Biology subjects

Walter, H.

Publications and source records attributed to Walter, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Dissociating Resting-State Networks of Pedohebephilic Preference and Child Sexual Offending Using Network-Based Prediction

Background: Pedohebephilia (P+), the sexual preference for prepubescent and early pubertal minors, regularly causes distress in those affected. While associated with child sexual offending (CSO), P+ is not a prerequisite for CSO. Limited neuroimaging studies have identified alterations associated with P+, while these may primarily reflect offending history. The correlates of P+ independent of CSO therefore remain unclear. We examined this distinction using intrinsic functional networks within a multivariate predictive framework. Methods: Using resting-state functional connectivity (rsFC) and network-based prediction (NBS-Predict), we performed pairwise classifications among non-offending pedohebephilic men (P+CSO-, n=35), pedohebephilic men with CSO (P+CSO+, n=38), and non-pedohebephilic controls without CSO (P-CSO-, n=63). Classification-relevant subnetwork nodal strength was associated post hoc with trait impulsivity within P+ groups. Results: P+CSO- were significantly distinguished from P-CSO- (balanced accuracy [BAC]=0.621, pFDR=.041), and showed reductions between default-mode hubs and temporoparietal somatomotor and ventral attention regions. P+CSO+ were also significantly distinguished from P+CSO- (BAC=0.645, pFDR=.032) and yielded a smaller subnetwork centered on the substantia nigra. Against P-CSO-, P+CSO+ showed widespread reductions, particularly across subcortical, default mode, visual, limbic, and somatomotor regions (BAC=0.640, pFDR=0.032). Right precentral gyrus nodal strength correlated with impulsivity in P+CSO+ only (r=0.57, pFDR=0.024). Conclusions: Whole-brain rsFC contains information distinguishing pedohebephilic preference in the absence of known CSO, while P+CSO+ show additional and more widespread network alterations involving systems implicated in action selection and behavioral regulation. These findings open new avenues for integrating pedohebephilia into broader neurobiological research frameworks.

neuroscience↗

bioRxiv2026

Predicting alcohol consumption from resting-state functional connectivity: Feature relevance and the potential of meta-matching.

Alcohol use disorder (AUD) is among the most prevalent mental disorders. Associations between resting-state fMRI functional connectivity (FC) and AUD have been reported but rarely replicated. In this preregistered study, we investigated the extent to which FC predicts alcohol consumption, as measured by the AUDIT-C score, in a large sample of non-treatment-seeking alcohol consumers (N = 739), including 528 participants with varying degrees of AUD severity. To optimize performance, we compared kernel ridge regression (KRR) trained directly on this sample with transfer-learning models based on multilayer meta-matching embeddings, from which we derived data-driven relevance maps at the connection, region, and network levels. We tested split-half reliability, reproducibility across cohorts, and outcome specificity of the relevance maps. Both models predicted AUDIT-C scores moderately but significantly (r {approx} 0.15). Meta-matching did not outperform KRR in prediction. Relevance maps showed limited split-half reliability, consistent with the modest predictive performance, but reproduced moderately well across two independent cohorts. These maps indicate reduced connectivity among limbic regions, the anterior middle temporal gyrus, the dorsomedial prefrontal cortex, and dorsal attention network regions. In contrast, hypothesized hyperconnectivity patterns involving the striatum, anterior cingulate cortex, and precuneus were less reliable and mostly non-significant. Overall, our findings suggest that resting-state FC contains reproducible but weak signals related to alcohol consumption. Transfer learning improved relevance-map reliability but not prediction performance and reduced outcome specificity. These results support the utility of interpretable machine learning for identifying connectivity patterns related to drinking behavior, while also highlighting current limitations in its predictive power.

neuroscience↗

bioRxiv2026

Neurostructural correlates of real-life social drinking risk in alcohol use disorder

The social context of alcohol consumption is increasingly recognized as an important factor in the development of alcohol use disorder (AUD). However, individuals with AUD may differ in how strongly alcohol use by people around them is associated with their own drinking, and the neurostructural basis of this variation remains unclear. Here we studied 83 individuals with AUD (53 men, 30 women; mean [SD] age, 37.20 [12.42] years; median DSM-5 AUD criteria, 4) using six weeks of ecological momentary assessment and structural magnetic resonance imaging (n = 80). Across 5,221 non-solitary observations, greater exposure to surrounding drinkers was associated with increased odds of momentary drinking (odds ratio, 4.23; 95% CI, 3.60-4.97), with significant between-person variation that we quantified as social drinking risk (SDR). Higher SDR was associated with lower gray matter volume in the pregenual anterior cingulate cortex (T = 5.40; P < .001) and weaker pregenual anterior cingulate cortex-ventral striatum structural covariance (T = 3.15; P = .032). These findings support SDR as an ecologically grounded phenotype linking daily-life social-context sensitivity to cingulostriatal structure, with potential relevance for mechanistic and interventional research in AUD.

neuroscience↗

bioRxiv2026

Impaired Control Symptoms in Alcohol Use Disorder Show Convergent Associations Across Behavioral and Neural Markers of Inhibitory Control and Drinking Behavior

Background: Deficits in inhibitory control are considered a central feature of alcohol use disorder (AUD), yet behavioral and neuroimaging findings have been inconsistent. One potential explanation is the marked heterogeneity of AUD, which encompasses multiple symptom dimensions that may differ in their underlying neurocognitive mechanisms. In this study, we investigated whether behavioral and neural markers of inhibitory control are associated with specific DSM-5 AUD symptom domains. Methods: A total of 589 participants (432 with non-treatment seeking AUD, 157 controls) completed a Stop-Signal Task (SST) during fMRI. Associations between DSM-5 symptom domains (impaired control over alcohol use, risky use, social problems, physical dependence) and SST performance, inhibition-related brain activation, and alcohol consumption were examined. Real-life drinking behavior was additionally assessed over 12 months using ecological momentary assessment (EMA). Results: Greater impaired control symptom severity was associated with poorer performance on go trials and diminished post-error slowing during SST. At the neural level, higher impaired control symptom severity was associated with reduced activation across fronto-insular, parietal, cingulate, supplementary motor, and cerebellar regions. In contrast, other symptom domains showed little to no associations with inhibition-related behavioral or neural measures. Higher impaired control symptom severity was associated with greater alcohol consumption across questionnaire-based and EMA-based assessments. Conclusions: In this large cohort, altered behavioral performance and recruitment of inhibition-related brain regions in the SST was selectively linked to impaired control symptoms. These findings highlight the importance of symptom-based approaches for understanding heterogeneity in addiction and may inform the development of more targeted interventions.

neuroscience↗

bioRxiv2026

Early life stress alters lifespan trajectories of amygdala development: a cross-species model of amygdala burnout

Early-life stress is associated with alterations in amygdala volume, but findings vary across studies and species. Rodent models typically report increased amygdala volume, whereas human studies often find reductions or mixed effects. We propose the amygdala burnout hypothesis, which suggests that early-life stress alters amygdala developmental trajectories, producing initial volumetric increases that are followed by reductions later in life. To test this hypothesis, we examined amygdala development across rodent and human cohorts spanning adolescence to older adulthood. In rodents, chronic stress was associated with increased amygdala nuclei volumes during adolescence. Comparable increases were observed in adolescents from the IMAGEN consortium, with evidence of a dose-dependent association between childhood stress and amygdala developmental trajectories. In contrast, analyses of older adults from the UK Biobank revealed reduced bilateral amygdala volume associated with early-life stress. Together, these findings support a developmental model that reconciles previously conflicting observations across species and age groups. Early-life stress is associated with alterations in amygdala volume, but findings vary across studies and species. Rodent models typically report increased amygdala volume, whereas human studies often find reductions or mixed effects. We propose the amygdala burnout hypothesis, which suggests that early-life stress alters amygdala developmental trajectories, producing initial volumetric increases that are followed by reductions later in life. To test this hypothesis, we examined amygdala development across rodent and human cohorts spanning adolescence to older adulthood. In rodents, chronic stress was associated with increased amygdala nuclei volumes during adolescence. Comparable increases were observed in adolescents from the IMAGEN consortium, with evidence of a dose-dependent association between childhood stress and amygdala developmental trajectories. In contrast, analyses of older adults from the UK Biobank revealed reduced bilateral amygdala volume associated with early-life stress. Together, these findings support a developmental model that reconciles previously conflicting observations across species and age groups.

neuroscience↗

bioRxiv2026

Linking post-stress brain connectivity to acute cortisol reactivity using network-based inference and prediction

Background: Acute cortisol responses to psychosocial stress vary substantially across individuals, yet how this variability is reflected in post-stress resting-state functional connectivity (rsFC) remains unclear. Although prior work has linked stress-related endocrine responses to brain connectivity, studies have been limited by small samples, region-of-interest approaches, or a sole focus on group-level analyses. Here, we investigated whether acute cortisol increase is associated with, and can be predicted from, whole-brain post-stress rsFC. Methods: We analyzed 339 healthy participants from two ScanSTRESS datasets using complementary inferential and predictive approaches. First, we used the Network-Based Statistic (NBS) to identify connected rsFC networks associated with acute cortisol increase, controlling for age, site, and sex/hormonal status. Second, we predicted participants' acute cortisol increase from their connectivity patterns using NBS-Predict and Connectome-Based Predictive Modeling (CPM). Together, we examined the cortisol-rsFC relationship at the population and individual levels. Results: Greater cortisol responses were associated with lower post-stress rsFC within a significant distributed network comprising 258 connections among 78 regions, centered on thalamic nuclei and pallidal regions and extending to default-mode, limbic, orbitofrontal, and cerebellar regions. Sex-stratified analyses revealed a significant negative association only in females, but formal sex-difference contrasts were not significant. NBS-Predict and CPM yielded modest but significant out-of-sample prediction, with predictive networks converging on subcortical and posterior cingulate regions. Conclusions: Post-stress rsFC carries convergent inferential and predictive information about individual HPA-axis reactivity. Stronger cortisol responses were characterized by reduced connectivity within a distributed subcortical-cingulate network, supporting a network-level perspective on neural-endocrine coupling following acute stress.

neuroscience↗

bioRxiv2026

Sex-specific associations of childhood adversity with frontostriatal network organization and anhedonia in young adulthood

IntroductionAnhedonia is a transdiagnostic psychiatric symptom linked to increased functional connectivity between the prefrontal cortex and striatum. Here, we examined how dimensions of early adversity contribute to this profile of connectivity. MethodsIn a European community sample of young adults (IMAGEN), we examined cross-sectional (n=613) and longitudinal (n=332) associations of adversity dimensions with resting-state fMRI-derived connectivity. We selected 10 ROIs from anhedonia literature, defined in the functional images as 4mm-radius spheres. We then used network-based regression models to identify clusters of ROI-ROI connections associated with threat and deprivation scores, using interaction terms to examine sex and age-specific associations. We also examined associations between adversity and anhedonia, operationalized using factor analysis of six items from self-report surveys. ResultsAt age 18-22, we identified sex-specific associations between deprivation and connectivity for a cluster of 9 ROI-ROI connections (p-FWE=0.038), primarily involving the nucleus accumbens. Specifically, we observed positive associations between deprivation and connectivity in males, and negative associations in females. In the longitudinal analysis, negative deprivation associations in females attenuated with age for a cluster of 14 connections (p-FWE=0.009). A cluster of 17 connections also had initial positive associations with threat in females that attenuated with age (p-FWE=0.008). No such longitudinal changes were observed in males. Higher deprivation was linked to increased later anhedonia in males but not females (p=0.026). ConclusionCompared to females, young adult males may be more vulnerable to developing anhedonia after experiencing deprivation in childhood. Dimensions of early adversity are linked to distinct pathways of frontostriatal development.

neuroscience↗

bioRxiv2026

Protracted Maturation of Proactive and Reactive Systems Predicts Cognitive Stability and Psychopathology : A longitudinal multi-cohort study

Inhibitory control matures progressively from childhood to early adulthood, yet the neural mechanisms driving this development and their relevance to psychiatric risk remain poorly understood. Guided by the Dual Cognitive Control model, we leveraged longitudinal fMRI from two independent cohorts in the US (ABCD, ages 9-12) and Europe (IMAGEN, ages 14-22) to map the spatiotemporal dynamics of reactive and proactive control using novel single-trial modeling and representational similarity analysis. We found both reactive and proactive stopping networks stabilize after mid-adolescence, tracking the developmental patterns of inhibitory control and behavioral stability. By decoding trial-by-trial fluctuations along a speed-caution continuum, we demonstrate that brain-behavior coupling to a proactive "Safe state" tightens progressively with age. Furthermore, network-level representational coherence of this Safe state emerged as a scanner-invariant, trait-like biomarker that robustly predicted inhibitory control, behavioral stability, and transdiagnostic psychopathology across multiple developmental windows, providing a validated neural phenotype for precision psychiatry.

neuroscience↗

bioRxiv2026

Adversity and adolescent brain development: differential associations with grey and white matter across two longitudinal cohorts

Globally, 60% of the population has experienced at least one type of adversity (e.g., emotional abuse, bullying) across infancy, childhood, and adolescence. Such experiences have been linked to an increased risk for mental health disorders. Changes in brain structure following experiences of childhood adversity have been hypothesised to be a mechanistic pathway explaining later mental health issues. However, to understand how changes in brain structure might mediate the effects of adversity, it is essential to identify which underlying neuronal processes may be affected by different types of adverse experiences. A key open question is whether grey or white matter is more vulnerable to adversity, as these two structures reflect distinct neurobiological mechanisms. This study investigated whether differences in trajectories of grey and white matter development during adolescence can be explained by exposure to different types of adversity. We applied the Adverse Adolescent Experiences Framework (Pollmann et al., 2025) categorising adversity into four levels: Intrapersonal (e.g., accidents), Caregiver (e.g., emotional neglect), Peer (e.g., bullying), and Community (e.g., neighbourhood safety). Exposure to each of the four factors was estimated through principal components analyses. We analysed two large longitudinal datasets: the Adolescent Brain Cognitive Development study ([~]12,000 adolescents measured at ages 10, 12, and 14) and the IMAGEN study ([~]1,400 adolescents measured at ages 14, 19, and 22). Using latent growth curve models, we captured individual differences in brain development by estimating baseline levels (intercepts) and rates of change (slopes) for total grey matter volume and mean white matter fractional anisotropy. In both cohorts, we found significant interindividual variability in baseline levels and rates of change for both grey matter volume and fractional anisotropy. Caregiver, Peer, and Community adversities were negatively associated only with the intercepts of grey matter volume and white matter fractional anisotropy. Importantly, associations differed between grey and white matter. In ABCD, Peer and Community adversities were more strongly associated with grey matter volume intercepts. In contrast, in IMAGEN, Caregiver, Peer and Community adversities were more strongly linked to white matter fractional anisotropy intercepts. This suggests that adversity has unique associations with grey and white matter, rather than exerting a uniform influence on brain structure. By demonstrating that different environments generate distinct biological associations with brain maturation, this work underscores the need to consider both grey and white matter when assessing the neurodevelopmental pathways to outcomes across the lifespan.

neuroscience↗

bioRxiv2026

Trajectories of brain organisation transition from predicting externalising to internalising symptoms across adolescence

Understanding the dynamics of brain-behaviour relationships during adolescence is critical for elucidating the neurodevelopmental basis of mental health. Leveraging two large-scale longitudinal cohorts--the Adolescent Brain Cognitive Development (ABCD) and IMAGEN studies, comprising over 10,000 participants aged 10 to 22 years with six waves of multimodal neuroimaging and behavioural data, we applied multi-view sparse canonical correlation analysis to investigate evolving associations between structural MRI, resting-state functional connectivity, and multi-domain behavioural measures. Our findings reveal four fundamental patterns of developmental reorganisation in brain-psychopathology relationships. First, symptom profiles evolved from predominantly externalising features (aggression, attention problems) at ages 10-12 toward global psychopathology by age 14, then transitioned toward internalising features (e.g., anxiety, depression) by ages 19-22, reflecting fundamental shifts in vulnerability from behavioural dysregulation to affective disturbance. Second, cortical thickness exhibited negative associations with externalising symptom profiles throughout development. During early adolescence (ages 10-14) this was driven by broadly distributed decreases across sensorimotor, temporal, visual, and cingulate regions alongside overall mean cortical thickness. After 14, this diffuse pattern shifted towards late maturing association cortices, notably the dorsolateral prefrontal and lateral temporal cortices. Third, this was accompanied by subcortical effects that exhibited greater age-specificity: whilst cerebellar volume contributions were evident at most timepoints, basal ganglia volume influence was principally evident in early development (ages 10-12), with thalamic structures and global subcortical grey matter volume becoming dominant at age 14, marking a transition in which subcortical structures mediate psychopathology associations. Fourth, functional connectivity showed a more dynamic developmental trajectory. During early adolescence, symptom associations were driven by positive connectivity between cognitive control and sensorimotor networks, whereas late adolescence exhibited predominantly positive connectivity patterns, transitioning from dense sensorimotor-frontoparietal configurations to more specific patterns involving the central executive and default-mode networks. These findings fundamentally challenge static biomarker models, demonstrating that adolescent psychopathology reflects developmentally contingent brain-behaviour relationships rather than static neural markers. Age 14 emerges as a critical inflection point marked by convergent thalamic reconfiguration, global subcortical grey matter dominance, and symptom profile transitions. This work provides an empirical foundation for precision mental health strategies tailored to specific developmental windows, with implications for reducing psychiatric burden in youth.

neuroscience↗

bioRxiv2026

Reproducible Human Reward Imaging Phenotypes Exhibit Differential Sensitivity to Dopamine D2 Receptor Antagonism

Human reward processing varies along cue-centric and outcome-centric axes, but a reproducible mechanistic account of individual variation in incentive salience attribution has been lacking. Using fMRI across five cohorts (N-total=1,251; N1=890; N2=245; N3=34; N4=48; N5=34), we identified two robust imaging phenotypes mirroring sign- and goal-tracking (ST-like, GT-like). ST-like individuals showed dominant ventral striatal responses to reward-anticipation cues and sustained incentive salience attribution; GT-like individuals showed heightened responses to reward outcomes. This distinction was replicable across sites and independent samples. Single-dose and repeated-dose D2/D3 antagonism (risperidone, haloperidol, amisulpride) selectively reduced anticipatory ventral striatal activity in ST, with single-dose antagonism additionally producing a parallel drop in self-reported energy. Instead, D2/D3 partial agonism (aripiprazole) increased anticipatory and reduced outcome-phase responses in GT. In a psychosis cohort, antipsychotic D2 affinity was associated with blunted anticipatory signals and higher negative symptom burden, offering a neuroimaging-driven basis for stratifying patients and predicting response to dopaminergic agents.

neuroscience↗

bioRxiv2026

Brain Functional Connectivity Signatures of Craving Across Substance Use Disorders: A Transdiagnostic Approach

Craving is a hallmark feature of substance use disorders (SUDs) and a major risk factor for relapse, yet reliable biomarkers that enable individual-level prediction remain scarce. Here, we applied connectome-based predictive modeling (CPM) to resting-state functional magnetic resonance imaging (fMRI) data in a transdiagnostic sample of individuals with cannabis, opioid, or tobacco use disorder (n = 78). Using CPM, we identified a distributed functional brain network that reliably predicted self-reported craving. Computational lesion analyses revealed key contributions from the right medial orbitofrontal cortex, right dorsal posterior cingulate cortex, and left lateral medial frontal gyrus. Importantly, the craving network generalized across two independent datasets. In alcohol-dependent patients (n = 41), the identified craving network, along with its positive and negative subnetworks, predicted distinct cognitive and motivational components of craving. In a second external dataset of smokers (n = 28), the craving network predicted both nicotine craving after abstinence as well as intra-individual changes in craving between sated and craving states. Together, these findings provide evidence for a robust, transdiagnostic craving signature in SUDs. Future work should assess the networks predictive utility for longitudinal outcomes such as relapse risk and treatment response.

neuroscience↗

bioRxiv2026

Digital Twin Brain simulation and manipulation of a functional brain network underlying mental illness

Linking synaptic-level perturbations to distributed brain-network dynamics remains a central challenge for understanding and treating mental illness. Although recent whole-brain models can reproduce individual brain activity patterns, they largely function as descriptive simulators rather than mechanistic, intervention-capable systems. Here we present an intervention-capable digital twin of the human brain, integrating individual neuroanatomy and task-evoked dynamics within a neuronal-scale framework. Individualised digital twin brains recapitulate a participant-specific compact cortico-subcortical network phenotype that captures transdiagnostic psychopathology across population and clinical cohorts. In silico modulation of excitatory and inhibitory synaptic conductance produces bidirectional, heterogeneous network responses across individuals. Population-scale simulations stratify individuals and predict longitudinal symptom trajectories from DTB-derived response profiles. Independent pharmacological functional MRI data further validate the predicted baseline-dependent network responses in vivo. Together, these findings establish digital brain models as experimental platforms for mechanistic perturbation, behavioural prediction and stratification, providing a foundation for precision neuroscience and psychiatry.

neuroscience↗

bioRxiv2025

Predicting stress response trajectories: Differential contributions of limbic and prefrontal regions to cortisol and affective responses

Why do individuals respond differently to stress? Since rodent studies indicated that stress regulation relies on limbic and medial prefrontal cortex (mPFC) outputs, we aimed to investigate whether data from these regions could also predict cortisol and affect trajectories following psychosocial stress in humans. In this pre-registered study, 281 healthy adults (145 female) were exposed to ScanSTRESS. Repeated assessments of salivary cortisol and negative affect were used to identify response trajectories (i.e. groups of participants) using latent class mixture modelling (LCMM). LCMMs without brain predictors were compared to LCMMs including structural (volume, thickness) and functional (activation, exposure-time effect) predictors from the amygdala, hippocampus, or mPFC regions. Results showed that cortisol LCMMs without brain predictors exhibited a single mean trajectory. Adding brain predictors resulted in three to four response trajectories, depending on region and outcome. Within identified models, cortisol hyper-response trajectories were predicted by larger amygdala and hippocampus volumes. Cortisol non-responses were predicted by greater amygdala activation and volume. Elevated baseline cortisol was predicted by higher hippocampal activation. mPFC markers did not predict cortisol trajectories, however, medial orbitofrontal cortex parameters identified affect response profiles mirroring trait-like affect. Together, our findings suggest dissociated roles of limbic and mPFC regions in stress regulation: While limbic structures predicted cortisol responses, the mPFC shaped affective experience.

neuroscience↗

bioRxiv2025

PlanktonFlow : hands-on deep-learning classification of plankton images for biologists

High throughput image-acquisition devices tremendously increase our capacity to observe biodiversity. However, for many biologists, the high-performance deep learning models that are needed to make biological sense out of very-large image sets remain difficult to implement. To fill this gap in biologists toolkit, we developed PlanktonFlow, a Python pipeline that streamlines the automation of plankton-image taxonomic assignment. PlanktonFlow makes it easy for inexperienced users to run a whole sequence of (i) automated image pre-processing and augmentation of rare classes, (ii) training up to four different high-performance convolution neural networks (CNNs: ResNet, DenseNet, EfficientNet, and YOLO), (iii) computing model classification-performance metrics so as to choose the best-performing model, and (iv) running inference on novel image sets. PlanktonFlow further includes routines to easily fine tune model hyper-parameters and optimize models performances. Using a tutorial style, we demonstrate the usage of PlanktonFlow to analyse freshwater-plankton images produced with the FlowCAM, comparing the relative classification performances of the four optimized CNN architectures. For a baseline comparison with a reference tool used by plankton biologists, we further assessed the classification performances of the EcoTaxa web-service when used without any eye validation in a pure-prediction mode. In line with a previous study on a benchmark plankton dataset, we found that EfficientNet-B5 achieved the highest macro-averaged F1 Score, outperforming other CNN models, which all surpassed EcoTaxa. Hyper-parameter optimization was key to improving model performances. To ease an appropriation and further developments by the community, PlanktonFlow is open source, comes with a detailed documentation, and has a modular structure. We foresee that future work could integrate new deep-learning architectures (e.g., vision transformers, semi-supervised learning), and test the pipeline on images produced by other devices or from other taxonomic groups.

ecology↗

bioRxiv2025

Linking brain structure to stress reactivity: Cingulate surface area predicts acute cortisol responses

BackgroundAltered stress responses are closely linked to mental disorders, but the role of brain structure in acute cortisol responses to psychosocial stress remains underexplored, particularly in healthy individuals. Previous studies, with predominantly small samples, primarily focused on selected limbic regions and functional measures. Thus, this study investigates associations between brain structure and cortisol responses to psychosocial stress, exploring if hypothalamic-pituitary-adrenal axis reactivity can be predicted from brain morphology. MethodsOur study included 291 subjects (157 females, 18-62 years) and consisted of two parts. First, a confirmatory analysis examined associations between specific cortical surface area, thickness, and subcortical volume with stress-induced cortisol increases using Permutation Analysis of Linear Models (PALM). Second, we conducted an exploratory whole- brain vertex-wise analysis, followed by out-of-sample prediction of cortisol increases from structural measures. ResultsWe found consistent negative associations between cingulate cortex (CC) sub- structures and acute cortisol increases. In PALM- and whole-brain analysis, a smaller surface area of the left rostral and caudal anterior cingulate cortex (cACC), posterior cingulate cortex, and right cACC were associated with higher cortisol stress responses, particularly in males. The left cACC surface area emerged as the most promising predictor in machine learning analyses. Additionally, other fronto-limbic structures were also associated with or predictive of acute cortisol reactivity. ConclusionsOur findings demonstrate that cortical and subcortical structural measures, particularly smaller surface areas of the CC, predict acute hormonal stress responses. Notably, the left cACC emerged as the most consistent predictor, underlining its potential as a biomarker for stress-related diseases.

neuroscience↗

bioRxiv2025

The elusive neural signature of emotion regulation capabilities: evidence from a large-scale consortium

Cognitive reappraisal is a fundamental emotion regulation strategy for mental and physical well-being, but how its neural mechanisms relate to individual differences remains poorly understood. In a consortium effort analyzing 40 fMRI datasets (N=2,175), we examined the relationship between neural activation during reappraisal tasks and three core individual difference indices of reappraisal capabilities: (1) trait questionnaires, (2) task-based affective ratings, and (3) amygdala down-regulation. Strikingly, there was no shared overlap across these three common indices. Only a very weak correlation emerged between amygdala down-regulation and task-based affective ratings. Whole-brain analyses revealed no reliable neural associations with trait questionnaires, and associations with task-based affective ratings fell outside canonical emotion regulation networks (e.g., prefrontal circuitry). Moreover, amygdala down-regulation, often interpreted as a stable individual marker, was confounded by person-specific whole-brain responses -- a limitation extending to fMRI research beyond the emotion regulation domain. These findings challenge the assumption that an individuals prefrontal activity is a valid indicator of their reappraisal capabilities and suggest that common trait, behavioral, and neural measures might capture distinct facets of emotion regulation. More broadly, our results highlight concrete methodological challenges for fMRI research on individual differences, with implications extending beyond emotion regulation to the neuroscience of personality, psychopathology, and general well-being.

neuroscience↗

bioRxiv2025

Chronic pain is linked to a resting-state neural archetype that optimizes learning from punishments

Chronic pain is a leading cause of disability, yet its underlying susceptibility traits remain unclear. Disorders like chronic pain may stem from extreme neural types, or archetypes, optimized for specific cognitive strategies and reflected in patterns of resting-state networks. Here, we examined a sample from the general population (n = 892) and three clinical samples with subacute back pain (n = 76), chronic back pain (n = 30), and treatment-resistant depression (n = 24). Using the sample from the general population, we found three neural archetypes that prioritize different cognitive strategies. Clinical pain samples, compared to the sample from the general population, mapped close to an archetype optimized for punishment learning (Archetype P). We replicated these results by recomputing the archetypes starting from the clinical pain samples, additionally revealing an association between Archetype P and pain severity. These findings suggest a neural-cognitive trait underlying susceptibility to chronic pain.

neuroscience↗