Personalized surgical procedures for epilepsy could be one step closer following the development of a computer model that is able to pinpoint the area within an individual’s brain that is responsible for epileptic seizures. The study, believed to be the first to combine computational modeling of brain dynamics with patient-specific MRI data, appeared recently in PLOS Computational Biology.
The research team based at Newcastle University (UK) combined brain scans from patients with temporal lobe epilepsy with computer modeling techniques in order to observe the brain as an example of a computer network.
Anticonvulsant drugs are currently the mainstay of therapy for epilepsy, but these are not 100 % effective. Patients who do not respond to these agents sometimes undergo surgical removal of the area of the brain that is causing the seizures, as determined by EEG. Approximately 30 % of these procedures also do not result in cessation of seizures.
Using their model, the investigators simulated such surgeries by disconnecting sections of the network that corresponded to the areas of the brain that are most frequently removed. Running these simulations for individual patients, i.e., removing the most seizure-prone areas of the brain for each individual, they demonstrated that patient-specific surgery promoted a significant improvement compared with removal of the regions routinely taken out.
“This research may help to explain why surgery is so often unsuccessful, as this work predicts that the areas most commonly removed in surgery are not always involved in starting and spreading seizures,” commented Peter Taylor (Newcastle University) , co-lead of the investigation. “It also takes us a step further towards rectifying the problem, as identifying the most seizure prone areas on an individual basis has the potential to show when the usual surgery procedures may not work for a patient.”
Marcus Kaiser, Professor of Neuroinformatics at Newcastle University, commented on the next steps for the team: “The next steps are to compare the computationally predicted outcomes with the actual surgery outcomes in individual patients and to investigate how alternative surgery targets can be included in the future treatment.”
Research lead Frances Hutchings, also of Newcastle University, concluded: “Removal of brain tissue is often the final option for treatment of temporal lobe epilepsy but we know that it is not always effective. It’s early days and there is more work to be done, but this model could assist surgeons in targeting surgical procedures more effectively and help people with epilepsy lead a more normal life.”