Multimodal Neuroimaging-Based Machine Learning Models Leveraging Cerebral Morphometry and Glymphatic Parameters Predict Short-Term Post-Programming STN-DBS Motor Response in Parkinson's Disease.

BACKGROUND AND OBJECTIVES: Deep brain stimulation of the subthalamic nucleus (STN-DBS) is effective for medication-refractory Parkinson's disease (PD) motor symptoms, but clinical response varies across symptom domains, particularly tremor and gait. Accurate preoperative stratification is clinically important, especially for early post-programming outcomes. METHODS: We retrospectively enrolled 155 patients with PD undergoing bilateral STN-DBS and 43 healthy controls. Preoperative structural magnetic resonance imaging and diffusion-weighted imaging were used to quantify brain morphometry and glymphatic markers, including diffusion tensor imaging along the perivascular space (DTI-ALPS) and choroid plexus volume (CPV). Total motor response was evaluated in all 155 patients, tremor response in 133 patients with complete tremor subscores, and an exploratory data-driven gait-improvement phenotype in 66 patients with paired instrumented gait assessments. Machine-learning models were developed using fold-wise feature selection and hyperparameter tuning and were evaluated by fivefold cross-validation. RESULTS: Best-performing trimodal models yielded AUCs of 0.850 ± 0.045 (95% CI, 0.794-0.906) for total motor response, 0.861 ± 0.047 (95% CI, 0.803-0.919) for tremor response, and 0.970 ± 0.019 (95% CI, 0.946-0.994) for the exploratory gait-improvement phenotype. Morphometric-only models retained substantial predictive performance, with maximum AUCs of 0.830, 0.849, and 0.955 for the motor, tremor, and gait-related endpoints, respectively. For the exploratory gait phenotype, clinical-plus-morphometric and trimodal models performed similarly, suggesting limited incremental value of glymphatic variables in this subgroup. CONCLUSION: Preoperative cerebral morphometry, complemented by selected glymphatic markers and baseline clinical variables, may help stratify short-term post-programming STN-DBS response in PD. These findings support further development of imaging-informed DBS outcome prediction, while external validation and longer-term follow-up remain necessary before clinical implementation.