Deep learning on brief interictal intracranial recordings can accurately characterize seizure onset zones
This work serves as evidence that deep learning on brief interictal intracranial data can classify seizure onset zones across the brain. Furthermore, our findings corroborate current understandings of interictal epileptiform discharges and may help uncover novel interictal morphologies. Clinical application of our models may reduce dependence on recorded seizures for localization and shorten presurgical evaluation time for drug-resistant epilepsy patients, reducing patient morbidity and hospital costs.
Authors
- Anas Reda
- Graham W. Johnson (ORCID: https://orcid.org/0000-0002-9154-4315)
- Baxter P. Rogers (ORCID: https://orcid.org/0000-0002-5666-2797)
- Dario J. Englot (ORCID: https://orcid.org/0000-0001-8373-690X)
- Sameer Sundrani (ORCID: https://orcid.org/0000-0002-1939-0462)
- Shawniqua Williams Roberson (ORCID: https://orcid.org/0000-0003-1331-380X)
- Victoria L. Morgan (ORCID: https://orcid.org/0000-0001-8263-2324)
- Derek J. Doss (ORCID: https://orcid.org/0000-0002-2685-0762)
- Ghassan S. Makhoul (ORCID: https://orcid.org/0009-0002-9281-541X)
- Sarah K. Bick (ORCID: https://orcid.org/0000-0003-0753-1720)
- Emily Liao
- Addison C. Cavender
- Bruno Hidalgo Monroy Lerma (ORCID: https://orcid.org/0009-0000-7307-4762)
Institutions
- Mayo Clinic (US)
- Vanderbilt University (US)
- Neurological Surgery (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Epilepsia
- Published
- 2025-05-27
- DOI
- https://doi.org/10.1111/epi.18478
- Primary Topic
- Epilepsy research and treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institute of General Medical Sciences
- National Institute of Neurological Disorders and Stroke
- National Institute of Biomedical Imaging and Bioengineering