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Dogs with epilepsy are among the commonest neurological patients in veterinary practice and therefore have historically attracted much attention with regard to definitions, clinical approach and management. A number of classification proposals for canine epilepsy have been published during the years reflecting always in parts the current proposals coming from the human epilepsy organisation the International League Against Epilepsy (ILAE). It has however not been possible to gain agreed consensus, “a common language”, for the classification and terminology used between veterinary and human neurologists and neuroscientists, practitioners, neuropharmacologists and neuropathologists. This has led to an unfortunate situation where different veterinary publications and textbook chapters on epilepsy merely reflect individual author preferences with respect to terminology, which can be confusing to the readers and influence the definition and diagnosis of epilepsy in first line practice and research studies. In this document the International Veterinary Epilepsy Task Force (IVETF) discusses current understanding of canine epilepsy and presents our 2015 proposal for terminology and classification of epilepsy and epileptic seizures. We propose a classification system which reflects new thoughts from the human ILAE but also roots in former well accepted terminology. We think that this classification system can be used by all stakeholders.

The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology. Epilepsies are common brain disorders and are classified based on clinical phenotyping, imaging and genetics. Here, the authors perform genome-wide association studies for 3 broad and 7 subtypes of epilepsy and identify 16 loci - 11 novel - that are further annotated by eQTL and partitioned heritability analyses.

Hippocampal sclerosis (HS) is considered the most frequent neuropathological finding in patients with mesial temporal lobe epilepsy (MTLE). Hippocampal specimens of pharmacoresistant MTLE patients that underwent epilepsy surgery for seizure control reveal the characteristic pattern of segmental neuronal cell loss and concomitant astrogliosis. However, classification issues of hippocampal lesion patterns have been a matter of intense debate. International consensus classification has only recently provided significant progress for comparisons of neurosurgical and clinic-pathological series between different centers. The respective four-tiered classification system of the International League Against Epilepsy subdivides HS into three types and includes a term of “gliosis only, no-HS”. Future studies will be necessary to investigate whether each of these subtypes of HS may be related to different etiological factors or with postoperative memory and seizure outcome. Molecular studies have provided potential deeper insights into the pathogenesis of HS and MTLE on the basis of epilepsy-surgical hippocampal specimens and corresponding animal models. These include channelopathies, activation of NMDA receptors, and other conditions related to Ca 2+ influx into neurons, the imbalance of Ca 2+ —binding proteins, acquired channelopathies that increase neuronal excitability, paraneoplastic and non-paraneoplastic inflammatory events, and epigenetic regulation promoting or facilitating hippocampal epileptogenesis. Genetic predisposition for HS is clearly suggested by the high incidence of family history in patients with HS, and by familial MTLE with HS. So far, it is clear that HS is multifactorial and there is no individual pathogenic factor either necessary or sufficient to generate this intriguing histopathological condition. The obvious variety of pathogenetic combinations underlying HS may explain the multitude of clinical presentations, different responses to clinical and surgical treatment. We believe that the stratification of neuropathological patterns can help to characterize specific clinic-pathological entities and predict the postsurgical seizure control in an improved fashion.

Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory. The ‘dynamotype’ of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties. Epileptic seizures have been recognized for centuries. But it was only in the 1930s that it was realized that seizures are the result of out-of-control electrical activity in the brain. By placing electrodes on the scalp, doctors can identify when and where in the brain a seizure begins. But they cannot tell much about how the seizure behaves, that is, how it starts, stops or spreads to other areas. This makes it difficult to control and prevent seizures. It also helps explain why almost a third of patients with epilepsy continue to have seizures despite being on medication. Saggio, Crisp et al. have now approached this problem from a new angle using methods adapted from physics and engineering. In these fields, “dynamics research” has been used with great success to predict and control the behavior of complex systems like electrical power grids. Saggio, Crisp et al. reasoned that applying the same approach to the brain would reveal the dynamics of seizures and that such information could then be used to categorize seizures into groups with similar properties. This would in effect create for seizures what the periodic table is for the elements. Applying the dynamics research method to seizure data from more than a hundred patients from across the world revealed 16 types of seizure dynamics. These “dynamotypes” had distinct characteristics. Some were more common than others, and some tended to occur together. Individual patients showed different dynamotypes over time. By constructing a way to classify seizures based on the relationships between the dynamotypes, Saggio, Crisp et al. provide a new tool for clinicians and researchers studying epilepsy. Previous clinical tools have focused on the physical symptoms of a seizure (referred to as the phenotype) or its potential genetic causes (genotype). The current approach complements these tools by adding the dynamotype: how seizures start, spread and stop in the brain. This approach has the potential to lead to new branches of research and better understanding and treatment of seizures.

Every fourth patient submitted to epilepsy surgery suffers from a brain tumor. Microscopically, these neoplasms present with a wide-ranging spectrum of glial or glio-neuronal tumor subtypes. Gangliogliomas (GG) and dysembryoplastic neuroepithelial tumors (DNTs) are the most frequently recognized entities accounting for 65 % of 1,551 tumors collected at the European Epilepsy Brain Bank ( n  = 5,842 epilepsy surgery samples). These tumors often present with early seizure onset at a mean age of 16.5 years, with 77 % of neoplasms affecting the temporal lobe. Relapse and malignant progression are rare events in this particular group of brain tumors. Surgical resection should be regarded, therefore, also as important treatment strategy to prevent epilepsy progression as well as seizure- and medication-related comorbidities. The characteristic clinical presentation and broad histopathological spectrum of these highly epileptogenic brain tumors will herein be classified as “long-term epilepsy associated tumors—LEATs”. LEATs differ from most other brain tumors by early onset of spontaneous seizures, and conceptually are regarded as developmental tumors to explain their pleomorphic microscopic appearance and frequent association with Focal Cortical Dysplasia Type IIIb. However, the broad neuropathologic spectrum and lack of reliable histopathological signatures make these tumors difficult to classify using the WHO system of brain tumors. As another consequence from poor agreement in published LEAT series, molecular diagnostic data remain ambiguous. Availability of surgical tissue specimens from patients which have been well characterized during their presurgical evaluation should open the possibility to systematically address the origin and epileptogenicity of LEATs, and will be further discussed herein. As a conclusion, the authors propose a novel A–B–C terminology of epileptogenic brain tumors (“epileptomas”) which hopefully promote the discussion between neuropathologists, neurooncologists and epileptologists. It must be our future mission to achieve international consensus for the clinico-pathological classification of LEATs that would also involve World Health Organization (WHO) and the International League against Epilepsy (ILAE).

Electroencephalogram (EEG) signal contains important information about abnormal brain activity, which has become an important basis for epilepsy diagnosis. Recently, epilepsy EEG signal classification methods mainly extract features from the perspective of a single domain, which cannot effectively utilize the spatial domain information in EEG signals. The redundant information in EEG signals will affect the learning features with the increase of convolution layer and multi-domain features, resulting in inefficient learning and a lack of distinguishing features. To tackle these issues, we propose an end-to-end 3D convolutional multiband seizure-type classification model based on attention mechanisms. Specifically, to process preprocessed electroencephalogram (EEG) data, a multilevel wavelet decomposition is applied to obtain the joint distribution information in the two-dimensional time–frequency domain across multiple frequency bands. Subsequently, this information is transformed into three-dimensional spatial data based on the electrode configuration. Discriminative joint activity features in the time, frequency, and spatial domains are then extracted by a series of parallel 3D convolutional sub-networks, where 3D channels and spatial attention mechanisms improve the ability to learn critical global and local information. A multi-layer perceptron is finally implemented to integrate the extracted features and further map them to the classification results. Experimental results on the TUSZ dataset, the world’s largest publicly available seizure corpus, show that 3D-CBAMNet significantly outperforms the state-of-the-art methods, indicating effectiveness in the seizure type classification task. •This paper builds a three-dimensional convolutional model based on channel and spatial attention, which is suitable for the seizure-type classification task.•The proposed model takes advantage of multi-stage wavelet decomposition to obtain multi-band time–frequency information and constructs time–frequency-spatial domain 3D features according to the electrode distribution.

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the “irritative zone” (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram–functional Magnetic Resonance Imagining (EEG–fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG–fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG–fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy. •We used a new approach for classifying epileptic spikes in 8 patients.•We compared the results with visual classification using EEG–fMRI maps.•The performance for EEG–fMRI analysis improved from 46 to 72%.•IED-cluster localization matched presumed IZ in additional 4 classes and 1 patient.

Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG.

Focal cortical dysplasias (FCD) which represent a composite group of cortical malformations are increasingly recognized as morphological substrate for severe therapy-refractory epilepsy in children and young adults. However, presurgical evaluation remains challenging as not all FCD variants can be reliably detected by high-resolution magnetic resonance imaging (MRI). Here, we studied a cohort of 52 epilepsy patients with neuropathological evidence for FCD using the 2011 classification of the International League against Epilepsy (ILAE) and systematically analysed those histopathologic features applicable also for MRI diagnostics. Histopathologic parameters included quantitative measurements of cellular profiles, cortical thickness, heterotopic neurons in white matter, and myelination that were compared between FCD subtypes and age-/localization-matched controls ( n  = 36) using multivariate analysis. Dysmorphic neurons in both FCD Type II variants showed significantly increased diameter of their cell bodies and nuclei. Cortical thickness was also increased with a distinct loss of myelin content specifying FCD Type IIb from IIa. The data further suggested that myelination deficits in FCD Type IIb result from compromised oligodendroglial lineage differentiation and we concluded that the “transmantle sign” is a unique finding in FCD Type IIb. In contrast, FCD Type Ia was characterized by a smaller cortical ribbon and higher neuronal densities, but these parameters failed to reach statistical significance (considering age- and location-dependent variability in controls). All FCD variants showed abnormal grey–white matter boundaries with increased numbers of heterotopic neurons. Similar results were obtained also at deep white matter location. Thus, many FCD variants may indeed escape visual MRI inspection, but suspicious areas with increased or decreased cortical thickness as well as grey–white matter blurring may be uncovered using post-processing protocols of neuroimaging data. The systematic analysis of well-specified histopathological features could be helpful to improve sensitivity and specificity in MRI detection during pre-surgical work-up of patients with drug-resistant focal epilepsies.

This article reviews the role of EEG in the diagnosis and management of patients with epilepsies. We review the morphologic and behavioral characteristics of the interictal and ictal EEG markers of the different types of epilepsy that should guide recording strategies to augment its diagnostic yield, and we attempt to delineate those particular features that may be relevant to the main epilepsy syndromes. Particular emphasis is placed on the activation methods, including hyperventilation, sleep deprivation and sleep, and specific triggers, as well as how these may differ between idiopathic and cryptogenic/symptomatic generalized and focal epilepsies, commenting on possible diagnostic pitfalls and areas of uncertainty. We also consider the indications for long-term recordings (video - telemetry and ambulatory) and emphasize the diagnostic value of polygraphic recordings.