Running therapy improves clinical symptoms and reorganizes dynamic brain networks in affective disorders.

BackgroundExercise therapy reduces depressive and anxiety symptoms, but its neural mechanisms are not fully understood. We examined whether and how running therapy reorganizes dynamic brain functional connectivity in affective disorders. MethodsAt baseline, resting-state fMRI was collected from 66 healthy controls and 50 individuals with affective disorders. Co-activation patterns analyses (CAPs) identified recurring whole-brain network states characterized by spatial patterns of regional co-activation/codeactivation patterns and their temporal occurrence rates. We compared CAPs between groups at baseline. Participants with affective disorders then received 16 weeks of running therapy or antidepressant treatment. We examined: (1) treatment-induced changes in brain CAPs and clinical symptoms, (2) brain-symptom associations at baseline versus post-treatment, and (3) associations between network reorganization and symptom improvement. ResultsAt baseline, individuals with affective disorders showed fewer occurrences of the visual-somatomotor-subcortical network state (VS-SCCAP) than controls (F=5.4, P=0.02, {superscript 2}=0.04). Running therapy significantly altered the temporal dynamics of two brain systems: the default mode (DMCAP: {beta} = -0.88, P = 0.006, d =- 0.88) and VS-SCCAP ({beta} = 0.87, P = 0.006, d = 0.85). These reorganizations were accompanied by significant improvements in depressive and anxiety symptoms (IDS: {beta} = -1.23, P < 0.001, d = -1.15; BAI: {beta} = - 0.98, P = 0.008, d = -0.93). DMCAP-symptom coupling changed significantly from baseline to post-treatment ({Delta}RHO=-0.48, Z{approx}-2.0, P<0.05). ConclusionsRunning therapy altered dynamic brain networks in association with clinical symptom improvement. These findings provide neurobiological evidence for exercise-induced therapeutic effects through transient brain-state reorganization, demonstrating the utility of dynamic connectivity approaches for characterizing neural mechanisms in affective disorders.