Functional Transitions of the Default Mode Network Predict Self-Reported Resilience

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.