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

By Anne Kuehnel, Michael Czisch, Philipp G Saemann, BeCome Study Team, Elisabeth B. Binder, Nils B Kroemer

Posted 18 Jul 2021
bioRxiv DOI: 10.1101/2021.07.16.452622

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 ({Delta}R2=.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.

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