Biology subjects

Kreuzpointner, L.

Publications and source records attributed to Kreuzpointner, L..

3 recordsLinked to original sources

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↗

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

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↗