New technologies to aid in seizure diagnosis and surgical planning will benefit millions of epilepsy patients, but the path to progress has been slow and difficult. New research from Carnegie Mellon University, Ben He and his team, in partnership with UPMC and Harvard Medical School, presents a new network analysis technique that uses minimally invasive resting-state electrophysiological recordings to identify areas of seizure onset and predict seizure outcomes.
Epilepsy affects about 70 million people worldwide and more than 3.4 million Americans. Of those infected, nearly a third cannot be treated with drugs alone. For these patients, surgical removal of tissue arising from seizures or neuromodulatory procedures are potential treatment modalities in order to preserve quality of life.
In current practice, prior to any surgical removal of tissue, physicians often drill holes in the skull to place recording electrodes over the brain. The electrodes record electrical activity in the brain over days or weeks, despite the time it takes for the seizure(s) to materialize, to report where the seizure(s) occurred. While this practice is necessary, it can be time consuming, expensive, and inconvenient for patients to stay in the hospital for days to weeks.
An alternative to the current clinical routine was developed by Hu and his collaborators and was recently published in advanced sciences. Their new network analysis technology can identify seizures originating from regions of the brain and predict the outcome of a patient’s seizure before surgery, using only 10-minute resting-state recordings without having to wait for seizures to occur.
In a group of 27 patients, our accuracy in identifying areas of attack onset was 88%, an impressive result. We use machine learning and network analysis to analyze the 10-minute resting-state recording to predict where the seizure will occur. While this method is still invasive, it has come down significantly, because we have taken the recording schedule from several days or even weeks down to 10 minutes.”
Ben He, Professor of Biomedical Engineering, Carnegie Mellon University
He continued, “In the same group of patients, our accuracy in predicting the outcome of their seizures, or the possibility of them becoming seizure-free after surgery, was 92%. Ultimately, this type of data can direct patients toward or away from surgery, information that is not available easily today.”
The technology extracts the flow of information through all poles of the recording and makes predictions based on the different levels of the information flow. He and colleagues discovered that the flow of information from non-seizure-generating tissues to seizure-free ones is much greater than in the reverse direction, and a larger difference in information flow often leads to a seizure-free outcome. Once implemented, this approach can have a significant impact on informing clinicians and families if a patient should proceed with surgery and what the likelihood of success of the surgery is.
Helping patients continues to be a motivator that is the motive and the overarching goal. By focusing on non-invasive and minimally invasive methods, he believes it can benefit both the patient and the healthcare system.
“This research will not only provide information about the potential for surgical success of individuals with epilepsy and their caregivers, but will also help us understand the mechanisms behind seizures using a minimally invasive approach,” said Vicki Whitmore, Ph.D. . , program director, National Institute of Neurological Disorders and Stroke, part of the National Institutes of Health.
source:
College of Engineering, Carnegie Mellon University
Journal reference:
Jiang, H.; et al. (2022) Resting-state SEEG communication between nicks localizes the seizure onset region and predicts seizure outcome. advanced sciences. doi.org/10.1002/advs.202200887.
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