Epilepsy affects approximately 70 million people worldwide. About 30% of epilepsy patients are drug resistant and must consider invasive alternatives such as resective surgery, and electrical stimulation therapy. Surgical candidates must have a well-localized focus in an area outside of eloquent brain structures. Although surgery can dramatically improve the lives of patients, it is irreversible and outcomes are highly variable (30-70% success rates). Electrical stimulation, on the other hand, is reversible and has great potential. Chronic open-loop stimulation has shown some efficacy, but does not account for dynamic brain activity and the continuously changing state of the patient, making it suboptimal and crude. To maximize therapeutic effects, new methods must be developed for fine dynamic tuning of stimulation parameters in a patient-specific manner. Closed-loop therapy provides an attractive option that minimizes intervention by limiting the delivery of therapy to times when the patient is in need. Efforts have been made to develop ?closed-loop? stimulation strategies using different protocols, yet none provide a highly effective and reliable solution. All closed-loop strategies proposed and studied are actually ?responsive switches? and haven?t produced reliable results that translate to the clinic. These strategies wait until a seizure is detected (via a detection algorithm) and then stimulate with a fixed pattern to suppress the seizure. In contrast, we will implement real closed-loop control that continuously steers the neural network away from seizure genesis entirely using adaptive stimulation patterns that change with EEG measurements - avoiding seizure detection and seizures altogether. To meet this objective, we plan to use in vivo experimental data to develop an innovative mathematical model that characterizes fundamental neural dynamics during seizure genesis, and the effects of different electrical stimuli on neural activity leading to seizure genesis. Based on this model, we will then design and implement a feedback controller that monitors neural activity in real-time to prevent seizures from evolving in the network. In particular, the controller will steer temporal patterns of stimulation to disrupt pre-seizure activity with minimal energy consumption. To accomplish our goals, we have assembled a highly interdisciplinary team with expertise in system identification, control, and experimental neurophysiology.

Public Health Relevance

About 30% of the 60 million epilepsy patients are drug resistant and must consider invasive alternatives such as electrical stimulation therapy. Chronic open-loop stimulation has shown some efficacy, but does not account for dynamic brain activity and the continuously changing state of the patient, making it suboptimal and crude. To maximize therapeutic effects, we will design and implement a closed-loop stimulation strategy in animal models that monitors neural activity in real-time to prevent seizures from evolving in the brain network altogether.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21NS103113-01A1
Application #
9527978
Study Section
Bioengineering of Neuroscience, Vision and Low Vision Technologies Study Section (BNVT)
Program Officer
Klein, Brian
Project Start
2018-04-01
Project End
2020-03-31
Budget Start
2018-04-01
Budget End
2019-03-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Biostatistics & Other Math Sci
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205