Epilepsy News Today: One-Step Epilepsy Surgery Can Now Be Done Using Computational Models
Epilepsy and seizures are extremely complicated neurological disorders. Understanding the exact pathological factors and identifying the most appropriate treatment procedure for the same require extensive biomedical analyses. Furthermore, it has been observed that most of these patients are unresponsive toward the conventional modes of treatment. In such cases, neurologists try to identify the region of origin of seizures and then treat it through surgical intervention.
However, it is easier said than done. Tracing out the focal point of origin of seizures is an extremely difficult task and requires surgical implantation of grid electrodes in the patient's brain. Once implanted the physicians then wait for the next seizure during which they track the brain activity of the patient via electroencephalography (EEG).
Based on the information generated, the focal point of origin of seizure is traced. Once the point is identified, the next step is to remove the electrodes and the diseased brain tissue region through a second surgery. Hence, the currently adept method of epilepsy surgery is actually a combination of two consecutive surgeries that increases the chances of post-surgical mortality and morbidity.
According to a recent public release on EurekAlert, Joseph Madsen, Director of Epilepsy Surgery department, and Eun-Hyoung Park, a computational biophysicist, Department of Neurosurgery of the Boston Children's Hospital, have developed a new computational model that can track the diseased portion of the brain of epileptic patients without inserting electrodes in it. Most importantly, it eliminates the requirement of two surgeries.
According to the article published in the Neurosurgery journal, Madsen and Park used the Granger causality analysis algorithm, Nobel Prize winning statistical economic forecasting model, to calculate the probability of how the activity of one brain region influences the subsequent activity in another region. They used it to develop a causal model that was later superimposed on the epileptogenic network of 25 patients.
News-Medical.net reported that comparative analysis of the superimposed images and the EEG data revealed the presence of statistically significant correlation. The data was further crosschecked and approved by 10 certified expert epileptologists. According to these experts, the clinical application of the statistical method to identify the diseased brain region for epilepsy surgery in real-time conditions requires large-scale analysis and process standardization.