Epilepsy affects 65 million people worldwide. While medications control many, over 20 million patients continue to have seizures despite maximal medical therapy. New surgical techniques, laser thermal ablation and responsive devices are exciting options for these patients, but their effectiveness is limited by our inability to accurately map which brain regions should be removed or treated with electrical stimulation. Currently, this mapping is done manually, but seizure onset patterns on intracranial EEG (IEEG) are frequently not well localized, and clinicians often disagree on seizure onset time, location, and what regions should be targeted. Finally, most patient evaluations present a number of viable options for surgery and device placement. There is currently no way to test the effects of a specific therapeutic approach- an operation or device placement- on outcome other than actually doing the procedure. A technique that could simulate these interventions and pick the best approach for individual patients would be a tremendous step forward in clinical care. In this proposal we develop and validate exciting new methods to localize epileptic networks from intracranial EEG that: (1) replace manual marking by clinicians with automated, objective tools, (2) remove the need for precipitating acute seizures during evaluation to localize them and (3) allow clinicians to simulate the effects of different brain surgeries or device placements for individual patients to select the treatment that will work best for them. This work marries new graph theoretical computational methods to model brain networks from IEEG with state of the art neuroimaging techniques to precisely localize implanted electrodes, devices and brain structure. Adult and pediatric patients undergoing brain implants during evaluation for epilepsy surgery or NeuroPace Responsive Neurostimulator (RNS) device placement will be enrolled at the Hospital of the University of Pennsylvania and Children's Hospital of Philadelphia. We will obtain high-resolution brain imaging before and after electrode implant and after surgery or device placement. Our models, recently published, will be applied to each patient's data and brain regions that drive seizures will be quantitatively identified and mapped to their brain images. Patients will undergo standard invasive therapy, either resection or device implant, and outcome- reduction in seizure frequency- will be compared to the amount of the epileptic network that is removed or stimulated by an implanted device. Finally, we will test our ?virtual resection? technique against each patient's data to predict which therapeutic intervention will be most effective, and compare this prediction to the performed procedure and patient outcome. This work differs from many computational studies in that its focus is on developing practical tools to guide invasive treatment for medication resistant epilepsy. It leverages an established collaboration between experienced clinicians in adult and pediatric epilepsy with experts in neuroimaging, bioengineering, functional neurosurgery and a MacArthur-award-winning computational neuroscientist at the University of Pennsylvania.

Public Health Relevance

Epilepsy affects 65 million people worldwide and while many benefit from medications, 20 million patients continue to have seizures despite maximal therapy. Surgery and new implanted devices can help, but their effectiveness is limited by our inability to accurately map which brain regions should be removed or treated with brain stimulation. In this proposal we develop an exciting new method to localize epileptic networks from intracranial EEG that: (1) replaces manual marking with an automated, objective method, (2) removes the need for precipitating acute seizures during evaluation to localize them and (3) allows clinicians to simulate the effects of different brain surgeries or device placements for individual patients in order to select the treatment that will work best for them.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS099348-03
Application #
9494716
Study Section
Acute Neural Injury and Epilepsy Study Section (ANIE)
Program Officer
Leenders, Miriam
Project Start
2016-09-01
Project End
2020-05-31
Budget Start
2018-06-01
Budget End
2019-05-31
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Neurology
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Medaglia, John D; Satterthwaite, Theodore D; Kelkar, Apoorva et al. (2018) Brain state expression and transitions are related to complex executive cognition in normative neurodevelopment. Neuroimage 166:293-306
Lefort-Besnard, Jérémy; Bassett, Danielle S; Smallwood, Jonathan et al. (2018) Different shades of default mode disturbance in schizophrenia: Subnodal covariance estimation in structure and function. Hum Brain Mapp 39:644-661
Betzel, Richard F; Medaglia, John D; Bassett, Danielle S (2018) Diversity of meso-scale architecture in human and non-human connectomes. Nat Commun 9:346
Braun, Urs; Schaefer, Axel; Betzel, Richard F et al. (2018) From Maps to Multi-dimensional Network Mechanisms of Mental Disorders. Neuron 97:14-31
Baum, Graham L; Roalf, David R; Cook, Philip A et al. (2018) The impact of in-scanner head motion on structural connectivity derived from diffusion MRI. Neuroimage 173:275-286
Ko, Jina; Baldassano, Steven N; Loh, Po-Ling et al. (2018) Machine learning to detect signatures of disease in liquid biopsies - a user's guide. Lab Chip 18:395-405
Muldoon, Sarah F; Costantini, Julia; Webber, W R S et al. (2018) Locally stable brain states predict suppression of epileptic activity by enhanced cognitive effort. Neuroimage Clin 18:599-607
Vitale, Flavia; Shen, Wendy; Driscoll, Nicolette et al. (2018) Biomimetic extracellular matrix coatings improve the chronic biocompatibility of microfabricated subdural microelectrode arrays. PLoS One 13:e0206137
Xia, Cedric Huchuan; Ma, Zongming; Ciric, Rastko et al. (2018) Linked dimensions of psychopathology and connectivity in functional brain networks. Nat Commun 9:3003
Mattar, Marcelo G; Wymbs, Nicholas F; Bock, Andrew S et al. (2018) Predicting future learning from baseline network architecture. Neuroimage 172:107-117

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