Progressive functional decline in the epilepsies is largely unexplained. We formed the ENIGMA-Epilepsy consortium to understand factors that influence brain measures in epilepsy, pooling data from 24 research centres in 14 countries across Europe, North and South America, Asia, and Australia. Structural brain measures were extracted from MRI brain scans across 2149 individuals with epilepsy, divided into four epilepsy subgroups including idiopathic generalized epilepsies (n =367), mesial temporal lobe epilepsies with hippocampal sclerosis (MTLE; left, n = 415; right, n = 339), and all other epilepsies in aggregate (n = 1026), and compared to 1727 matched healthy controls. We ranked brain structures in order of greatest differences between patients and controls, by metaanalysing effect sizes across 16 subcortical and 68 cortical brain regions. We also tested effects of duration of disease, age at onset, and age-by-diagnosis interactions on structural measures. We observed widespread patterns of altered subcortical volume and reduced cortical grey matter thickness. Compared to controls, all epilepsy groups showed lower volume in the right thalamus (Cohen's d = À0.24 to À0.73; P 5 1.49 Â 10 À4 ), and lower thickness in the precentral gyri bilaterally (d = À0.34 to À0.52; P 5 4.31 Â 10 À6 ). Both MTLE subgroups showed profound volume reduction in the ipsilateral hippocampus (d = À1.73 to À1.91, P 5 1.4 Â 10 À19 ), and lower thickness in extrahippocampal cortical regions, including the precentral and paracentral gyri, compared to controls (d = À0.36 to À0.52; P 5 1.49 Â 10 À4 ). Thickness differences of the ipsilateral temporopolar, parahippocampal, entorhinal, and fusiform gyri, contralateral pars triangularis, and bilateral precuneus, superior frontal and caudal middle frontal gyri were observed in left, but not right, MTLE (d = À0.29 to À0.54; P 5 1.49 Â 10 À4 ). Contrastingly, thickness differences of the ipsilateral pars opercularis, and contralateral transverse temporal gyrus, were observed in right, but not left, MTLE (d = À0.27 to À0.51; P 5 1.49 Â 10 À4 ). Lower subcortical volume and cortical thickness associated with a longer duration of epilepsy in the all-epilepsies, all-other-epilepsies, and right MTLE groups (beta, b 5 À0.0018; P 5 1.49 Â 10 À4 ). In the largest neuroimaging study of epilepsy to date, we provide information on the common epilepsies that could not be realistically acquired in any other way. Our study provides a robust ranking of brain measures that can be further targeted for study in genetic and neuropathological studies. This worldwide initiative identifies patterns of shared grey matter reduction across epilepsy syndromes, and distinctive abnormalities between epilepsy syndromes, which inform our understanding of epilepsy as a network disorder, and indicate that certain epilepsy syndromes involve more widespread structural compromise than previously assumed.

Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1021 adults with epilepsy compared to 1564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy colocalized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Negative effects of age on atrophy further revealed a strong influence of connectome architecture in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided deeper insights into the macroscale features that shape the pathophysiology of common epilepsies.

Objective: To determine clinical and EEG features that might help identify patients with epilepsy harboring small, intrinsically epileptogenic, surgically treatable, bottom-of-sulcus dysplasias (BOSDs).Methods: Retrospective review of clinical records, EEG, MRI, and histopathology in 32 patients with drug-resistant epilepsy and MRI-positive (72% 3.0 tesla), pathologically proven (type 2B cortical dysplasia) BOSDs operated at our centers during 2005-2013. Results: Localization of BOSDs was frontal in 19, insula in 5, parietal in 5, and temporal in 3, on the convexity or interhemispheric surfaces. BOSDs were missed on initial MRI at our centers in 22% of patients. Patients presented with focal seizures during infancy in 9, preschool years in 15, and school years in 8 (median age 5 years). Seizures were stereotyped, predominantly nocturnal, and typically nonconvulsive, with semiology referable to the fronto-central or perisylvian regions. Seizures occurred at high frequency during active periods, but often went into prolonged remission with carbamazepine or phenytoin. Intellect was normal or borderline, except in patients with seizure onset during infancy. Scalp EEG frequently revealed localized interictal epileptiform discharges and ictal rhythms. Patients underwent lesionectomy (median age 14 years) guided by electrocorticography and MRI, with prior intracranial EEG monitoring in only one patient. Twentyeight patients (88%) became seizure-free, and 20 discontinued antiepileptic medication (median follow-up 4.1 years). Conclusions:In patients with cryptogenic focal epilepsy, this clinical presentation and course should prompt review of or repeat MRI, looking for a BOSD in the frontal, parietal, or insula cortex. If a BOSD is identified, the patient might be considered for single-stage lesionectomy. Bottom-of-sulcus dysplasia (BOSD)1 is a localized variety of type 2 focal cortical dysplasia (FCD) in which the dysplastic features are maximal at the sulcal depth, tapering to a relatively normal gyral crown.2,3 Pathologically, BOSDs are characterized by neuronal dyslamination and dysmorphology, with or without balloon cells, at the bottom of the sulcus, with hypomyelination in the underlying white matter. 2,4,5 The characteristic MRI features of BOSD are cortical thickening, gray-white junction blurring, and subcortical T2 hyperintensity, often tapering to the ventricle as a "transmantle sign." [6][7][8][9] Straightening and elongation of the dysplastic sulcus, and depression or unusual sulcation of the overlying cerebral surface, may also be appreciated. 8,10,11 BOSDs are typically found in the frontal and parietal lobes.

Objective.To test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD).Methods.We used clinically-acquired 3D T1-weighted and 3D FLAIR MRI of 148 patients (median age, 23 years [range, 2-55]; 47% female) with histologically-verified FCD at nine centers to train a deep convolutional neural network (CNN) classifier. Images were initially deemed as MRI-negative in 51% of cases, in whom intracranial EEG determined the focus. For risk stratification, the CNN incorporated Bayesian uncertainty estimation as a measure of confidence. To evaluate performance, detection maps were compared to expert FCD manual labels. Sensitivity was tested in an independent cohort of 23 FCD cases (13±10 years). Applying the algorithm to 42 healthy and 89 temporal lobe epilepsy disease controls tested specificity.Results.Overall sensitivity was 93% (137/148 FCD detected) using a leave-one-site-out cross-validation, with an average of six false positives per patient. Sensitivity in MRI-negative FCD was 85%. In 73% of patients, the FCD was among the clusters with the highest confidence; in half it ranked the highest. Sensitivity in the independent cohort was 83% (19/23; average of five false positives per patient). Specificity was 89% in healthy and disease controls.Conclusions.This first multicenter-validated deep learning detection algorithm yields the highest sensitivity to date in MRI-negative FCD. By pairing predictions with risk stratification this classifier may assist clinicians to adjust hypotheses relative to other tests, increasing diagnostic confidence. Moreover, generalizability across age and MRI hardware makes this approach ideal for pre-surgical evaluation of MRI-negative epilepsy.Classification of evidence.This study provides Class III evidence that deep learning on multimodal MRI accurately identifies FCD in epilepsy patients initially diagnosed as MRI-negative.