New techniques to help diagnose seizures and plan surgery have benefited millions of epilepsy patients, but the path to progress is slow and challenging. New research from Bin He of Carnegie Mellon University and his team, in partnership with UPMC and Harvard Medical School, introduces a novel network analysis technology that uses minimally invasive resting state electrophysiological recordings to localize seizure onset regions of the brain and predict seizure outcomes.
Epilepsy affects about 70 million people worldwide and more than 3.4 million Americans. Of those affected, nearly a third cannot be treated by medications alone. For these patients, surgical removal of the tissues of origin of the attack or neuromodulation techniques are potential treatment options to maintain quality of life.
In current practice, prior to any surgical removal of tissues, clinicians often drill holes in the skull to place recording electrodes on the surface of the brain. The electrodes record electrical activity of the brain over the course of days or weeks, no matter how long the attack (s) occur, to determine where the attack (s) occurred. While necessary, this practice can be time consuming, expensive, and uncomfortable for patients to remain in the hospital for days to weeks.
An alternative to the current clinical routine was developed by Him and his colleagues and was recently published in Advanced Science. Their new network analysis technique can focus on attacks originating in brain regions and predict the outcome of a patient’s attack before surgery, using only 10 minutes of recording. in a state of rest without having to wait for attacks to occur.
In a group of 27 patients, our accuracy in localizing the onset of attack to brain regions, was 88%, which is an interesting result. We used machine learning and network analysis to analyze the 10-minute recording of the resting state to predict where the attack would come from. While this method is still invasive, it is in a greatly reduced degree, as we take the recording timeline from several days or even weeks to 10 minutes. ”
Bin He, professor of biomedical engineering, Carnegie Mellon University
He continued, “In the same group of patients, our accuracy in predicting their outcome of an attack, or the probability of having no attack after surgery, is 92%. And this is the information that not readily available today. ”
The technique takes the information flow at all the recording electrodes and makes a prediction based on different levels of the information flow. He and colleagues discovered that the flow of information from non-seizure generating tissue to seizure originating tissue is greater than in the inverse direction, and that larger differences in information flow often lead to seizure-free. as a result. Once implemented, this procedure can have a huge impact in informing clinicians and families when a patient should proceed with an operation and what the likelihood of surgical success is.
Helping patients continues He drives enthusiasm and overall purpose. By focusing on non-invasive and minimally-invasive procedures, He believes the patient and the healthcare system can benefit.
“This research will not only provide information about the probability of surgical success in individuals with epilepsy and their caregivers, but it will also help us understand the underlying mechanism of seizures using a minimally-invasive approach. method, ”said Vicky Whittemore, Ph.D., program director, National Institute of Neurological Disorders and Stroke, a division of the National Institutes of Health.
College of Engineering, Carnegie Mellon University
Jiang, H., and so on. (2022) Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Attack Outcome. Advanced Science. doi.org/10.1002/advs.202200887.