Computational Approach Facilitates Epilepsy Neurosurgery

They therefore conducted a study to explore whether the regions of high GC would correlate with the topography derived from ictally active electrode (IAE) sets, the method currently used to pinpoint diseased brain areas where seizures originate, which requires placing invasive grids of electrodes on the brain's surface.

The researchers therefor applied GC algorithms to interictal EEG data from 25 randomly selected patients with hard-to-treat epilepsy who underwent long-term IAE monitoring, and evaluated data from the first 20 seizure-free minutes of the patients' EEGs; the GC maps derived were then quantitatively compared to the conventionally constructed surgical plans. They found that in 16 of the 25 cases, the interictal GC maps correlated with the ictal networks constructed from IAE electrode sets. The study was published on May 2 2017, in Neurosurgery.

“We know that the diseased brain network responsible for the seizures is there all along. So rather than wait for the patient to have a seizure, we set out to find patterns of interaction between various points in the brain that might predict where seizures would eventually start,” said senior author Joseph Madsen, MD, director of epilepsy surgery at BCH. “We still need to validate and refine our approach before it can be used clinically; but we are hopeful that these advanced computer applications can help us treat more children with epilepsy - with less risk and lower cost.”

The anatomic site of seizure origin is indicated by an IAE set determined by epileptologists from ictal intracranial electroencephalography (iEEG) and communicated to the surgeon when planning resection strategy. In order to obtain ictal data, it is necessary to wait for one or more seizure during the invasive monitoring period, typically requiring at least a week. If interictal data could be mined to reveal aspects of the seizure network, which currently drives the practice of waiting for seizures, it is possible that some invasive monitoring cases could be managed in just one stage.