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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross‐sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE‐HS) correlate with clinical features. Methods: We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1‐weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA‐Epilepsy consortium, comprising 804 people with MTLE‐HS and 1625 healthy controls from 25 centers. Features with a moderate case–control effect size (Cohen d ≥.5) were used to train an event‐based model (EBM), which estimates a sequence of disease‐specific biomarker changes from cross‐sectional data and assigns a biomarker‐based fine‐grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. Results: In MTLE‐HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ =.293, p = 7.03 × 10−16), age at onset (ρ = −.18, p = 9.82 × 10−7), and ASM resistance (area under the curve =.59, p =.043, Mann–Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE‐HS with mild or nondetectable abnormality on T1W MRI. Significance: From cross‐sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE‐HS subjects in other cohorts and help establish connections between imaging‐based progression staging and clinical features. [ABSTRACT FROM AUTHOR]

Previous studies investigating associations between white matter alterations and duration of temporal lobe epilepsy (TLE) have shown differing results, and were typically limited to univariate analyses of tracts in isolation. In this study, we apply a multivariate measure (the Mahalanobis distance), which captures the distinct ways white matter may differ in individual patients, and relate this to epilepsy duration. Diffusion MRI, from a cohort of 94 subjects (28 healthy controls, 33 left‐TLE and 33 right‐TLE), was used to assess the association between tract fractional anisotropy (FA) and epilepsy duration. Using ten white matter tracts, we analysed associations using the traditional univariate analysis (z‐scores) and a complementary multivariate approach (Mahalanobis distance), incorporating multiple white matter tracts into a single unified analysis. For patients with right‐TLE, FA was not significantly associated with epilepsy duration for any tract studied in isolation. For patients with left‐TLE, the FA of two limbic tracts (ipsilateral fornix, contralateral cingulum gyrus) were significantly negatively associated with epilepsy duration (Bonferonni corrected p <.05). Using a multivariate approach we found significant ipsilateral positive associations with duration in both left, and right‐TLE cohorts (left‐TLE: Spearman's ρ = 0.487, right‐TLE: Spearman's ρ = 0.422). Extrapolating our multivariate results to duration equals zero (i.e., at onset) we found no significant difference between patients and controls. Associations using the multivariate approach were more robust than univariate methods. The multivariate Mahalanobis distance measure provides non‐overlapping and more robust results than traditional univariate analyses. Future studies should consider adopting both frameworks into their analysis in order to ascertain a more complete understanding of epilepsy progression, regardless of laterality. [ABSTRACT FROM AUTHOR]