Functional Transitions of the Default Mode Network Predict Self-Reported Resilience
Joshua Goh, Chun-Wei Hsu, Shulan Hsieh, Wan-Rue Lin, Ya-Ting Chang, Yu-Shiang Su, Cheng-Ta Yang, Yun-Hsuan Chang, Sheng-Hsiang Lin
preprint DOI: 10.21203/rs.3.rs-4131775/v1 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Topics | Default mode network |
|---|---|
| Key points | Smaller functional state transitions in the default mode network selectively predicted greater perceived self-resilience, especially among individuals reporting lower life outlook or social support. Transitions of DMN mean responses were broadly associated with generic resilience measures, but the authors note these associations were susceptible to signal confounds. |
Abstract
Abstract How the brain maintains stability in its internal states is fundamental to understanding human resilience of psychological states against external perturbations. Here, we evaluated the neurocomputational stability of the default mode network (DMN) using indices of its functional transitions based on multi-voxel states and conventional regional mean responses, and examined associations with self-reported resilience. Smaller DMN functional state transitions selectively predicted greater perceived self-resilience amidst lower life outlook or social support. Transitions of DMN mean responses were broadly associated with generic resilience measures, albeit susceptible to signal confounds. These findings form a foundational basis for making neurocomputational linkages between subjective experience and brain functional dynamics. Implications are also present for targeted interventions that fortify brain stability and mental resilience amidst environmental stressors.