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OBJECTIVE: Epilepsy with eyelid myoclonia (EEM) spectrum is a generalized form of epilepsy characterized by eyelid myoclonia with or without absences, eye closure-induced seizures with electroencephalographic paroxysms, and photosensitivity. Based on the specific clinical features, age at onset, and familial occurrence, a genetic cause has been postulated. Pathogenic variants in CHD2, SYNGAP1, NEXMIF, RORB, and GABRA1 have been reported in individuals with photosensitivity and eyelid myoclonia, but whether other genes are also involved, or a single gene is uniquely linked with EEM, or its subtypes, is not yet known. We aimed to dissect the genetic etiology of EEM. METHODS: We studied a cohort of 105 individuals by using whole exome sequencing. Individuals were divided into two groups: EEM- (isolated EEM) and EEM+ (EEM accompanied by intellectual disability [ID] or any other neurodevelopmental/psychiatric disorder). RESULTS: We identified nine variants classified as pathogenic/likely pathogenic in the entire cohort (8.57%); among these, eight (five in CHD2, one in NEXMIF, one in SYNGAP1, and one in TRIM8) were found in the EEM+ subcohort (28.57%). Only one variant (IFIH1) was found in the EEM- subcohort (1.29%); however, because the phenotype of the proband did not fit with published data, additional evidence is needed before considering IFIH1 variants and EEM- an established association. Burden analysis did not identify any single burdened gene or gene set. SIGNIFICANCE: Our results suggest that for EEM, as for many other epilepsies, the identification of a genetic cause is more likely with comorbid ID and/or other neurodevelopmental disorders. Pathogenic variants were mostly found in CHD2, and the association of CHD2 with EEM+ can now be considered a reasonable gene-disease association. We provide further evidence to strengthen the association of EEM+ with NEXMIF and SYNGAP1. Possible new associations between EEM+ and TRIM8, and EEM- and IFIH1, are also reported. A

We present an illustrative case to address anterior temporal lobe atrophy with poor delineation of the temporopolar gray-white matter interface based on T2-weighted and fluid-attenuated inversion recovery (FLAIR) images in patients with temporal lobe epilepsy associated with hippocampal sclerosis (TLE-HS). A 52-year-old woman with pharmacoresistant seizures since the age of six months underwent a previous MRI scan using a suboptimal protocol which was reported as unremarkable. MRI performed according to an epilepsy protocol showed classic signs of left HS and ipsilateral temporal polar atrophy with blurring of the gray-white matter boundary on FLAIR images. She underwent a left amygdalohippocampectomy and anterior temporal resection and remains seizure-free after 24 months. Histopathological analyses showed HS and no signs of focal cortical dysplasia (FCD). Blurring and atrophy of the ipsilateral temporal pole are common in TLE-HS and often misinterpreted as FCD. This relates to delayed myelination in patients with seizures before the age of two, is more pronounced on FLAIR sequences, and gives a false impression of cortical thickening. However, the T1-weighted images show a relatively well-demarcated cortical-subcortical transition and normal cortical thickness. By contrast, the cortical thickening in FCD is observed on both T1-weighted and FLAIR images. Since FCD also occurs in temporal lobe regions, it is important to differentiate the extra-hippocampal MRI abnormalities in TLE-HS from those likely to be FCD. This case highlights the importance of evaluation based on detailed imaging, which should always be conducted considering the EEG, seizure semiology, and other clinical information.

Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine ou

AIMS: The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis. METHODS: Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy. RESULTS: We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia. CONCLUSIONS: These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy.

Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.

The ILAE Neuroimaging Task Force aims to publish educational case reports highlighting basic aspects related to neuroimaging in epilepsy consistent with the educational mission of the ILAE. Previous quantitative MRI studies have established important imaging markers of epilepsy-related pathology, including features sensitive to hippocampal cell loss and reactive astrogliosis. Here, we review the case of a female with pediatric drug-resistant epilepsy. Throughout her course of treatment, she had seven MRI investigations at several centers; the first three did not follow optimized epilepsy imaging protocols whereas the remaining four adhered to HARNESS-MRI protocols ( har monized n euroimaging of e pilepsy s tructural s equences). Visual inspection of a set of HARNESS-MR images revealed conspicuous left hippocampal hyperintensity which may have been initially overlooked on non-optimized MR images. Quantitative analysis of these multimodal imaging data along hippocampal subfields provided clear evidence of hippocampal sclerosis, with increased atrophy, increased mean diffusivity, increased T2-FLAIR signal, and lower qT1 values observed in the anterior portions of the left, compared to the right hippocampus. The patient underwent a left anterior temporal lobectomy with amygdalohippocampectomy at age 16 years. Histopathology of the resected specimen also confirmed hippocampal sclerosis with widespread gliosis and focal neuronal loss in the hippocampal subfields overlapping with regions of multimodal quantitative alterations. The patient remains seizure-free one year after surgery. Collectively, this case highlights the need for optimized data acquisition protocols early in the treatment of epilepsy and supports quantitative analysis of MRI contrasts to enhance personalized diagnosis and prognosis of drug-resistant patients with epilepsy.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Magnetic resonance imaging (MRI) plays a central role in the management and evaluation of patients with epilepsy. It is important that structural MRI scans are optimally acquired and carefully reviewed by trained experts within the context of all available clinical data. The aim of this review is to discuss the essentials of MRI that will be useful to health care providers specialized in epilepsy, as outlined by the competencies and learning objectives of the recently developed ILAE curriculum. This review contains information on basic MRI principles, sequences, field strengths and safety, when to perform and repeat an MRI, epilepsy MRI protocol (HARNESS-MRI) and the basic reading guidelines, and common epileptic pathologies. More advanced topics such as MRI-negative epilepsy, functional MRI and diffusion-weighted imaging are also briefly discussed. Although the available resources can differ markedly across different centers, it is the hope that this review can provide general guidance in the everyday practice of using MRI for patients with epilepsy.

Structural magnetic resonance imaging (MRI) is of fundamental importance to the diagnosis and treatment of epilepsy, particularly when surgery is being considered. Despite previous recommendations and guidelines, practices for the use of MRI are variable worldwide and may not harness the full potential of recent technological advances for the benefit of people with epilepsy. The International League Against Epilepsy Diagnostic Methods Commission has thus charged the 2013-2017 Neuroimaging Task Force to develop a set of recommendations addressing the following questions: (1) Who should have an MRI? (2) What are the minimum requirements for an MRI epilepsy protocol? (3) How should magnetic resonance (MR) images be evaluated? (4) How to optimize lesion detection? These recommendations target clinicians in established epilepsy centers and neurologists in general/district hospitals. They endorse routine structural imaging in new onset generalized and focal epilepsy alike and describe the range of situations when detailed assessment is indicated. The Neuroimaging Task Force identified a set of sequences, with three-dimensional acquisitions at its core, the harmonized neuroimaging of epilepsy structural sequences-HARNESS-MRI protocol. As these sequences are available on most MR scanners, the HARNESS-MRI protocol is generalizable, regardless of the clinical setting and country. The Neuroimaging Task Force also endorses the use of computer-aided image postprocessing methods to provide an objective account of an individual's brain anatomy and pathology. By discussing the breadth and depth of scope of MRI, this report emphasizes the unique role of this noninvasive investigation in the care of people with epilepsy.

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 meta-analysing 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 = -0.24 to -0.73; P < 1.49 × 10-4), and lower thickness in the precentral gyri bilaterally (d = -0.34 to -0.52; P < 4.31 × 10-6). Both MTLE subgroups showed profound volume reduction in the ipsilateral hippocampus (d = -1.73 to -1.91, P < 1.4 × 10-19), and lower thickness in extrahippocampal cortical regions, including the precentral and paracentral gyri, compared to controls (d = -0.36 to -0.52; P < 1.49 × 10-4). 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 = -0.29 to -0.54; P < 1.49 × 10-4). Contrastingly, thickness differences of the ipsilateral pars opercularis, and contralateral transverse temporal gyr