Preoperative Functional Connectivity Predicts Antiparkinson Drug Change after Deep Brain Stimulation

Background Deep brain stimulation (DBS) can effectively ameliorate motor symptoms in Parkinson's disease (PD), but patient outcomes remain variable. Clinical predictors lack reliability and only explain a small proportion of outcome variance, outlining a need for biomarkers that can enhance prediction accuracy. Functional magnetic resonance imaging (fMRI) could address this, offering insight into the relative impact of functional brain health on DBS outcomes. Methods Preoperative resting‐state fMRI was retrospectively collected for 120 patients with PD and DBS targeting the subthalamic nucleus or pallidum. Motor network connectivity was computed, and clinical predictors extracted including age, sex, target, hemisphere treated, and preoperative medication responsiveness. Pre‐to‐post changes in antiparkinson medications defined outcomes. Regression analysis selected important connectivity features and evaluated additional outcome variance explained by fMRI when combined with clinical predictors. Last, classification analysis assessed fMRI feature specificity to PD compared to Huntington's disease and healthy controls. Results Alongside clinical predictors, fMRI features explained more outcome variance ( R 2 adjusted = 0.36) than clinical predictors alone ( R 2 adjusted = 0.13). Five connectivity pairs bridging the cortico–basal ganglia–cerebellar network were predictive of outcomes and distinguished PD from controls and Huntington's disease with reasonable accuracy (67% and 73%). Exploratory analysis revealed that intracerebellar connectivity, although a less stable predictor of DBS outcomes, dramatically increased PD classification accuracy (≥98%). Conclusion Patient‐specific motor connectivity enhances DBS outcome prediction and could aid detection and monitoring of basal ganglia disorders. Future work will validate and extend our models for multi‐parameter MR prediction of standardized outcomes, toward the development of decision support tools for DBS.