The care of Veterans with epilepsy is a priority for the Department of Veterans Affairs as attested by the creation of several Epilepsy Centers of Excellence (ECoE) across the country. The constant but seemingly unpredictable re-occurrence of seizures dramatically impacts the autonomy and quality of life of Veterans and civilians suffering from epilepsy. Unfortunately, 25-35% of patients with epilepsy continue to have seizures despite maximal medical therapy (Annegers 2001). Regional surgical resection, besides carrying significant risk, is curative in only ~ 40-70% of cases (Engel et al., 2003). Even when pharmacological or surgical methods work, side effects can be severe. Alternative seizure prevention and warning systems would be highly desirable to restore quality of life and autonomy of Veterans and civilians with intractable epilepsy. Seizure prevention and warning systems based on intracranial EEG (iEEG) recordings have been intensively examined in the past 30 years, yet reliable seizure prediction and early detection remain elusive. Overall, progress has been hampered by the difficulty of monitoring the activity of ensembles of single neurons in humans. We will use a novel technology, intracortical 96-microelectrode arrays (MEAs), to examine human epilepsy at a much higher spatial resolution than in iEEG recordings. We will record the activity of ensembles of single neurons (single units, SUs), and multichannel multi-unit (MU) and high-density field potentials (LFPs) in patients with pharmacologically intractable focal epilepsy undergoing pre-resection surgery monitoring.
AIM 1 will study how seizures start, spread and terminate at multiple scales, from the microphysiological level of ensembles of SUs, MUs and LFPs to the macroscopic dynamics reflected in iEEGs signals. We will test the hypothesis that neuronal ensemble dynamics preceding seizure onset change gradually and that these changes can be detected several minutes before the seizure onset. We further hypothesize that this transition should manifest in changes in spatiotemporal correlations among SUs, MUs and LFPs.
AIM 2 a will develop and test a new framework for seizure prediction and early warning based on multi-scale MEA neural signals in people with focal epilepsy.
AIM 2 b will compare this new framework for seizure prediction with state of the art prediction approaches based exclusively on iEEG. This comparison will reveal the advantages and disadvantages of seizure prediction approaches that include MEA neural signals versus approaches that do not.

Public Health Relevance

Based on a novel recording technology that allows an unprecedented level of detail, this project will provide the basis for new therapies by furthering our understanding of the microphysiology and mechanisms of epilepsy. This project will develop and test the feasibility of seizure prediction systems aimed at restoring the autonomy and quality of life of Veterans and civilians suffering from epilepsy. We believe this novel technology and approach will initiate a paradigm shift in the science and treatment of one of the most common and devastating neurological disorders. PUBLIC HEALTH RELEVANCE: The studies in this project will develop a framework for seizure prediction and early detection to be used in prevention of human focal epileptic seizures. The long term aim of this seizure prediction framework is the restoration of quality of life and autonomy of Veterans and civilians suffering from epilepsy. Furthermore, based on novel recording technologies that allow unprecedented level of detail, the proposed studies will provide the basis for new therapies by furthering our understanding of the microphysiology and mechanisms of human epilepsy.

Agency
National Institute of Health (NIH)
Institute
Veterans Affairs (VA)
Type
Non-HHS Research Projects (I01)
Project #
5I01RX000668-04
Application #
9268451
Study Section
Brain Injury: TBI & Stroke (RRD1)
Project Start
2014-04-01
Project End
2019-03-31
Budget Start
2017-04-01
Budget End
2018-03-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Providence VA Medical Center
Department
Type
DUNS #
182465745
City
Providence
State
RI
Country
United States
Zip Code
02908
Heitmann, Stewart; Rule, Michael; Truccolo, Wilson et al. (2017) Optogenetic Stimulation Shifts the Excitability of Cerebral Cortex from Type I to Type II: Oscillation Onset and Wave Propagation. PLoS Comput Biol 13:e1005349
Sarma, Anish A; Crocker, Britni; Cash, Sydney S et al. (2016) A modular, closed-loop platform for intracranial stimulation in people with neurological disorders. Conf Proc IEEE Eng Med Biol Soc 2016:3139-3142
Y Ho, E C; Truccolo, Wilson (2016) Interaction between synaptic inhibition and glial-potassium dynamics leads to diverse seizure transition modes in biophysical models of human focal seizures. J Comput Neurosci 41:225-44
Aghagolzadeh, Mehdi; Hochberg, Leigh R; Cash, Sydney S et al. (2016) Predicting seizures from local field potentials recorded via intracortical microelectrode arrays. Conf Proc IEEE Eng Med Biol Soc 2016:6353-6356
Wagner, Fabien B; Eskandar, Emad N; Cosgrove, G Rees et al. (2015) Microscale spatiotemporal dynamics during neocortical propagation of human focal seizures. Neuroimage 122:114-30
Lu, Yao; Truccolo, Wilson; Wagner, Fabien B et al. (2015) Optogenetically induced spatiotemporal gamma oscillations and neuronal spiking activity in primate motor cortex. J Neurophysiol 113:3574-87
Wagner, Fabien B; Truccolo, Wilson; Wang, Jing et al. (2015) Spatiotemporal dynamics of optogenetically induced and spontaneous seizure transitions in primary generalized epilepsy. J Neurophysiol 113:2321-41
Harrison, Matthew T; Amarasingham, Asohan; Truccolo, Wilson (2015) Spatiotemporal conditional inference and hypothesis tests for neural ensemble spiking precision. Neural Comput 27:104-50
Truccolo, Wilson; Ahmed, Omar J; Harrison, Matthew T et al. (2014) Neuronal ensemble synchrony during human focal seizures. J Neurosci 34:9927-44