Despite the high prevalence of epilepsy, which affects nearly 4% of the population over their lifetime, roughly one third of afflicted patients are incompletely responsive to anticonvulsant drugs, requiring in severe cases neurosurgical resections or novel approaches such as deep-brain stimulation (DBS). Recently, I helped develop an electrophysiology-based online DBS protocol for seizure detection and interruption in rodent models of absence epilepsy, a form of epilepsy involving aberrant thalamocortical activity. However, like other online DBS procedures which detect seizures as they occur, there was little ability to predict incipient seizures due in large part to limited spatiotemporal resolution of the signal we used, the electrocorticogram (ECoG). ECoG and related electroencephalogram (EEG), being large scale local field potential approaches, do not provide single-cell resolution of neural dynamics that are likely required to obtain predictive information. Over the last decade, the field of brain-machine interface (BMI) has made breakthroughs in neuroscience and engineering by developing methods for multi-electrode array recording of large scale neural spiking activity and efficient reduction of the resultant high-dimensional neural activity to a smaller number of dimensions to effectively control neural prostheses. We predict that this approach will be invaluable for understanding neural dynamics during seizures and stimulation, and for developing predictive algorithms and adaptive DBS protocols. Therefore, the goal of this project is to precisely characterize thalamocortical neural activity during spontaneous absence seizures and following thalamic stimulation by combining large-scale thalamic and cortical neural recordings, optogenetics, and BMI mathematical techniques. Additionally, the experiments presented in this proposal will use the recorded neural activity to develop adaptive algorithms that both predict oncoming seizure activity and modify stimulation parameters based on the neural state and response to stimulation in real-time. This proposal is aimed towards increasing our understanding of the neural dynamics of epileptic networks and improving the efficacy and safety of online DBS.

Public Health Relevance

Roughly one third of epileptic patients are resistant to pharmacological treatment, which has promoted the development of deep brain stimulation (DBS) as a means to detect and prevent seizures. However, the neural activity underlying epileptic discharges and the effects of DBS on this activity are poorly understood, partially due to the limited information carried by the EEG signals used for monitoring seizures. A more precise characterization of epileptic neural circuits is crucial to improve current DBS design and enhance the sensitivity and efficacy of seizure detection and disruption, which demands the tools and resources of basic research.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Predoctoral Individual National Research Service Award (F31)
Project #
5F31NS100265-03
Application #
9604827
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Langhals, Nick B
Project Start
2016-12-22
Project End
2019-12-21
Budget Start
2018-12-22
Budget End
2019-12-21
Support Year
3
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Stanford University
Department
Neurology
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304