HCN channels are highly expressed and functionally relevant in neurons and increasing evidence demonstrates their involvement in the etiology of human epilepsies. Among HCN isoforms, HCN4 is important in cardiac tissue, where it underlies pacemaker activity. Despite being expressed also in deep structures of the brain, mutations of this channel functionally shown to be associated with epilepsy have not been reported yet. Using Next Generation Sequencing for the screening of patients with idiopathic epilepsy, we identified the p.Arg550Cys (c.1648C>T) heterozygous mutation on HCN4 in two brothers affected by benign myoclonic epilepsy of infancy. Functional characterization in heterologous expression system and in neurons showed that the mutation determines a loss of function of HCN4 contribution to activity and an increase of neuronal discharge, potentially predisposing to epilepsy. Expressed in cardiomyocytes, mutant channels activate at slightly more negative voltages than wild-type (WT), in accordance with borderline bradycardia. While HCN4 variants have been frequently associated with cardiac arrhythmias, these data represent the first experimental evidence that functional alteration of HCN4 can also be involved in human epilepsy through a loss-of-function effect and associated increased neuronal excitability. Since HCN4 appears to be highly expressed in deep brain structures only early during development, our data provide a potential explanation for a link between dysfunctional HCN4 and infantile epilepsy. These findings suggest that it may be useful to include HCN4 screening to extend the knowledge of the genetic causes of infantile epilepsies, potentially paving the way for the identification of innovative therapeutic strategies.

Mutations in PCDH19, encoding protocadherin 19 on chromosome X, cause familial epilepsy and mental retardation limited to females or Dravet-like syndrome. We wished to explore the causative role of PCDH19 gene (Xq22) in female patients with epilepsy, from Southern Italy.

MicroRNAs (miRNAs) are small noncoding RNAs that have emerged as new potential epigenetic biomarkers. Here, we evaluate the efficacy of six circulating miRNA previously described in the literature as biomarkers for the diagnosis of temporal lobe epilepsy (TLE) and/or as predictive biomarkers to antiepileptic drug response. We measured the differences in serum miRNA levels by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) assays in a cohort of 27 patients (14 women and 13 men; mean ± SD age: 43.65 ± 17.07) with TLE compared to 20 healthy controls (HC) matched for sex, age and ethnicity (11 women and 9 men; mean ± SD age: 47.5 ± 9.1). Additionally, patients were classified according to whether they had drug-responsive (n = 17) or drug-resistant (n = 10) TLE. We have investigated any correlations between miRNAs and several electroclinical parameters. Three miRNAs (miR-142, miR-146a, miR-223) were significantly upregulated in patients (expressed as average expression ± SD). In detail, miR-142 expression was 0.40 ± 0.29 versus 0.16 ± 0.10 in TLE patients compared to HC (t-test, p < 0.01), miR-146a expression was 0.15 ± 0.11 versus 0.07 ± 0.04 (t-test, p < 0.05), and miR-223 expression was 6.21 ± 3.65 versus 1.23 ± 0.84 (t-test, p < 0.001). Moreover, results obtained from a logistic regression model showed the good performance of miR-142 and miR-223 in distinguishing drug-sensitive vs. drug-resistant TLE. The results of this pilot study give evidence that miRNAs are suitable targets in TLE and offer the rationale for further confirmation studies in larger epilepsy cohorts.

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 our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.

Three relatives carrying a t(4;8)(p15.2;p23.2) translocation had juvenile myoclonic epilepsy, self-limited photosensitive occipital epilepsy and migraine with aura. The t(4;8) translocation interrupted the coding sequence of CSMD1 gene and occurred immediately to the 3'UTR of STIM2 gene. STIM2 was overexpressed in the patient carrying the unbalanced translocation, and all three individuals had a single functional copy of CSMD1. Array CGH study disclosed that these three individuals also carried a deletion at 5q12.3 that involves the RGS7BP gene. The overall results favor the view that CSMD1, STIM2, and RGS7BP genes could contribute to epilepsy and migraine phenotypes in our family.

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.

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.

Dèjà-vù (DV) can occur as a seizure of mesial temporal lobe epilepsy (MTLE) and in almost 80% of healthy individuals. The remarkable similarity between epileptic DV and DV in healthy individuals raises the possibility that DV might sometimes be an ictal phenomenon in apparently normal individuals. Thus, we studied a group of healthy subjects versus individuals with benign MTLE (bMTLE) both experiencing DV.

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 gyrus, were observed in right, but not left, MTLE (d = -0.27 to -0.51; P < 1.49 × 10-4). 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 < -0.0018; P < 1.49 × 10-4). 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.

Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome.

Progressive myoclonus epilepsies (PMEs) comprise a group of clinically and genetically heterogeneous rare diseases. Over 70% of PME cases can now be molecularly solved. Known PME genes encode a variety of proteins, many involved in lysosomal and endosomal function. We performed whole-exome sequencing (WES) in 84 (78 unrelated) unsolved PME-affected individuals, with or without additional family members, to discover novel causes. We identified likely disease-causing variants in 24 out of 78 (31%) unrelated individuals, despite previous genetic analyses. The diagnostic yield was significantly higher for individuals studied as trios or families (14/28) versus singletons (10/50) (OR = 3.9, p value = 0.01, Fisher's exact test). The 24 likely solved cases of PME involved 18 genes. First, we found and functionally validated five heterozygous variants in NUS1 and DHDDS and a homozygous variant in ALG10, with no previous disease associations. All three genes are involved in dolichol-dependent protein glycosylation, a pathway not previously implicated in PME. Second, we independently validate SEMA6B as a dominant PME gene in two unrelated individuals. Third, in five families, we identified variants in established PME genes; three with intronic or copy-number changes (CLN6, GBA, NEU1) and two very rare causes (ASAH1, CERS1). Fourth, we found a group of genes usually associated with developmental and epileptic encephalopathies, but here, remarkably, presenting as PME, with or without prior developmental delay. Our systematic analysis of these cases suggests that the small residuum of unsolved cases will most likely be a collection of very rare, genetically heterogeneous etiologies.

A recent study has implicated a tagging single nucleotide polymorphism (SNP) rs398702 located 3' of KCNMB4 (encoding calcium-activated potassium channel, subfamily M subunit beta 4) as a possible susceptibility allele for mesial temporal lobe epilepsy (mTLE). Such a finding warrants a further well-powered study in additional carefully phenotyped cohorts. Here we examined the role of the SNP (rs398702) in a cohort of 332 patients (182 women and 150 men; mean±SD age: 47.06±18.12) who had diagnoses of mTLE. None of the patients had a mass lesion, malformations of cortical development, or traumatic brain injury. Brain MRI study revealed hippocampal sclerosis (Hs) in 86/332 (26%) patients. Most patients (254/332, 76%) patients had drug-responsive mTLE. We also enrolled 335 healthy controls (164 women and 171 men; mean±SD age: 48.20±21.90), matched for age, sex and ethnicity. All patients and controls were Caucasian and were born in Italy. The genotype distribution of the SNP rs398702 in patients and controls was within Hardy-Weinberg equilibrium (p>0.05). There was no statistically significant difference in the genotype or allelic frequencies between patients and controls (p=0.878 and p=0.666 respectively). Moreover, such a variant did not influence the main clinical characteristics of mTLE, the presence of Hs or responsiveness to antiepileptic drugs. In conclusion, our data suggest that the rs398702 variant in the KCNMB4 gene is unlikely to influence significantly the risk of developing mTLE or its severity. They further highlight the importance of replication to confirm the validity of association study results.

Nocturnal frontal lobe epilepsy (NFLE) can be sporadic or autosomal dominant; some families have nicotinic acetylcholine receptor subunit mutations. We report a novel autosomal recessive phenotype in a single family and identify the causative gene.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

Identifying late epileptic seizures (LS) following cerebral venous thrombosis (CVT) can be useful for prognosis and management. We systematically reviewed the literature to identify risk factors for LS due to CVT.

The Hyperpolarization-activated Cyclic Nucleotide-gated (HCN) channels are highly expressed in the Central Nervous Systems, where they are responsible for the Ih current. Together with specific accessory proteins, these channels finely regulate neuronal excitability and discharge activity. In the last few years, a substantial body of evidence has been gathered showing that modifications of Ih can play an important role in the pathogenesis of epilepsy. However, the extent to which HCN dysfunction is spread among the epileptic population is still unknown. The aim of this work is to evaluate the impact of genetic mutations potentially affecting the HCN channels' activity, using a NGS approach. We screened a large cohort of patients with epilepsy of unknown etiology for mutations in HCN1, HCN2 and HCN4 and in genes coding for accessory proteins (MiRP1, Filamin A, Caveolin-3, TRIP8b, Tamalin, S-SCAM and Mint2). We confirmed the presence of specific mutations of HCN genes affecting channel function and predisposing to the development of the disease. We also found several previously unreported additional genetic variants, whose contribution to the phenotype remains to be clarified. According to these results and data from literature, alteration of HCN1 channel function seems to play a major role in epilepsy, but also dysfunctional HCN2 and HCN4 channels can predispose to the development of the disease. Our findings suggest that inclusion of the genetic screening of HCN channels in diagnostic procedures of epileptic patients should be recommended. This would help pave the way for a better understanding of the role played by Ih dysfunction in the pathogenesis of epilepsy.

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.

The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current cortico-centric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural MRI in 1,602 adults with epilepsy and 1,022 healthy controls across twenty-two sites from the global ENIGMA-Epilepsy working group.