Predicting neural electrical activity during shock therapy

​​​​​​​A new algorithm by Fadi Karameh​ (Department of Electrical and Computer EngineeringMSFEA​) and Ziad Nahas (Department of Psychiatry, AUB; currently University of of Minnesota) has successfully mapped part of the brain’s circuitry during shock therapy. Also termed Electroconvulsive therapy (ECT), a long standing, highly effective clinical method for the treatment of severe, medication-resistant depression, with more than 2 million treatments performed annually world wide. A major side e​ffect is memory loss, a result of poor targeting.

​The algorithm used system theory to compensate for the limited information available from scalp EEG recordings to fill-in gaps when mapping sub-networks of the brain affected by the spreading seizures.

The tool furnishes the first direct (electrical) evidence of the induced seizure focality, and could thus help in designing safer and more effective tr​eatment. For brain research at large, the novel approach could help explain activity under other brain-wide phenomena such as anesthesia and sleep, using the tiny window that EEG provides into real-time brain dynamics.

​Watch the video below (produced by Research Square)

A Blind Module Identification Approach for Predicting Effective Connectivity Within Brain Dynamical Subnetworks. This research was published by Brain Topography: A Journal of Cerbral Function and Dynamics in August 2018.