Quantitative scalp and intracranial EEG in surgical planning

The choice of surgery to treat drug-resistant epilepsy hinges on whether a patient has a focal (well-localized to a single, relatively small brain region) or diffuse epileptic network. Patients with focal networks are candidates for destructive, and hopefully curative, brain surgery; those with diffuse networks are eligible for palliative therapy, usually with a neuromodulatory device. Determining whether a patient has a focal or diffuse network is often challenging. Clinicians use scalp EEG, imaging, and other data to estimate network focality, but if uncertainty remains, patients undergo invasive intracranial EEG (iEEG). iEEG is morbid, expensive, and often inconclusive: many patients do not receive definitive surgical therapy following iEEG, and many that do continue to have seizures postoperatively, often due to undetected diffuse networks. There is a critical need for better tools to identify diffuse networks earlier, reduce unnecessary invasive procedures, and guide patients to the most appropriate therapy. Our group developed quantitative methods to measure seizure spread and interictal abnormalities on iEEG. In pilot studies, we found that rapid seizure spread and diffuse interictal abnormalities are markers of diffuse networks. We recently showed that similar features can be extracted from scalp EEG, enabling the possibility of scalable, non-invasive assessments of network focality. We hypothesize that patients with diffuse networks exhibit rapid seizure spread and diffuse interictal abnormalities on both scalp EEG and iEEG. We further propose that combining these data modalities can improve estimates of network focality. In Aim 1, we will optimize tools to measure seizure spread and interictal abnormalities from iEEG and integrate them to predict network focality and surgical outcomes. In Aim 2, we will adapt these tools to scalp EEG. In Aim 3, we will combine scalp EEG and iEEG data to overcome gaps in spatial sampling, using a dataset with simultaneous scalp-iEEG data for benchmarking. This work leverages a multidisciplinary team with expertise in epilepsy, signal processing, and data science, and will be developed using large multi-center datasets. The resulting tools could improve clinical decision-making, reduce invasive monitoring, and inform more personalized, effective treatment pathways. Project Number: 1R01NS148413-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of Neurological Disorders and Stroke (NINDS) | Principal Investigator: Erin Conrad | Institution: UNIVERSITY OF PENNSYLVANIA, PHILADELPHIA, PA | Award Amount: $646,295 | Activity Code: R01 | Study Section: Special Emphasis Panel[ZRG1 NINC-Q (01)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11342919