Our goal is to refine existing EEG biomarkers, such as high-frequency oscillations (HFOs), and identify new spatial biomarkers of the epileptic brain using a data-driven AI approach. Ultimately, we aim to leverage these biomarkers to guide epilepsy surgery and improve post-surgical outcomes in children with drug-resistant epilepsy.
1. Zhang Y, Daida A, Liu L, Kuroda N, Ding Y, Oana S, Monsoor T, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Sankar R, Staba RJ, Engel J Jr, Asano E, Roychowdhury V, and Nariai H. “Discovering Neurophysiological Characteristics of Pathological High-Frequency Oscillations in Epilepsy with an Explainable Deep Generative Model” medRxiv [Preprint]. 2024 Jul 11:2024.07.10.24310189. doi: 10.1101/2024.07.10.24310189. PMID: 39040207
2. Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, and Nariai H. “Optimizing Detection and Deep Learning-based Classification of Pathological High-Frequency Oscillations in Epilepsy” Clin Neurophysiol. 2023 Oct;154:129-140. doi: 10.1016/j.clinph.2023.07.012. Epub 2023 Aug 9. PMID: 37603979
3. Zhang Y, Chung H, Ngo JP, Monsoor T, Hussain SA, Matsumoto JH, Walshaw PD, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J, Speier W, Roychowdhury V and Nariai H. "Characterizing physiological high-frequency oscillations using deep learning" J Neural Eng. 2022. (19)066027. DOI 10.1088/1741-2552/aca4fa. PMID: 36541546
4. Zhang Y, Lu Q, Monsoor T, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J, Speier W, Roychowdhury V, and Nariai H. “Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach.” Brain Commun. 2021 Nov 3;4(1):fcab267. doi: 10.1093/braincomms/fcab267. eCollection 2022. PMID: 35169696
We aim to explore and validate non-invasive EEG biomarkers in children with epilepsy. Our analytical approach includes artifact rejection, quantitative EEG analysis—such as phase-amplitude coupling and high-frequency oscillations (HFOs)—as well as traditional qualitative expert-based EEG evaluation. Our goal is to utilize these biomarkers for diagnosis, monitoring disease activity and treatment effects, and predicting clinical outcomes.
1. Daida A, Oana S, Nadkarni D, Espiritu BL, Edmonds BD, Stanecki C, Samuel AS, Rao LM, Rajaraman RR, Hussain SA, Matsumoto JH, Sankar R, Hannauer PS, and Nariai H. 2023. "Overnight Electroencephalogram to Forecast Epilepsy Development in Children with Autism Spectrum Disorders." J Pediatr. 2024 Jul 27;274:114217. doi: 10.1016/j.jpeds.2024.114217. Online ahead of print. PMID: 39074735.
2. Lu M, Zhang Y, Diada A, Oana S, Rajaraman RR, Nariai H, Roychowdhury V, and Hussain SA. 2023. "Application of an EEG-based deep learning model to discriminate children with epileptic spasms from normal controls" medRxiv: 2023.06.30.23292096.
3. Mazumder R, Lagoro DK, Nariai H, Danieli A, Eliashiv D, Engel J Jr, Dalla Bernardina B, Kegele J, Lerche H, Sejvar J, Matuja W, Schmutzhard E, Bonanni P, De Polo G, Wagner T, Winkler AS. “Ictal Electroencephalographic Characteristics of Nodding Syndrome: A Comparative Case-Series from South Sudan, Tanzania, and Uganda” Ann Neurol. 2022 Apr 19. doi: 10.1002/ana.26377. PMID: 3543820
4. Miyakoshi M, Nariai H, Rajaraman RR, Bernardo D, Shrey DW, Lopour B, Sim MS, Staba RJ, and Hussain SA. “An automated pipeline for preprocessing scalp-recorded awake EEG data for phase-amplitude coupling analysis: application to infantile spasms.” Epilepsy Res. 2021 Dec;178:106809. doi: 10.1016/j.eplepsyres.2021.106809. Epub 2021 Nov 7. PMID: 34823159
Growing evidence suggests that epilepsy is a network disorder, a concept particularly relevant to epilepsy surgery. Accurate mapping of the epileptic network, both at the cortical and cortico-subcortical levels, is essential for guiding effective treatment. We employ connectivity analysis, including coherence and Granger causality, to understand these networks. Based on this mechanistic insight, our goal is to optimize network-based neuromodulation treatments for pediatric epilepsy.
1. Ahn S, Edmonds B, Rajaraman RR, Rao LM, Hussain SA, Matsumoto JH, Sankar R, Salamon N, Fallah A, and Nariai H. “Bilateral centromedian nucleus of thalamus responsive neurostimulation for pediatric-onset drug-resistant epilepsy.”Epilepsia. 2024 Aug;65(8):e131-e140. doi: 10.1111/epi.18031. Epub 2024 Jun 7. PMID: 38845459
2. Panchavati S, Daida A, Edmonds B, Miyakoshi M, Oana S, Ahn SS, Arnold C, Salamon N, Sankar R, Fallah A, Speier W, Nariai H. “Uncovering spatiotemporal dynamics of the corticothalamic network at ictal onset.”Epilepsia. 2024 Jul;65(7):1989-2003. doi: 10.1111/epi.17990. Epub 2024 Apr 25. PMID: 38662128
3. Nagahama Y, Zervos T, Murata K, Holman L, Karsonovich T, Parker JJ, Chen JS, Phillips W, Fajardo M, Nariai H, Hussain S, Porter B, Grant GA, Ragheb J, Wang S, O’Neill BR, Alexander A, Bollo RJ, Fallah A. “Real-world preliminary experience with responsive neurostimulation in pediatric epilepsy: a multi-center retrospective observational study” Neurosurgery. 2021 Nov 18;89(6):997-1004. PMID: 34528103
4. Edmonds B, Miyakoshi M, Remore LG, Ahn S, Phillips W, Daida A, Salmon N, Bari A, Sankar R, Matsumoto JH, Fallah A, and Nariai H. “Characteristics of ictal thalamic EEG in pediatric-onset neocortical focal epilepsy” medRxiv. 2023 Jun29:2023.06.22.23291714. doi: 10.1101/2023.06.22.23291714. Preprint. PMID: 37425697
