Objective Epilepsy surgery fails to achieve seizure freedom in 30%–40% of cases. It is not fully understood why some surgeries are unsuccessful. By comparing interictal magnetoencephalography (MEG) band power from patient data to normative maps, which describe healthy spatial and population variability, we identify patient‐specific abnormalities relating to surgical failure. We propose three mechanisms contributing to poor surgical outcome: (1) not resecting the epileptogenic abnormalities (mislocalization), (2) failing to remove all epileptogenic abnormalities (partial resection), and (3) insufficiently impacting the overall cortical abnormality. Herein we develop markers of these mechanisms, validating them against patient outcomes. Methods Resting‐state MEG recordings were acquired for 70 healthy controls and 32 patients with refractory neocortical epilepsy. Relative band‐power spatial maps were computed using source‐localized recordings. Patient and region‐specific band‐power abnormalities were estimated as the maximum absolute z‐score across five frequency bands using healthy data as a baseline. Resected regions were identified using postoperative magnetic resonance imaging (MRI). We hypothesized that our mechanistically interpretable markers would discriminate patients with and without postoperative seizure freedom. Results Our markers discriminated surgical outcome groups (abnormalities not targeted: area under the curve [AUC] = 0.80, p = .003; partial resection of epileptogenic zone: AUC = 0.68, p = .053; and insufficient cortical abnormality impact: AUC = 0.64, p = .096). Furthermore, 95% of those patients who were not seizure‐free had markers of surgical failure for at least one of the three proposed mechanisms. In contrast, of those patients without markers for any mechanism, 80% were ultimately seizure‐free. Significance The mapping of abnormalities across the brain is important for a wide range of neurological conditions. Here we have demonstrated that interictal MEG band‐power mapping has merit for the localization of pathology and improving our mechanistic understanding of epilepsy. Our markers for mechanisms of surgical failure could be used in the future to construct predictive models of surgical outcome, aiding clinical teams during patient pre‐surgical evaluations.

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.