Revolution in the Diagnosis of Epilepsy

by Dr. Trupti Shirole on Dec 11 2014 7:16 AM

 Revolution in the Diagnosis of Epilepsy
By using computer algorithms and mathematical models, the Scientists from University of Exeter, were able to develop systems to analyze EEG recordings gathered while the patient is at rest, and reveal subtle differences in dynamic network properties that enhance susceptibility to seizures in people with idiopathic generalized epilepsy (IGE).
Professor Terry who led the research team said, "Our research offers the fascinating possibility of a revolution in diagnosis for people with epilepsy. It would move us from diagnosis based on a qualitative assessment of easily observable features, to one based on quantitative features extracted from routine clinical recordings. Not only would this remove risk to people with epilepsy, but also greatly speed up the process, since only a few minutes of resting state data would need to be collected in each case."

In their first study, researchers used EEG recordings to build a picture of how different regions of the brain were connected. They observed that in people with IGE these networks were relatively over-connected, in contrast to healthy controls. They also observed similar findings in first-degree relatives of this cohort of people with IGE; which suggests that brain network alterations are an endophenotype of the condition- an inherited and necessary, but not sufficient, condition for epilepsy to occur.

In the second study the team sought to understand how these alterations in large-scale brain networks could heighten susceptibility to recurrent seizures and thus epilepsy.  The study revealed that the level of communication between regions that would lead to their activity becoming synchronized was lower in the brain networks of people with IGE. Also, altering the activity in specific left frontal brain regions of the computational model could more easily lead to synchronous activity throughout the whole network.

In their most recent study, researchers have introduced the concept of ‘Brain Network Ictogenecity’ (BNI) as a probabilistic measure for a given network to generate seizures. They found that the brain networks of people with IGE had a high BNI, as compared to the brain networks of healthy controls who had a low BNI.

The research has been published in a recent series of papers, the most recent of which has been published online in the scientific journal ‘Frontiers in Neurology’.