Identifying epileptogenic abnormalities through spatial clustering of MEG interictal band power

Abstract

Abstract Successful epilepsy surgery depends on localizing and resecting cerebral abnormalities and networks that generate seizures. Abnormalities, however, may be widely distributed across multiple discontiguous areas. We propose spatially constrained clusters as candidate areas for further investigation and potential resection. We quantified the spatial overlap between the abnormality cluster and subsequent resection, hypothesizing a greater overlap in seizure‐free patients. Thirty‐four individuals with refractory focal epilepsy underwent pre‐surgical resting‐state interictal magnetoencephalography (MEG) recording. Fourteen individuals were totally seizure‐free (ILAE 1) after surgery and 20 continued to have some seizures post‐operatively (ILAE 2+). Band power abnormality maps were derived using controls as a baseline. Patient abnormalities were spatially clustered using the k‐means algorithm. The tissue within the cluster containing the most abnormal region was compared with the resection volume using the dice score. The proposed abnormality cluster overlapped with the resection in 71% of ILAE 1 patients. Conversely, an overlap only occurred in 15% of ILAE 2+ patients. This effect discriminated outcome groups well (AUC = 0.82). Our novel approach identifies clusters of spatially similar tissue with high abnormality. This is clinically valuable, providing (a) a data‐driven framework to validate current hypotheses of the epileptogenic zone localization or (b) to guide further investigation.

Keywords

• clustering
• epilepsy
• MEG
• outcome
• prediction
• surgery