Functional Connectivity and Volumetrics Improve Outcome Prediction for Deep Brain Stimulation in Parkinson's Disease.
Functional Connectivity and Volumetrics Improve Outcome Prediction for Deep Brain Stimulation in Parkinson's Disease.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Chapel Hill, US · Author affiliation
Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.Location evidence
US · Author affiliation · country only
Department of Neurology, Washington University in St Louis, St Louis, MO, USA.Location evidence
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Original abstract
BackgroundDeep brain stimulation (DBS) targeting the subthalamic nucleus (STN) can effectively treat motor symptoms of Parkinson's disease (PD). However, optimal patient selection remains challenging due to the inadequacy of outcome predictors. Most clinicians rely on levodopa response to predict DBS motor outcomes, yet previous studies have identified other MRI-based predictors including resting-state functional connectivity (FC) and volumetric measures.ObjectivesTo compare the predictive value of functional and volumetric MRI data with levodopa response alone for motor outcomes in STN DBS for PD.MethodsWe analyzed 65 participants who underwent STN DBS for PD at Washington University in St. Louis. We used relaxed LASSO regression to select factors including clinical, volumetric, and FC measures to generate predictive models for relative changes in motor scores post-DBS and assessed cross-validated performance. We then compared the relative influence of the predictive factors with standardized regression coefficients.ResultsAddition of MRI predictors (FC and volumetric) improved model fit and cross-validated model performance over levodopa response alone (levodopa alone: R2 = 0.191, RMSE 13.6; MRI + levodopa: R2 = 0.31, RMSE 12.6). Within the optimized model, aggregated FC and levodopa response exhibited the highest influence on motor outcome prediction.ConclusionsIncluding MRI-based predictors significantly enhances the prediction of motor outcomes in STN DBS compared to levodopa response alone, with FC predictors demonstrating the greatest influence in the optimized model. External validation studies are necessary to confirm the clinical utility of these predictors in routine practice.