Connectivity-based selection of optimal deep brain stimulation contacts: A feasibility study

Abstract

Background: The selection of optimal deep brain stimulation (DBS) parameters is time-consuming, experience-dependent, and best suited when acute effects of stimulation can be observed (e.g., tremor reduction). Objectives: To test the hypothesis that optimal stimulation location can be estimated based on the cortical connections of DBS contacts. Methods: We analyzed a cohort of 38 patients with Parkinson's disease (24 training, and 14 test cohort). Using whole-brain probabilistic tractography, we first mapped the cortical regions associated with stimulation-induced efficacy (rigidity, bradykinesia, and tremor improvement) and side effects (paresthesia, motor contractions, and visual disturbances). We then trained a support vector machine classifier to categorize DBS contacts into efficacious, defined by a therapeutic window ≥2 V (threshold for side effect minus threshold for efficacy), based on their connections with cortical regions associated with efficacy versus side effects. The connectivity-based classifications were then compared with actual stimulation contacts using receiver-operating characteristics (ROC) curves. Results: Unique cortical clusters were associated with stimulation-induced efficacy and side effects. In the training dataset, 42 of the 47 stimulation contacts were accurately classified as efficacious, with a therapeutic window of ≥3 V in 31 (66%) and between 2 and 2.9 V in 11 (24%) electrodes. This connectivity-based estimation was successfully replicated in the test cohort with similar accuracy (area under ROC = 0.83). Conclusions: Cortical connections can predict the efficacy of DBS contacts and potentially facilitate DBS programming. The clinical utility of this paradigm in optimizing DBS outcomes should be prospectively tested, especially for directional electrodes.