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Spatio-temporal dynamics of stress-induced network reconfigurations reflect negative affectivity

Background Chronic stress is an important risk factor in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk. Methods Using an established psycho-social stress task flanked by two resting-state scans, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 unmedicated participants with and without mood and anxiety disorders. To estimate block-wise changes in stress-induced brain activation and FC, we used hierarchical mixed-effects models based on denoised timeseries within a predefined stress network. We predicted inter- and intra-individual differences in stress phases (anticipation vs. acute stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines. Results We identified four subnetworks showing distinct changes in FC over time. Subnetwork trajectories predicted the stress phase (accuracy: 71%, pperm<.001) and increases in pulse rate (R2=.10, pperm<.001). Critically, individual spatio-temporal trajectories of changes across networks also predicted negative affectivity (ΔR2=.08, pperm=.009), but not the presence or absence of a mood and anxiety disorder. Conclusions Spatio-temporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.

that may map to risk for psychopathology could help pinpoint potential intervention targets (i.e., for non-invasive brain stimulation techniques (51) and improve means to study network perturbations in clinical trials.
Here, we used a hierarchical model of stress-induced changes in brain responses and FC to characterize trajectories of network reconfigurations across the stress cycle. Using individual FC signatures of stress adaptation, we identified dynamic FC changes that differentiate between stress states and predict interindividual differences in negative affectivity, providing a link between acute psycho-social stress reactivity and psychopathology. A) The psycho-social stress task consists of 15 blocks (50s each) of arithmetic problems interleaved with rest blocks (fixation cross, 40s each). The first five blocks are without aversive feedback (PreStress), followed by five blocks with negative feedback and time constraints (Stress), and another five blocks without aversive feedback (PostStress). Illustratively, we depict the average time series of the vmPFC after denoising across all measurements, which tracks the structure of the paradigm. B) Stress-induced changes in activation and functional connectivity (FC) from block to block are characterized for all regions and edges within a predefined stress network. C) Changes in activation and FC for each block are estimated using a hierarchical extension of generalized psychophysiological interactions (gPPI) estimated with one hierarchical linear model for each edge of the network, leading to group-level estimates of task-induced FC change for each block and 210 edges. D) For further predictive analyses, edges with similar changes over time are clustered into four subnetworks using hierarchical clustering. E) Lastly, we use individuallevel profiles of the four subnetworks FC changes (average across all edges per subnetwork) to predict either the task phase of unseen blocks (four features per block) or interindividual differences in adaptive vs. maladaptive stress reactivity, vmPFC = ventromedial prefrontal cortex, dACC = dorsal anterior cingulate cortex, Put = putamen, PCC = posterior cingulate cortex, pIns = posterior insula, aIns = anterior insula, pHipp = posterior hippocampus, mHipp = medial hippocampus, aHipp = anterior hippocampus, Amy = amygdala, SVM = support vector machine

fMRI data acquisition and preprocessing
Briefly, MRI data were acquired on a GE 3T scanner (Discovery MR750, GE, Milwaukee, U.S.A.). The functional data were 755 T2*-weighted echo-planar images (EPI) for the stress task and 155 EPIs for each of the resting-state scans. All fMRI data preprocessing was performed in Matlab 2018a (The Mathworks Inc., Natick, MA, USA) and SPM12 (v12; Wellcome Department of Imaging Neuroscience, London, UK). Data was slice-time corrected, realigned, normalized to the MNI-template using DARTEL (64), and spatially smoothed with a 6x6x6mm 3 full-width at half maximum kernel (Supplementary Information).

Questionnaire data: Non-negative matrix factorization
To extract interpretable dimensions capturing maladaptive stress reactivity from the questionnaires, we used non-negative matrix factorization (NNMF, (65)). In contrast to other dimension reduction methods, NNMF captures additive latent variables that are intuitively interpretable since all weights are positive. We included all items from the questionnaires after rescaling them between 0 and 1. We estimated NNMF (nnmf, MATLAB 2020b) with 150 iterations and 50,000 replicates to ensure stability. To determine the optimal number of dimensions, we used the elbow method for explained variance (66).

Stress response to the psycho-social stress task
The endocrine stress response was estimated as the change in cortisol concentration (∆Cort) between T1 and T6. Since we took blood samples in a subset of participants and a cortisol response to the procedure may confound the response to the stress task (58), we included a nuisance regressor (dummy coded) classifying participants with a response > 2.5 nmol/l (0.91 ng/ml, (58,67)) at T1 (20 min after arrival) compared to baseline (T0) as pre-task cortisol responders in all analyses.
The autonomous stress response was estimated as change in average pulse rate during the arithmetic blocks in the Stress phase compared to PreStress or PostStress, respectively (58). Subjective stress effects were estimated as the change in positive and negative affect (sum scores across 15 BSKE items) after the task (57,58) (Supplementary Information).

fMRI data
To compare stress-induced changes in brain response with previous studies, we assessed associations between stress-induced changes in activation and dimensions of stress susceptibility using previously reported first-level contrasts including one task regressor for each task phase (PreStress, Stress, and PostStress) Individual similarity was calculated as Fisher's z-transformed Pearson correlations. All analyses regarding fMRI data and psychometric variables (whole-brain regressions and elastic net predictions) included age, sex, pre-task cortisol, and average logtransformed framewise displacement as covariates (Supplementary Information).
To model dynamic changes in FC across the task, we extracted average timeseries from the preprocessed but unsmoothed stress task, the preceding and the following resting state in 21 ROIs based on previously reported activations ( We then used hierarchical linear models (LME, (72,73)) analogous to hierarchical generalized psychophysiological interactions (74) to estimate block-wise changes in activation in all 21 ROIs (Fig. 1B) and FC for all (21*20)/2 edges between nodes. We estimated one model for each edge with all predictors as random effects, deriving group-level and regularized individual-level estimates simultaneously (75)(76)(77).
Each model included the timeseries of one region (ROI1) as a dependent variable and the timeseries of the other region (ROI2) as independent variable together with one regressor for each of the 15 task blocks (convolved with hemodynamic response function) and the interaction of each task-block regressor with the predictive timeseries. Additionally, we included an interaction term for the post task resting-state to account for lasting stress-induced changes in FC. For interaction terms, the predictors were mean-centered. To evaluate the predictive performance of stress-induced FC changes within the subnetworks, we used machine learning algorithms to predict intra-and inter-individual differences in stress susceptibility. First, we predicted the task phase (PreStress, Stress, or PostStress) of unseen blocks based on average FC-changes in the 4 subnetworks using support-vector machine (SVM) classifiers with a radial basis function (one vs. one, SVC, scikitlearn (79), Python 3.7.0) with nested 10-fold crossvalidation. We used a leave-subject-out approach so that all data from 10% of the participants was in a held-out fold. Second, we used the same approach to predict zscored pulse-rate changes for each block using support-vector regression (SVR). To test whether the prediction provided information in addition to differences between stress phases (i.e., higher pulse rate during stress), we estimated LMEs including the observed pulse-rate change of each block, the stress phase and their interaction as predictors (random effects by participant, (80)). Last, we predicted interindividual differences in dimensions of stress reactivity derived from NNMF using activation (12 ROIs) and connectivity (4 subnetworks) trajectories across the task blocks. Since the models included between 68 (connectivity) and 180 (activation) features, we used elastic net (lasso, preset alpha = .5) with nested 10-fold cross-validation. Elastic net performs well if features are correlated and their number is moderately high compared to the number of observations (81). To account for confounding variables, we included them in the baseline prediction models and evaluated the incremental variance explained by fMRI features. Notably, average log-transformed framewise displacement was not associated with diagnosis status or psychometric dimensions of stress reactivity (rs < .12, ps > .11). Statistical significance was determined using permutation tests (1,000 iterations), where the outcome was shuffled together with the confounders to keep their correlation structure.

Statistical threshold and software
Statistical analyses were performed in R v3.5.1. (82). For whole-brain fMRI analyses, the voxel threshold was set at p<.001 (uncorrected). Clusters were considered significant with an FWE cluster-corrected p-value threshold of pcluster.FWE<.05. Additional LME models were estimated in R using lmerTest (83).

Average task-induced stress responses do not differ in mood and anxiety disorders
The task induced stress across multiple levels: positive affect decreased (b=-2.35, p<.001), while negative affect (b=7.6, p<.001, Fig. 2A) increased after the task.
Likewise, pulse rate (b=6.5, p<.001, Fig. 2B) increased during stress as well as salivary cortisol (b=.42, p=.007, Fig. 2C). On the neural level, stress led to significant deactivation in the DMN (PCC and angular gyrus), insula, dorsomedial prefrontal cortex as well as activation in the visual and parietal cortex (see Fig. 2E).
In contrast to previous reports, average stress reactivity did not differ between participants with and without mood and anxiety disorders on the autonomous, endocrine, or subjective level (Fig. 2, Supplementary Table S3). Likewise, there were no significant differences in brain response on average whole-brain maps (Fig. 2E), although neural similarity during stress recovery was significantly lower in participants with a mood or anxiety disorder (b=-0.05, p=.005, Fig. 2B), indicating slower recovery.
The lack of significant alterations of stress reactivity may be due to the heterogeneous phenotype of the patient group, or because individual stress-induced changes are only insufficiently reflected in average maps that lack dynamic information about networklevel reconfiguration.  Table S3). C) The task leads to an increase in salivary cortisol compared to baseline (T0). Thin lines depict individual cortisol trajectories, thick lines show group averages. The shaded area shows the timing of the stress task. D) Neural similarity (58) assessing regional and directional unspecific neural changes comparing PostStress but not Stress (b=0.0, p=.80) to the PreStress baseline differed between groups. E) Stress-induced changes in neural activation do not differ between groups. All models include age, sex, and pre-task cortisol response as covariates and response variables are residualized accordingly. Neural similarity models additionally included average framewise displacement. Error bars depict 95% confidence intervals.

Dynamic connectivity changes predict stress state and changes in pulse rate
To assess stress-induced changes throughout the stress cycle, we used concatenated data from the psycho-social stress task and two flanking resting-state scans (see Fig. S1). To derive stress-induced changes at a single-block resolution, we used mixed-effects models of fMRI timeseries. By fitting a hierarchical model, such estimates recover individual deviations from group averages more robustly (75)(76)(77).
While stress-induced changes in brain responses were similar across task blocks  Next, to verify that these spatio-temporal profiles indeed reflect the experimentally induced stress phases, we predicted the phase of unseen blocks based on individual-level estimates within the four subnetworks using SVM. Stress-induced changes in FC predicted stress phases with high accuracy (71% vs. 33% chance; pperm<.001, individual accuracy M=71% ±14%, Figure 4A). However, predictions solely based on changes in activation barely exceeded chance levels (40%, Figure 4B). The same FC features predicted relative changes in pulse rate of each block within participants using SVR (r=.31, R 2 =.10, pperm<.001, Figure 4C). Critically, successful prediction of pulse rate was not only driven by changes between task phases (e.g., higher pulse rate during stress), but also recovered differences in pulse rate of blocks within the same stress phase (ps between .02 and <.001, Fig. 4D  Predictions are best for the PreStress condition and the initial stress blocks. In contrast, the transition from Stress to PostStress is harder to differentiate, indicating a gradual recovery into discernable states of recovery. B: Predictions based solely on changes in brain responses do not exceed chance levels (40%). C: Changes in FC predict changes in pulse rate within participants (R 2 =.10, p<.001). To account for baseline differences, the pulse is z-standardized within each participant. D: Successful prediction of changes in pulse rate does not only recover differences between stress and non-stress conditions, but also predicts the magnitude of pulse-rate changes within stress recovery and acute stress phases. E-F: Comparing inter-individual differences in stress-induced changes (e.g., Stress-PreStress (r=.22, p=.002) or PostStress-PreStress (r=.25, p<.001)), derived from the observed and the predicted pulse rate changes of each block, showed significant correlations, indicating that interindividual differences in the stress-induced pulse rate response can also be recovered.

Dynamic connectivity changes predict negative affectivity as a transdiagnostic dimension reflecting maladaptive stress reactivity
To map differences in dynamic FC to psychological constructs related to stress adaptation, we derived questionnaire-based dimensions reflecting psychological responses to stress (i.e., resilience and susceptibility) using NNMF. To this end, we included single-item responses assessing state and trait factors including depressive symptoms (BDI), trait-like negative affect (TAI) as well as stress coping (SVF), intolerance of uncertainty (IoU) and resilience (Resilience). The most parsimonious solution revealed five interpretable dimensions (Fig. 5A). Two dimensions captured stress-resilient phenotypes (1:self-instruction; 2:social and cognitive coping) that loaded high on items from the resilience scale and the coping questionnaire (Fig. 5A).
In contrast, two dimensions captured maladaptive stress phenotypes (3:intolerance of uncertainty, 5:avoidance/distraction) that loaded high on the respective IoU and coping subscales. Finally, the fourth dimension (negative affectivity) loaded high on state depressive symptoms and TAI items (Fig. 5B) that are part of the 'depression' factor (22). Elevated TAI scores are highly prevalent in mood and anxiety disorders (23), and might have a shared genetic basis (24). Notably, individual scores on the five dimensions of stress adaptation correlated differentially with the subjective response to the psycho-social stress (Supplementary Figure S8 and Table S5).
Next, we evaluated whether these inter-individual differences in stress adaptation can be predicted by stress-induced changes in brain responses and FC using elastic net with nested cross-validation. Individual block-wise changes in FC predicted negative affectivity considerably better than confounding variables alone (ΔR 2 =.08; pperm=.009, Fig. 5C-E). Specifically, lower FC in the anticipatory PreStress phase across the DMN and cortico-limbic clusters as well as higher stress-induced FC in the same clusters were associated with higher negative affectivity (Fig. 5E). While Stress-induced network effects Kühnel et al. 20 block-wise changes in brain responses did slightly improve the prediction compared to confounding variables (ΔR 2 =.06; pperm=.038), combining block-wise changes in brain response and FC changes did not further improve the prediction. Stress-induced changes in FC did not predict other psychological dimensions of stress adaptation, while changes in brain responses predicted resilience: self-instruction (ΔR 2 =.091; p=.01, Supplementary Figure S10), suggesting that changes in activation and FC contribute to separable psychological constructs.
Since negative affectivity predominantly reflected scores of the BDI and TAI, we used the same algorithm to predict average scores of both questionnaires. Trait anxiety was predicted best based on stress-induced changes in brain responses and FC (combined: ΔR 2 =.10; pperm=.005; activation: ΔR 2 =.06; pperm=.019, FC: ΔR 2 =.06; pperm=.02). In contrast, neither BDI scores nor the presence of a mood or anxiety disorder were predicted better than when we relied only on confounding variables (Supplementary Figure S9-S10). Adding stressinduced changes in brain responses and FC improves the prediction of negative affectivity compared to permutations of only the response variable (chance level, yellow) or the response variable and the confounding variables age, sex, average framewise displacement and pretask cortisol response correspondingly (confound baseline, turquoise). Error bars depict 95% percentiles. E: Weights from the combined prediction model including stress-induced changes in brain response and FC (retained weights add to the prediction beyond confounding variables). DMN = default mode network, BDI = Becks depression inventory, TAI = trait anxiety inventory, IoU = intolerance of uncertainty, SVF = coping questionnaire (Stressverarbeitungsfragebogen), SOC = social support, AVO = avoidance.

Discussion
Symptoms of impaired stress regulation are common across many mental disorders and mapping individual symptoms onto stress-induced brain network reconfigurations may help increase our pathomechanistic understanding of disorders.
Here, we characterized dynamic changes in FC and brain response across all phases of a psycho-social stress task in participants with and without a range of mood and anxiety disorders. First, we showed that dynamic stress-induced changes in FC, but not activation, recover the current state of the stress cycle. Second, we showed that only spatio-temporal FC profiles predicted inter-individual differences in a dimension related to negative affectivity, a well-established transdiagnostic marker of heightened stress susceptibility. Third, reduced FC in subnetworks dominated by DMN and corticolimbic edges during stress anticipation and increased FC during stress added to the prediction, highlighting that anticipatory stress regulation (11,84,85) could help unravel signatures indicative of a key psychopathology dimension of affective disorders (86,87). Taken together, our results provide a quantitative mapping of dynamic brain connectivity changes in the acute stress response that reflect psychological differences in affective processing (i.e., measures that have been associated with mood and anxiety disorders). Our results highlight the large potential of novel analysis techniques that capitalize on the rich individual information in spatio-temporal brain response profiles to stress, supporting the idea that mood and anxiety disorders can be best understood as disorders arising from individual differences in dynamic network interactions.  (44), where a dynamic network perspective helps extracting unique information (48,109) that tracks adaptive responses to stressors more faithfully from a neurobiological perspective (110)(111)(112).
Specifically, our results suggest that network-based reconfigurations in FC during stress as well as in anticipation of stress, particularly between the DMN and edges of a cortico-limbic subnetwork, are important markers reflecting negative affectivity which is in line with extensive previous research on mood and anxiety disorders (113)(114)(115).
Notably, neurobiologically-inspired treatments such as TMS target comparable networks to elicit clinically meaningful responses (116) and present-centered psychotherapy has been shown to normalize cortico-limbic processing in stress-related disorders (117). To conclude our predictive model demonstrates that an emphasis on modeling individual differences in psychopathology and network-based reconfiguration within the stress network improves prediction and interpretability (45,118), dovetailing well with recent insights on effective treatment mechanisms.
Although our study provides an innovative approach to bridge the predictive gap between acute stress reactivity and psychological responsivity, it has limitations that need to be addressed in future work. First, to ensure robust inferences, we had to aggregate connectivity within subnetworks to balance model complexity with the number of participants and larger samples will allow to disentangle the specific contribution of connections between nodes in the future. Second, it is conceivable that stress-induced changes on timescales that are not explicitly modeled with our approach (i.e., specific events such as feedback) could improve the prediction. Third, to establish robustness, replication of the spatio-temporal signatures of negative affectivity in an independent dataset is preferable. Likewise, whether the dynamic changes in network FC generalize to other stress tasks remains to be shown and will be an important step for a better understanding of FC in relation to stress-related disorders. Finally, while previous work has shown that negative affectivity is associated with mood and anxiety disorders, our association between short-term stress-induced FC changes and this psychopathological trait cannot address the question of a causal link.
To summarize, dynamic FC changes within the stress network, but not changes in activation, tracked the current stress phase and corresponding changes in pulse rate. Furthermore, these network-based reconfigurations, driven by reduced corticolimbic and DMN FC in anticipation of stress and increased FC during stress, predicted the psychopathology dimension negative affectivity. Collectively, our results emphasize that characterizing the neural stress response across the entire cycle by modeling individual signatures with high spatial and temporal resolution in a hierarchical model improves the prediction of key changes within participants (i.e., stress phase, pulse rate) and between participants (i.e., differences in negative affectivity). Crucially, since individual signatures predicted the psychopathology dimension negative affectivity, but not the presence of any mood and anxiety disorder, our study highlights the need for transdiagnostic approaches to better understand the multifaceted psychopathological profile of individuals within broad disorder categories.
Therefore, our results offer novel insights into stress-related pathomechanisms of mental disorders, providing a potential endophenotype that may guide future translational research.

Acknowledgement
We thank Anna Hetzel and Ines Eidner for help with data acquisition and Alina Tontsch, Manfred Uhr and the team of the Max Planck Institute of Psychiatry Biobank for sample processing. NBK received salary support from the University of Tübingen, fortune grant #2453-0-0.

Author contributions
EBB and PGS were responsible for the study concept and design. MC and PGS validated the paradigm and procedure. AK and NBK conceived the method and AK performed the data analysis. AK wrote the first draft of the manuscript and NBK contributed to the writing. All authors contributed to the interpretation of findings, provided critical revision of the manuscript for important intellectual content and approved the final version for publication.

Financial disclosure
The authors declare no competing financial interests.