Search

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.

More than 100 genetic etiologies have been identified in developmental and epileptic encephalopathies (DEEs), but correlating genetic findings with clinical features at scale has remained a hurdle because of a lack of frameworks for analyzing heterogenous clinical data. Here, we analyzed 31,742 Human Phenotype Ontology (HPO) terms in 846 individuals with existing whole-exome trio data and assessed associated clinical features and phenotypic relatedness by using HPO-based semantic similarity analysis for individuals with de novo variants in the same gene. Gene-specific phenotypic signatures included associations of SCN1A with "complex febrile seizures" (HP: 0011172; p = 2.1 × 10-5) and "focal clonic seizures" (HP: 0002266; p = 8.9 × 10-6), STXBP1 with "absent speech" (HP: 0001344; p = 1.3 × 10-11), and SLC6A1 with "EEG with generalized slow activity" (HP: 0010845; p = 0.018). Of 41 genes with de novo variants in two or more individuals, 11 genes showed significant phenotypic similarity, including SCN1A (n = 16, p < 0.0001), STXBP1 (n = 14, p = 0.0021), and KCNB1 (n = 6, p = 0.011). Including genetic and phenotypic data of control subjects increased phenotypic similarity for all genetic etiologies, whereas the probability of observing de novo variants decreased, emphasizing the conceptual differences between semantic similarity analysis and approaches based on the expected number of de novo events. We demonstrate that HPO-based phenotype analysis captures unique profiles for distinct genetic etiologies, reflecting the breadth of the phenotypic spectrum in genetic epilepsies. Semantic similarity can be used to generate statistical evidence for disease causation analogous to the traditional approach of primarily defining disease entities through similar clinical features.

The epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analysed from 1069 healthy controls and 1249 patients: temporal lobe epilepsy with hippocampal sclerosis (n = 599), temporal lobe epilepsy with normal MRI (n = 275), genetic generalized epilepsy (n = 182) and non-lesional extratemporal epilepsy (n = 193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fibre tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at P

PurposeLamb-Shaffer syndrome (LAMSHF) is a neurodevelopmental disorder described in just over two dozen patients with heterozygous genetic alterations involving SOX5, a gene encoding a transcription factor regulating cell fate and differentiation in neurogenesis and other discrete developmental processes. The genetic alterations described so far are mainly microdeletions. The present study was aimed at increasing our understanding of LAMSHF, its clinical and genetic spectrum, and the pathophysiological mechanisms involved.MethodsClinical and genetic data were collected through GeneMatcher and clinical or genetic networks for 41 novel patients harboring various types ofSOX5 alterations. Functional consequences of selected substitutions were investigated.ResultsMicrodeletions and truncating variants occurred throughout SOX5. In contrast, most missense variants clustered in the pivotal SOX-specific high-mobility-group domain. The latter variants prevented SOX5 from binding DNA and promoting transactivation in vitro, whereas missense variants located outside the high-mobility-group domain did not. Clinical manifestations and severity varied among patients. No clear genotype-phenotype correlations were found, except that missense variants outside the high-mobility-group domain were generally better tolerated.ConclusionsThis study extends the clinical and genetic spectrum associated with LAMSHF and consolidates evidence that SOX5 haploinsufficiency leads to variable degrees of intellectual disability, language delay, and other clinical features.

ObjectiveClassification of epilepsy into types and subtypes is important for both clinical care and research into underlying disease mechanisms. A quantitative, data-driven approach may augment traditional electroclinical classification and shed new light on existing classification frameworks.MethodsWe used latent class analysis, a statistical method that assigns subjects into groups called latent classes based on phenotypic elements, to classify individuals with common familial epilepsies from the Epi4K Multiplex Families study. Phenotypic elements included seizure types, seizure symptoms, and other elements of the medical history. We compared class assignments to traditional electroclinical classifications and assessed familial aggregation of latent classes.ResultsA total of 1120 subjects with epilepsy were assigned to five latent classes. Classes 1 and 2 contained subjects with generalized epilepsy, largely reflecting the distinction between absence epilepsies and younger onset (class 1) versus myoclonic epilepsies and older onset (class 2). Classes 3 and 4 contained subjects with focal epilepsies, and in contrast to classes 1 and 2, these did not adhere as closely to clinically defined focal epilepsy subtypes. Class 5 contained nearly all subjects with febrile seizures plus or unknown epilepsy type, as well as a few subjects with generalized epilepsy and a few with focal epilepsy. Family concordance of latent classes was similar to or greater than concordance of clinically defined epilepsy types.SignificanceQuantitative classification of epilepsy has the potential to augment traditional electroclinical classification by (1) combining some syndromes into a single class, (2) splitting some syndromes into different classes, (3) helping to classify subjects who could not be classified clinically, and (4) defining the boundaries of clinically defined classifications. This approach can guide future research, including molecular genetic studies, by identifying homogeneous sets of individuals that may share underlying disease mechanisms.

Gene identification in epilepsy has mainly been limited to large families segregating genes of major effect and de novo mutations in epileptic encephalopathies. Many families that present with common non-acquired focal epilepsies and genetic generalized epilepsies remain unexplained. We assembled a cohort of 'genetically enriched' common epilepsies by collecting and phenotyping families containing multiple individuals with unprovoked seizures. We aimed to determine if specific clinical epilepsy features aggregate within families, and whether this segregation of phenotypes may constitute distinct 'familial syndromes' that could inform genomic analyses. Families with three or more individuals with unprovoked seizures were studied across multiple international centres. Affected individuals were phenotyped and classified according to specific electroclinical syndromes. Families were categorized based on syndromic groupings of affected family members, examined for pedigree structure and phenotypic patterns and, where possible, assigned specific familial epilepsy syndromes. A total of 303 families were assembled and analysed, comprising 1120 affected phenotyped individuals. Of the 303 families, 117 exclusively segregated generalized epilepsy, 62 focal epilepsy, and 22 were classified as genetic epilepsy with febrile seizures plus. Over one-third (102 families) were observed to have mixed epilepsy phenotypes: 78 had both generalized and focal epilepsy features within the same individual (n = 39), or within first or second degree relatives (n = 39). Among the genetic generalized epilepsy families, absence epilepsies were found to cluster within families independently of juvenile myoclonic epilepsy, and significantly more females were affected than males. Of the 62 familial focal epilepsy families, two previously undescribed familial focal syndrome patterns were evident: 15 families had posterior quadrant epilepsies, including seven with occipito-temporal localization and seven with temporo-parietal foci, and four families displayed familial focal epilepsy of childhood with multiple affected siblings that was suggestive of recessive inheritance. The findings suggest (i) specific patterns of syndromic familial aggregation occur, including newly recognized forms of familial focal epilepsy; (ii) although syndrome-specificity usually occurs in multiplex families, the one-third of families with features of both focal and generalized epilepsy is suggestive of shared genetic determinants; and (iii) patterns of features observed across families including pedigree structure, sex, and age of onset may hold clues for future gene identification. Such detailed phenotypic information will be invaluable in the conditioning and interpretation of forthcoming sequencing data to understand the genetic architecture and inter-relationships of the common epilepsy syndromes.

Making a specific diagnosis in neurodevelopmental disorders is traditionally based on recognizing clinical features of a distinct syndrome, which guides testing of its possible genetic etiologies. Scalable frameworks for genomic diagnostics, however, have struggled to integrate meaningful measurements of clinical phenotypic features. While standardization has enabled generation and interpretation of genomic data for clinical diagnostics at unprecedented scale, making the equivalent breakthrough for clinical data has proven challenging. However, increasingly clinical features are being recorded using controlled dictionaries with machine readable formats such as the Human Phenotype Ontology (HPO), which greatly facilitates their use in the diagnostic space. Improving the tractability of large‐scale clinical information will present new opportunities to inform genomic research and diagnostics from a clinical perspective. Here, we describe novel approaches for computational phenotyping to harmonize clinical features, improve data translation through revising domain‐specific dictionaries, quantify phenotypic features, and determine clinical relatedness. We demonstrate how these concepts can be applied to longitudinal phenotypic information, which represents a critical element of developmental disorders and pediatric conditions. Finally, we expand our discussion to clinical data derived from electronic medical records, a largely untapped resource of deep clinical information with distinct strengths and weaknesses. [ABSTRACT FROM AUTHOR]

Objective: The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference. We sought to redesign the HPO seizure subontology to improve its consistency with current epileptological concepts, supporting the use of large clinical data sets in high‐throughput clinical and research genomics. Methods: We created a new HPO seizure subontology based on the 2017 International League Against Epilepsy (ILAE) Operational Classification of Seizure Types, and integrated concepts of status epilepticus, febrile, reflex, and neonatal seizures at different levels of detail. We compared the HPO seizure subontology prior to, and following, our revision, according to the information that could be inferred about the seizures of 791 individuals from three independent cohorts: 2 previously published and 150 newly recruited individuals. Each cohort's data were provided in a different format and harmonized using the two versions of the HPO. Results: The new seizure subontology increased the number of descriptive concepts for seizures 5‐fold. The number of seizure descriptors that could be annotated to the cohort increased by 40% and the total amount of information about individuals' seizures increased by 38%. The most important qualitative difference was the relationship of focal to bilateral tonic‐clonic seizure to generalized‐onset and focal‐onset seizures. Significance: We have generated a detailed contemporary conceptual map for harmonization of clinical seizure data, implemented in the official 2020‐12‐07 HPO release and freely available at hpo.jax.org. This will help to overcome the phenotypic bottleneck in genomics, facilitate reuse of valuable data, and ultimately improve diagnostics and precision treatment of the epilepsies. [ABSTRACT FROM AUTHOR]

Juvenile myoclonic epilepsy (JME) is a clinically and genetically heterogenous, generalized epilepsy syndrome usually starting in adolescence. An age-related, predominantly frontocortical-subcortical network dysfunction is likely to be the substrate of bilateral myoclonic seizures occurring at full consciousness within hours after awakening, which are the clinical hallmark of JME. Although essential features of JME were recognized by Herpin more than 140 years ago, it is still an enigmatic epilepsy syndrome in many ways; advanced imaging techniques reveal multi-focal abnormalities in this paradigmatic generalized epilepsy syndrome; clinical studies reveal a major role of genetics in etiology, but the underlying molecular changes are likely to be highly heterogeneous; many JME patients have psycho-social issues, even though their intelligence is normal; antiepileptic drugs (AEDs), notably valproic acid, achieve seizure remission in two thirds of patients, but more patients seem to relapse after stopping AEDs than in any other epilepsy syndrome. This pessimistic outlook has been challenged in recent population-based studies and needs to be assessed in randomized AED withdrawal trials. This review summarizes recent focus neuroimaging, genetic, and behavioral aspects of JME and re-appraises the entrenched view that remission off AEDs is exceptionally rare in JME. [ABSTRACT FROM AUTHOR]

Congenital hyperekplexia is a rare, potentially treatable neuromotor disorder. Three major genes of effect are known, and all three affect glycinergic neurotransmission. Two genes encode for subunits of the postsynaptic inhibitory glycine receptor, GLRA1 encoding the α1 subunit and GLRB encoding the β subunit. The third, SLC6A5, encodes the cognate presynaptic glycine transporter 2. Ninety-seven individuals had a clinical diagnosis of hyperekplexia confirmed by genetic testing: 61 cases had mutations in GLRA1, 24 cases in SLC6A5 and 12 in GLRB. Detailed retrospective clinical analysis ascertained that all gene-positive cases present in the neonatal period (occasionally prenatally) and that clonazepam is the treatment of choice (95% found it to be efficacious). We confirm that hyperekplexia is predominantly a recessive condition but dominant cases are seen (16%). We found no genetic evidence for ‘major’ or ‘minor’ forms of hyperekplexia on a population basis. Thirty-five gene-negative cases were studied for comparison, their cardinal feature was presentation after the first month of life (P < 0.001). In addition to the characteristic ‘stiffness, startles and stumbles’ of hyperekplexia, apnoea attacks (50 of 89) and delayed development (47 of 92) were frequently reported. Patients with SLC6A5 mutations were significantly more likely to have had recurrent infantile apnoeas (RR1.9; P < 0.005) than those with GLRA1 mutations. Patients with GLRB and SLC6A5 mutations were more likely to have developmental delay (RR1.5 P < 0.01; RR1.9 P < 0.03) than those with GLRA1 mutations; 92% of GLRB cases reported a mild to severe delay in speech acquisition. Molecular modelling of pathogenic mutations demonstrates specific patterns of protein disruption that can be used to predict phenotype severity. The developmental delay in hyperekplexia, and speech acquisition in particular, may represent failure of developmental neural networks or subtle neurogenic migration defects in the absence of presynaptic glycine release. We recommend early genetic testing for symptomatic neonates and possibly preconception counselling for those at risk for GLRB and SLC6A5 mutations, because of the more challenging phenotype. [ABSTRACT FROM PUBLISHER]

Abstract: It has been estimated that JME (juvenile myoclonic epilepsy), when compared to other adult epilepsy syndromes, is most likely to have a genetic cause. However, decades of research have not brought us closer to finding a single ‘JME gene’ that is important on a population basis. Is this due in part to the genetic complexity of the syndrome, the cryptic nature of the genes of effect, or perhaps because JME is not one condition at all but many? Before we can begin to harness the power of next-generation sequencing techniques, we must first reduce JME down to lacunae of homogeneity — using increasingly more sophisticated phenotyping tools. The current technological advances in gene sequencing have been used to dramatic effect to identify single gene causes in rare syndromes and identify risk variants in malignancies. Filtering the variety of the human exome or genome down into a handful of biologically plausible candidates now relies on a pipeline of biostatistics, software, and functional analyses. It is simply unacceptable to return uncertain findings to the clinical domain and, therefore, it is crucial that pathogenicity is fully determined before families receive genetic counseling and test results. This article is part of a Special Issue entitled “The Future of Translational Epilepsy Research”. [Copyright &y& Elsevier]

It is well recognized that mood disorders and epilepsy commonly co-occur. Despite this, our knowledge regarding the relationship between epilepsy and bipolar disorder is limited. Several shared features between the two disorders, such as their episodic nature and potential to run a chronic course, and the efficacy of some antiepileptic medications in the prophylaxis of both disorders, are often cited as evidence of possible shared underlying pathophysiology. The present paper aims to review the bidirectional associations between epilepsy and bipolar disorder, with a focus on epidemiological links, evidence for shared etiology, and the impact of these disorders on both the individual and wider society. Better recognition and understanding of these two complex disorders, along with an integrated clinical approach, are crucial for improved evaluation and management of comorbid epilepsy and mood disorders. [ABSTRACT FROM AUTHOR]