Spatiotemporal Dynamics of Stress-Induced Network Reconfigurations Reflect Negative Affectivity

预测(人工智能) 消极情感 心情 心理学 焦虑 压力(语言学) 精神病理学 临床心理学 神经科学 精神科 人工智能 计算机科学 语言学 哲学
作者
Anne Kühnel,Michael Czisch,Philipp G. Sämann,NULL AUTHOR_ID,Elisabeth B. Binder,Nils B. Kroemer
出处
期刊:Biological Psychiatry [Elsevier]
卷期号:92 (2): 158-169 被引量:8
标识
DOI:10.1016/j.biopsych.2022.01.008
摘要

Background Maladaptive stress responses are important risk factors 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 psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. 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. FC but not activation trajectories predicted the stress phase (accuracy = 70%, pperm < .001) and increases in heart rate (R2 = 0.075, pperm < .001). Critically, individual spatiotemporal trajectories of changes across networks also predicted negative affectivity (ΔR2 = 0.075, pperm = .030) but not the presence or absence of a mood and anxiety disorder. Conclusions Spatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of 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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