Virtual Responsive Neurostimulation Implantation: From Intracranial Connectivity to Optimized Lead Placement

Area of Science:

• Neuroscience
• Epileptology
• Medical Devices

Background:

• Responsive neurostimulation (RNS) treats drug-resistant focal epilepsy, but outcomes vary significantly.
• Current methods lack a framework to predict RNS efficacy or optimize electrode placement.
• Variability in RNS response may relate to lead placement within specific brain network connectivity patterns.

Purpose of the Study:

• To develop and validate a method predicting individual RNS response based on pre-implantation functional connectivity.
• To create patient-specific 3D maps of predicted RNS efficacy to guide surgical planning.
• To investigate the relationship between interictal functional connectivity and RNS treatment outcomes.

Main Methods:

• Retrospective study of 49 patients with drug-resistant focal epilepsy undergoing RNS implantation across three centers.
• Development of a composite functional connectivity score using interictal intracranial EEG (iEEG) data.
• Creation of patient-specific 3D efficacy maps and validation of prediction accuracy with distance-based correction.

Main Results:

• The functional connectivity score achieved 72% group-level and 61% individual-level prediction accuracy.
• Optimized lead placement near iEEG electrodes yielded 100% prediction accuracy.
• Validation cohort showed 68% accuracy, with responders having significantly higher spatial scores.
• Actual RNS lead placement in responders correlated with predicted favorable network regions.

Conclusions:

• Interictal iEEG functional connectivity is a reliable predictor of individual RNS response in epilepsy.
• Patient-specific 3D efficacy maps derived from connectivity data can guide RNS lead implantation.
• This network-informed approach offers a promising strategy for optimizing RNS surgical planning and patient outcomes.