Whole-brain functional connectivity (FC) robustly predicts multidimensional treatment outcomes in patients with internalizing psychopathologies (IPs) across diagnoses and therapies.
Predictive neural signatures prominently involve the default mode network (DMN) and dorsal/ventral attention networks, supporting their transdiagnostic relevance.
Machine learning models leveraging pretreatment FC generalize across cognitive-behavioral therapy, SSRIs, and supportive therapy, underscoring FC’s utility in precision psychiatry.
Integrative approaches combining multi-dimensional clinical outcomes and whole-brain FC yield superior predictive performance compared to reduced models.
