Across the world, nearly 50 million people suffer from epilepsy. For an estimated 30% of these individuals, seizures remain poorly controlled despite maximal medical management. Moreover, treatment of epilepsy through medication often results in significant - sometimes debilitating - side effects. To advance the therapeutic management of epilepsy, we must understand the physiological mechanisms which support this disease. Unraveling these mechanisms is especially difficult. Like many neural processes, the seizure involves spatial scales spanning many orders of magnitude, from the individual neuron to the entire nervous system. Moreover, typical clinical recordings provide only a limited view of the vast mechanisms underlying the seizure. In this project, an interdisciplinary research group consisting of clinicians, statisticians, and mathematicians will study the dynamical mechanisms that support epilepsy and, in doing so, suggest novel therapies to treat the disease. The research will focus on three fundamental aspects of the seizure - how it begins, how it spreads over the brain, and how it ends - observed in voltage recordings from human patients with epilepsy. These recordings will span multiple spatial scales and include activity generated by individual neurons, small neural populations, and large brain regions. To characterize these data, sophisticated analysis techniques will be applied that link the activity across spatial scales. In addition, computational models of the neural activity will be developed and constrained using the multiscale data. These models will then be applied to suggest the mechanisms that support multiscale interactions during seizure and to propose novel therapies to treat epilepsy. Completion of the proposed research will represent a significant step forward toward a deeper and more complete understanding of epileptic physiology, and toward a system for exploring and testing innovative methods for seizure control.

Public Health Relevance

PROJECT NARRATIVE Epilepsy is a devastating and poorly understood illness that evolves on multiple spatial scales. During a seizure, multiscale interactions are prominent yet the mechanisms that support these interactions remain unknown. To address (and eventually treat) these mechanisms, an interdisciplinary team of researchers will record, analyze and model multiscale voltage data from human patients with epilepsy.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BST-E (90))
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Stewart, Randall R
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Boston University
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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Naftulin, Jason S; Ahmed, Omar J; Piantoni, Giovanni et al. (2018) Ictal and preictal power changes outside of the seizure focus correlate with seizure generalization. Epilepsia 59:1398-1409
Martinet, L-E; Fiddyment, G; Madsen, J R et al. (2017) Human seizures couple across spatial scales through travelling wave dynamics. Nat Commun 8:14896
Chu, Catherine J; Chan, Arthur; Song, Dan et al. (2017) A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram. J Neurosci Methods 277:46-55
Voytek, Bradley; Kramer, Mark A; Case, John et al. (2015) Age-Related Changes in 1/f Neural Electrophysiological Noise. J Neurosci 35:13257-65
Martinet, Louis-Emmanuel; Ahmed, Omar J; Lepage, Kyle Q et al. (2015) Slow Spatial Recruitment of Neocortex during Secondarily Generalized Seizures and Its Relation to Surgical Outcome. J Neurosci 35:9477-90
González-Ramírez, Laura R; Ahmed, Omar J; Cash, Sydney S et al. (2015) A biologically constrained, mathematical model of cortical wave propagation preceding seizure termination. PLoS Comput Biol 11:e1004065
Aoi, Mikio C; Lepage, Kyle Q; Kramer, Mark A et al. (2015) Rate-adjusted spike-LFP coherence comparisons from spike-train statistics. J Neurosci Methods 240:141-53
Meng, Liang; Kramer, Mark A; Middleton, Steven J et al. (2014) A unified approach to linking experimental, statistical and computational analysis of spike train data. PLoS One 9:e85269
Kopell, Nancy J; Gritton, Howard J; Whittington, Miles A et al. (2014) Beyond the connectome: the dynome. Neuron 83:1319-28
Gerhard, Felipe; Kispersky, Tilman; Gutierrez, Gabrielle J et al. (2013) Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone. PLoS Comput Biol 9:e1003138

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