedure was highly successful in both HTR and LTR. Analysis on static FC (SFC) showed that LTR exhibited a marked increase in SFC during the TSST, while HTR showed a comparatively lesser increase. Further analysis on DFC, the prefrontal-based DFCs were higher in LTR during TSST compared with control condition, but these patterns were not in HTR. But higher variability of DFC between left IFG and left MFG related to higher state rumination.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCurrent study may shed light on the aberrant prefrontal functional connectivity pattern underlying rumination and its association with stress. Further research in this area may elucidate the specific cognitive control mechanisms that are impaired in high ruminators and their impact on emotional regulation and psychological well-being.\u003c/p\u003e","manuscriptTitle":"Acute stress-related aberrant prefrontal based functional connectivity in high ruminators: An fNIRS study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-19 21:32:22","doi":"10.21203/rs.3.rs-3842177/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"945c7a21-563c-4985-97cc-b79067837492","owner":[],"postedDate":"January 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-07T07:37:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-19 21:32:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3842177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3842177","identity":"rs-3842177","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}