Linking post-stress brain connectivity to acute cortisol reactivity using network-based inference and prediction
Serin, E.; Emurla, E.; Baertl, C.; Giglberger, M.; Konzok, J.; Peter, H. L.; Kreuzpointner, L.; Kudielka, B. M.; Wuest, S.; Erk, S.; Walter, H.; Henze, G.-I.
Show abstract
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.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Pubertal hormones and the early adolescent female brain: a multimodality brain MRI study 93%
- Mental health in the UK Biobank: a roadmap to self-report measures and neuroimaging correlates 92%
- Tensorial Independent Component Analysis Reveals Social and Reward Networks Associated with Major Depressive Disorder 92%
Similar papers in this journal
- Maternal Protection in Childhood is Associated with Amygdala Reactivity and Structural Connectivity in Adulthood 93%
- The Developmental Trajectory of the Social Brain: A Movie-Based Exploration from childhood to adolescence 92%
- Lower neural value signaling in the prefrontal cortex is related to childhood family income and depressive symptomatology during adolescence 92%
Similar papers in this journal
- Linking brain structure to stress reactivity: Cingulate surface area predicts acute cortisol responses 96%
- Serotonin and childhood maltreatment interact to shape brain architecture and anxious avoidant behavior, a TPH2 imaging genetics approach 93%
- Corticolimbic connectivity mediates the relationship between pubertal timing and mental health problems 93%
Similar papers in this journal
- The Effects of Stress Across the Lifespan on the Brain, Cognition and Mental Health: A UK Biobank study 92%
- Metabolic state shapes cortisol reactivity to acute stress: A systematic review and meta-analysis of metabolic and hormonal modulators 90%
- Juvenile exposure to acute traumatic stress leads to long-lasting alterations in grey matter myelination in adult female but not male rats 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.