Novel network analysis technology can generate pinpoint seizures in brain regions in minutes

Fresh techniques to aid seizure diagnosis and surgical planning stand to benefit hundreds of epilepsy patients, but the path to progress has been slow and challenging. New research from Carnegie Mellon University’s Bin He 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 brain regions and predict seizure outcomes.

Epilepsy affects about 70 million people around the world and more than 3.4 million Americans. Of those affected, roughly a third cannot be treated by drugs alone. For these patients, surgical removal of seizure-derived tissues or neuromodulation procedures are potential treatment avenues in order to maintain quality of life.

In current practice, prior to any surgical removal of tissues, clinicians will often drill holes into the skull to place recording electrodes on the brain. The electrodes record electrical activity in the brain over the course of days or weeks, however long it takes for the seizure (s) to materialize, to inform where the seizure (s) are taking place. While necessary, this practice can be time-consuming, costly, and uncomfortable for patients to stay in hospital for days to weeks.

An alternative to the current clinical routine has been developed by He and his collaborators and recently published Advanced ScienceGeneral Chat Chat Lounge Their novel network analysis technique can pinpoint seizures originating in brain regions and predict a significant seizure before surgery, using only 10 minutes of resting state recordings without the need to wait for seizures to occur.

In a group of 27 patients, our accuracy of localizing seizure onset brain regions was 88%, which is a fascinating result. We use machine learning and network analysis to analyze a 10-minute resting state recording forecast where the seizure will come out. While this method is still invasive, it is to a lesser degree, because we’ve taken the recording timeline from multiple days or even weeks down to 10 minutes. “

Bin He, professor of biomedical engineering, Carnegie Mellon University

He continued, “In the same group of patients, our accuracy predicting their seizure outcome, or the likelihood of seizure-free post-surgery, was 92%. Eventually, this type of data could guide patients toward or away from surgery, and it is information that is not readily available today. “

This technique extracts information flow across all recording electrodes and makes a prediction based on different levels of information flow. He and colleagues discovered that the information originating from non-seizure-generating tissue to non-seizure-producing tissue is much larger than in the inverse direction, and the notably larger difference in information flow often leads to a seizure-free outcome. Once implemented, this approach could have a major impact on informed clinicians and families if a patient should have a surgery and what would be the likelihood of surgical success.

Helping patients continue to be the driving motivation and overarching goal. By focusing on non-invasive and minimally-invasive approaches, he believes both the patient and the healthcare system can benefit.

“This research will not only provide information about the surgical success of likelihood to individuals with epilepsy and their caregivers, but it will also help us to understand the underlying mechanisms of seizures using a minimally-invasive approach,” said Vicky Whittemore, Ph.D. , Program Director, National Institute of Neurological Disorders and Stroke, a part of the National Institutes of Health.


College of Engineering, Carnegie Mellon University

Journal reference:

Jiang, H., et al. (2022) Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome. Advanced ScienceGeneral Chat Chat Lounge

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