BRAIN-DISC: a novel analytical approach integrating data-driven brain pattern discovery with cohort studies for evaluating findings in brain modulation interventions.

Brain modulation interventions (BMIs) targeting cognitive and neuropsychiatric symptoms have shown substantial heterogeneity in response, limiting their clinical utility. Resting-state functional connectivity (rsFC) may capture BMI-induced neuroplasticity and support patient stratification. However, examining these biomarkers within small, heterogeneous intervention samples remains challenging. The objective of this study is to present BRAIN-DISC, an analytic framework that links large-scale cohort-derived rsFC patterns with evaluation in targeted BMI trials. Three demonstrations were conducted. In the CogTE trial (n = 74), the Alzheimer's-resilient connectome (ARC), derived from cohort contrasts of Superagers and Alzheimer's disease, was evaluated as a response biomarker for cognitive training in mild cognitive impairment (MCI). In the BEEM trial (n = 26), a brain-derived neuropsychiatric phenotyping (BNP) subtype was used to stratify response to transcranial direct current stimulation combined with training. In a third demonstration, we conducted an end-to-end implementation by discovering rsFC biotypes jointly informed by autonomic nervous system (ANS) and cognitive function (rsFC-AC) in the MIDUS cohort (n = 208), and evaluating it as a predictive biomarker in BREATHE trial (n = 56). In CogTE, greater shifts toward the ARC pattern were associated with improvements in executive function and episodic memory. In BEEM, individuals with the affective dysregulation subtype showed greater improvement in corresponding neuropsychiatric domains. In the third demo, three rsFC-AC biotypes were discovered in MIDUS cohort; the high-ANS subtype showed greater improvement in episodic memory in BREATHE trial. BRAIN-DISC provides a scalable framework for translating cohort-derived rsFC signatures into intervention settings to support both response monitoring and patient stratification.