Introduction: Precise localization of epileptogenic zones is essential for the successful surgical treatment of refractory epilepsy including Lennox-Gastaut syndrome (LGS). The surgical resection areas are generally determined by epileptologists based on diverse neuroimaging modalities; however, exact epileptogenic zones cannot be accurately localized in many patients with LGS using the conventional methods. Therefore, new reliable algorithms are still required for enhancing the success rate of the resective epilepsy surgery. In the present study, we introduce an approach to localize epileptogenic zones in LGS based on the graph theoretical analysis of ical intracranial EEG (iEEG). Methods: Four patients with LGS who became seizure-free after the resective epilepsy surgery were selected. Before the surgery, their epileptogenic zones were delineated using EEG, iEEG, and several conventional imaging modalities. Phase locking value (PLV) analysis was applied to construct functional connectivity networks during ictal events, and then several graph theoretical indices including betweenness centrality (BC) were evaluated for each iEEG sensor to find the primary hubs of the ictal epileptic network. The graph theoretical index values were then overlaid on 3D individual cortical surface. Results: The iEEG channels with high BC values coincided well with the surgical resection areas. Among various graph theoretical measures such as local efficiency, participation coefficient, and eigenvector centrality, only BC showed fair correspondence with the surgical resection areas. Conclusions: The primary hubs in the ictal epileptic networks coincided well with areas of surgical resection in LGS patients with successful surgical outcomes. This observation warrants further studies to determine if the graph theoretical network analysis of ictal iEEG recordings can serve as a new auxiliary tool to localize epileptogenic zones in LGS.
All Science Journal Classification (ASJC) codes
- Pediatrics, Perinatology, and Child Health
- Developmental Neuroscience
- Clinical Neurology