Evaluation of DBS computational modeling methodologies using in-vivo electrophysiology in Parkinson's disease

Optimizing deep brain stimulation (DBS) parameter settings requires postoperative adjustments through a time-consuming trial-and-error process. As such, researchers have been developing computational models to guide DBS programming. Despite growing interest in image-guided DBS technology, and recent adoption into clinical practice, the direct validation of the prediction accuracy remains limited.

The objective of this study was to establish a comparative framework for validating the accuracy of various DBS computational modeling methodologies in predicting the activation of clinically relevant pathways using in vivo measurements from PD patients undergoing subthalamic (STN) DBS surgery.

In this study, we compared the accuracy of six computational modeling variations for predicting the activation of the corticospinal/bulbar tract (CSBT) and cortico-subthalamic hyperdirect pathway (HDP) using very short- (<2 ms) and short-latency (2-4 ms) cortical evoked potentials (cEPs). We constructed the variations using three key factors: modeling method (Driving Force [DF] vs. Volume of Tissue Activated [VTA]), imaging space (native vs. normative), and anatomical representation (pathway vs. structure). The model performances were quantified using the coefficient of determination (R²) between the cEP amplitudes and percent pathway or structure activation.

We compared model accuracy for 11 PD patients. The DF-Native-Pathway model was the most accurate method for quantitatively predicting experimental subcortical pathway activations. Additionally, our analysis showed that using normative brain space significantly diminished the accuracy of model predictions.

The choice of methodology should depend on the specific application and the required level of precision for the intended analysis. However, model parameters should be optimized to accurately predict known experimental activation measures.