Probabilistic Subthalamic Nucleus Stimulation Sweet Spot Integration Into a Commercial Deep Brain Stimulation...

Area of Science:

• Neurosurgery
• Neurology
• Biomedical Engineering

Background:

• Deep brain stimulation (DBS) programming for Parkinson disease (PD) using segmented leads can be challenging.
• Identifying optimal stimulation parameters is crucial for effective treatment and minimizing side effects.

Purpose of the Study:

• To evaluate the predictive value of integrating a probabilistic subthalamic nucleus (STN) sweet spot into commercial software for clinically effective DBS programming.
• To assess the accuracy of image-based predictions against clinical outcomes in PD patients.

Main Methods:

• 14 PD patients with bilateral STN DBS using segmented leads were included.
• A probabilistic STN sweet spot was co-registered with patient-specific STN segmentation, lead reconstruction, and corticospinal tract (CST) tractography.
• Contacts were ranked, and stimulation effect/side-effect thresholds were predicted based on the overlap of the volume of activated tissue (VTA) with the sweet spot and CST.

Main Results:

• Image-based contact prediction demonstrated high interrater reliability (Cohen kappa 0.851-0.91).
• The image-based and clinical ranking of stimulation level and direction matched in 72% and 65% of cases, respectively.
• The median difference between predicted and observed side-effect thresholds was -0.5 mA (p < 0.001).

Conclusions:

• Integrating a probabilistic STN sweet spot into surgical software shows potential for predicting optimal DBS contact(s) and settings for PD.
• This image-based programming approach may optimize DBS with segmented leads.
• Further validation on larger datasets and prospective multicenter studies are warranted.