NIH Reporter #1R01EB038575-01A1

Personalized Epilepsy Surgery Through Virtual Resection and Multimodal Language Mapping

The award funds a project to develop advanced multimodal language mapping and a virtual resection tool to improve epilepsy surgery outcomes and reduce postoperative language deficits.

Description

This project aims to develop a multimodal presurgical language mapping framework integrating functional MRI (fMRI), magnetoencephalography (MEG), high-gamma modulation (HGM), and diffusion-weighted imaging (DWI). It evaluates combining HGM with electrical stimulation mapping (ESM) to improve mapping accuracy and reduce testing time. Machine learning support vector regression (SVR) models will predict postoperative language outcomes based on combined modalities and clinical variables. A virtual resection tool (VRT) will simulate surgical scenarios to assist clinicians in decision-making, personalize surgical plans, and improve patient counseling. The project seeks to minimize language deficits and enhance quality of life for epilepsy patients.

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