Wednesday, April 2, 2025
4/2/2025
Development of multimodal network analyses to improve epilepsy surgery outcomes - PROJECT SUMMARY/ABSTRACT In individuals with drug-resistant focal epilepsy, epilepsy surgery can often reduce or eliminate seizures by resection or ablation of regions responsible for seizure generation, or epileptogenic zones (EZs). Successful epilepsy surgery depends critically on accurate and complete localization of these EZs. Current clinical strategies for EZ localization include noninvasive presurgical evaluation, plus invasive intracranial monitoring with stereo- SEEG (SEEG) in over 50% of cases. However, 33-50% of patients continue to suffer from disabling seizures after surgery, as traditional presurgical testing does not consider the connectivity of presumed EZs to other brain regions. We hypothesize that true EZs are network nodes with abnormal and identifiable connectivity patterns, and brain network connectivity measurements may supplement standard clinical testing to guide EZ localization. Here we propose innovative, multimodal studies incorporating structural and functional MRI connectivity measures, resting-state and seizure (ictal) SEEG recordings, and electrically-stimulated cortico-cortical evoked potentials (CCEPs) to help guide surgical decisions and localize “true” EZs using network connectivity. In Aim 1, we will develop a novel supervised machine-learning approach using whole-brain MRI structural connectivity to identify epilepsy subtypes and predict surgical outcomes. These measures may guide initial surgical decisions, and help patients avoid intracranial monitoring when it is not necessary. In Aim 2, we will use a combination of resting-state and ictal SEEG as well as CCEPs to define connectivity fingerprints of true EZs, which we hypothesize will demonstrate increased inward (inhibitory) connectivity at rest but increased outward (excitatory) connectivity at seizure onset (the Interictal Suppression Hypothesis). Using these measures, we will create a combined SEEG connectivity model to supplement traditional ictal interpretation and improve EZ localization. In Aim 3, we will relate SEEG and functional MRI connectivity measures to each other using penalized regression, aligning brain states during both modalities using simultaneous scalp EEG. This will allow us to identify noninvasive functional MRI network measures for EZ localization that are validated by SEEG in the same patients. Our multimodal network approaches will use both electrophysiology and neuroimaging connectivity measures, combining invasive and noninvasive techniques, to ultimately aid accurate EZ localization and guide surgical decisions. Overall, our goal is to develop novel and innovative network measures that can be applied broadly using existing hardware at surgical centers to improve patient care in drug-resistant focal epilepsy.
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