We characterize a consanguineous Egyptian family with an autosomal recessively inherited familial cortical myoclonic tremor and epilepsy. We used multipoint linkage analysis to map the causative mutation to a 12.7 megabase interval within 1q31.3-q32.2 with a log of odds score of 3.6. For further investigation of the linked region in an efficient and unbiased manner, we performed exome sequencing. Within the suspected region we identified a homozygous single base pair deletion (c.503_503delG) leading to a frameshift in the coding region of the sixth exon of CNTN2 alias TAG-1 (p.Trp168fs), which segregated in the respective family. Many studies point towards an important role of the CNTN2 product contactin 2 in neuronal excitability. Contactin 2, a glycosylphosphatidylinositol-anchored neuronal membrane protein, and another transmembrane protein called contactin associated protein-like 2 (CNTNAP2 alias CASPR2) are together necessary to maintain voltage-gated potassium channels at the juxtaparanodal region. CNTN2 knockout mice were previously reported to suffer from spontaneous seizures and mutations in the CNTNAP2 gene have been described to cause epilepsy in humans. To further delineate the role of CNTN2 in patients with epilepsy, we sequenced the coding exons in 189 Caucasian patients with epilepsy. No recessive mutation was detected and heterozygote carriers of rare CNTN2 variants do not seem to be predisposed to epilepsy. Given the severity of the mutation and the proposed function of the gene, we consider this mutation as the most likely cause for cortical myoclonic tremor and epilepsy in this family.

Epilepsy is a neurological disorder for which the electroencephalogram (EEG) is the most important diagnostic tool. In particular, this diagnosis heavily depends on the detection of interictal (between seizures) paroxysmal epileptic discharges (IPED) in the EEG. This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist visual inspections of human readers. We present a new method, which allows automatic detection of IPED based on discrete wavelet decomposition and a random forest classifier. The algorithm was trained and cross validated using 17 subjects with scalp EEG and 10 subjects with intracranial EEG. The performance of this method reached 62% recall and 26% precision for surface EEG subjects and 63% recall and 53% precision for intracranial EEG subjects. Thus, the method hereby proposed has great potential for diagnosis support in clinical environments.

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Presurgical evaluation of mesial temporal and neocortical focal pharmacoresistant epilepsy patients using intracranial EEG recordings has led to the generation of extensive data on interictal epileptiform discharges, located within or remotely from seizure onset zones. In this study, we used this data to investigate how interictal epileptiform discharges are modulated and how their spatial distribution changes during wake and sleep and analysed the relationship between these discharge events and seizure onset zones. Preoperative evaluation data from 11 adult patients with focal pharmacoresistant epilepsy were extracted from the Epilepsiae database. Interictal epileptiform discharges were automatically detected during wakefulness and over several hours of continuous seizure-free sleep (total duration of EEG recordings:106.7 h; mean per patient: 9.7 h), and analysed across four brain areas (mesial temporal, lateral neocortical, basal cortical and the temporal pole). Sleep stages were classified manually from scalp EEG. Discharge events were characterized according to their rate and morphology (amplitude, sharpness and duration). Eight patients had a seizure onset zone over mesial areas and three patients over lateral neocortical areas. Overall, discharge rates varied across brain areas during wakefulness and sleep [wake/sleep stages × brain areas interaction; Wald χ2(df = 6) = 31.1, P < 0.0001]. N2–N3 non-rapid eye movement sleep increased interictal epileptiform discharges in mesial areas compared with wakefulness and rapid eye movement sleep (P < 0.0001), and to other areas (P < 0.0001 for all comparisons). This mesial pattern was observed both within and outside of seizure onset zones. During wakefulness, the rate of interictal epileptiform discharges was significantly higher than during N2–N3 non-rapid eye movement sleep (P = 0.04), and rapid eye movement sleep (P = 0.01) in lateral neocortical areas (referred to as lateral neocortical pattern), a finding that was more pronounced in seizures onset zones (P = 0.004). The morphological characteristics of the discharge events were modulated during wakefulness and sleep stages across brain areas. The effect of seizure onset zones on discharge morphology was conditioned by brain area and was particularly marked in temporal pole areas. Our analysis of discharge patterns in relation to cerebral localization, vigilance state and the anatomical affiliation of seizure onset zones revealed the global and local aspects of the complex relationship between interictal discharges, sleep and seizure onset zones. This novel approach may lead to a better understanding of cognitive decline and responses to therapy, as well as to adaptation of surgical interventions for epileptic patients.