Prescribing model for the most effective contacts of implanted brain electrodes in the subthalamic nucleus in patients with Parkinson’s disease

Abstract

Introduction. This project focuses on optimizing the initial programming process of electrodes implanted in the subthalamic nucleus for deep brain stimulation in patients with Parkinson's disease. Traditionally, this programming requires extensive testing ("contact testing") to determine which electrode contact(s) generate the best response without adverse effects.
Project Objective: To develop a predictive system based on intraoperative multi-unit micro-recording data and postoperative images to select the most effective electrode contact, thereby reducing programming time and improving patient quality of life.
Method: System Components: Use of fused pre- and postoperative CT and MRI images for stereotactic planning. Intraoperative multi-unit micro-recordings for precise localization of the subthalamic nucleus. Data analysis with visualization tools (heat maps, 3D reconstruction) and machine learning algorithms (supervised and unsupervised). Expected Benefits: Reduction in consultation time for programming; more accurate and faster selection of effective contacts; rapid improvement in the control of symptoms such as rigidity, tremor, and bradykinesia; and optimization of institutional resources. Model Evaluation: A confusion matrix will be used to validate the system's accuracy against the traditional method, and a comparative follow-up will be conducted between the model's prediction and the neurologist's assessment. Implementation: The model was implemented in the first patient in April 2024.
Conclusion: We believe that the following project could lead to a practical and extremely useful tool in the field of brain neuromodulation for the treatment of Parkinson's disease. With this model, we aim to expeditiously and accurately select the most effective contacts for the implanted electrodes and thus obtain optimal therapeutic results early.