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Epilepsies have numerous specific mechanisms. The understanding of neural dynamics leading to seizures is important for disclosing pathological mechanisms and developing therapeutic approaches. We investigated electrographic activities and neural dynamics leading to convulsive seizures in patients and mouse models of Dravet syndrome (DS), a developmental and epileptic encephalopathy in which hypoexcitability of GABAergic neurons is considered to be the main dysfunction. We analyzed EEGs from DS patients carrying a SCN1A pathogenic variant, as well as epidural electrocorticograms, hippocampal local field potentials, and hippocampal single-unit neuronal activities in Scn1a+/- and Scn1aRH/+ DS mice. Strikingly, most seizures had low-voltage-fast onset in both patients and mice, which is thought to be generated by hyperactivity of GABAergic interneurons, the opposite of the main pathological mechanism of DS. Analyzing single-unit recordings, we observed that temporal disorganization of the firing of putative interneurons in the period immediately before the seizure (preictal) precedes the increase of their activity at seizure onset, together with the entire neuronal network. Moreover, we found early signatures of the preictal period in the spectral features of hippocampal and cortical field potential of Scn1a mice and of patients' EEG, which are consistent with the dysfunctions that we observed in single neurons and that allowed seizure prediction. Therefore, the perturbed preictal activity of interneurons leads to their hyperactivity at the onset of generalized seizures, which have low-voltage-fast features that are similar to those observed in other epilepsies and are triggered by hyperactivity of GABAergic neurons. Preictal spectral features may be used as predictive seizure biomarkers. [ABSTRACT FROM AUTHOR]

Objective: This study was undertaken to evaluate our treatment algorithm for infantile epileptic spasms syndrome (IESS) used between 2000 and 2018. We initiated vigabatrin (VGB), and steroids were added if the electroclinical response (spasms and electroencephalogram [EEG]) to VGB was not obtained or incomplete. Methods: Individuals with IESS treated with VGB were recruited from our hospital clinical data warehouse based on electronic health records (EHRs) generated since 2009 and containing relevant keywords. We confirmed the diagnosis of IESS. Clinical, EEG, imaging, and biological data were extracted from the EHRs. We analyzed factors associated with short‐term response, time to response, relapse, time to relapse of spasms, and the presence of spasms at last follow‐up. Results: We collected data from 198 individuals (female: 46.5%, IESS onset: 6 [4.5–10.3] months, follow‐up: 4.6 [2.5–7.6] years, median [Q1–Q3]) including 129 (65.2%) with identifiable etiology. VGB was started 17 (5–57.5) days after IESS diagnosis. A total of 113 individuals were responders (57.1% of the cohort), 64 with VGB alone and 38 with VGB further combined with steroids (56.6% and 33.6% of responders, respectively). Among responders, 33 (29%) experienced relapses of spasms, mostly those with later onset of spasms (p =.002) and those who received VGB for <24 months after spasms cessation compared to a longer duration on VGB (45% vs. 12.8%, p =.003). At follow‐up, 92 individuals were seizure‐free (46.5% of the whole cohort), including 26 free of therapy (13.1%). One hundred twelve individuals (56.6%) were still receiving VGB, with a duration of 3.2 (1.75–5.7) years. Significance: Our sequential protocol introducing VGB then adding steroids is an effective alternative to a combined VGB–steroids approach in IESS. It avoids steroid‐related adverse events, as well as those from VGB–steroid combination. According to our data, a period of 7 days seems sufficient to assess VGB response and enables the addition of steroids rapidly if needed. Continuing VGB for 2 years may balance the risk of relapse and treatment‐induced adverse events. [ABSTRACT FROM AUTHOR]

We aimed to identify caregivers' opinions on the outcome measures that matter in clinical trials in individuals with Dravet syndrome (DS). We conducted a prospective European multicenter study based on an 11 closed questions survey developed by the French reference center for rare epilepsies and DS patients' advocacy groups. Items included questions on seizures and daily life outcomes that a clinical trial on a therapy for individuals with DS should target. Statistical analyses were performed to evaluate the impact of the country of residence and of the patients' age. The survey was answered by 153 caregivers (68%: France, 28%: Germany, and 24%: Italy) off individuals with DS. Individuals with DS included 86 males (mean age of 11.4 [interquartile: 7‐20.4] years). Families ranked as important almost all the items proposed. However, items related to daily life had the highest rank in all three countries compared to items about seizures (P = 0.02). Increase in individuals' age was associated with a higher age at diagnosis (ρ = 0.26, P = 0.02), and a lower impact of seizure duration (ρ = −0.25, P = 0.005) and on the need of hospital referral (ρ = −0.26, P = 0.005). These data can help tailor patient‐centered outcome measures in future clinical and real‐life trials for DS. [ABSTRACT FROM AUTHOR]

We propose an instructive figure that summarized the classification of epilepsy syndromes according to the 2022 report of the ILAE Task Force on Nosology and Definitions. Our aim is to present on the same figure different concepts such as the names of epilepsy syndromes, their extreme and classical ages of onset, their epilepsy types (generalized, focal, or generalized and focal) but also their membership in groups of epilepsy syndromes as for self‐limited or developmental and epileptic encephalopathies. With this figure, we provide an interactive tool, as supplementary data, helping to present this classification and link it to electro‐clinical mandatory, alerts, and exclusionary criteria of each syndrome, in accordance with the ILAE position papers on syndromes classification and nosology. This report may be used as an illustrative tool for teaching epilepsy syndromes and as a practical and comprehensive aid for the classification of epilepsy individuals' syndromes. [ABSTRACT FROM AUTHOR]

Background: The growing use of Electronic Health Records (EHRs) is promoting the application of data mining in health-care. A promising use of big data in this field is to develop models to support early diagnosis and to establish natural history. Dravet Syndrome (DS) is a rare developmental and epileptic encephalopathy that commonly initiates in the first year of life with febrile seizures (FS). Age at diagnosis is often delayed after 2 years, as it is difficult to differentiate DS at onset from FS. We aimed to explore if some clinical terms (concepts) are significantly more used in the electronic narrative medical reports of individuals with DS before the age of 2 years compared to those of individuals with FS. These concepts would allow an earlier detection of patients with DS resulting in an earlier orientation toward expert centers that can provide early diagnosis and care. Methods: Data were collected from the Necker Enfants Malades Hospital using a document-based data warehouse, Dr Warehouse, which employs Natural Language Processing, a computer technology consisting in processing written information. Using Unified Medical Language System Meta-thesaurus, phenotype concepts can be recognized in medical reports. We selected individuals with DS (DS Cohort) and individuals with FS (FS Cohort) with confirmed diagnosis after the age of 4 years. A phenome-wide analysis was performed evaluating the statistical associations between the phenotypes of DS and FS, based on concepts found in the reports produced before 2 years and using a series of logistic regressions. Results: We found significative higher representation of concepts related to seizures' phenotypes distinguishing DS from FS in the first phases, namely the major recurrence of complex febrile convulsions (long-lasting and/or with focal signs) and other seizure-types. Some typical early onset non-seizure concepts also emerged, in relation to neurodevelopment and gait disorders. Conclusions: Narrative medical reports of individuals younger than 2 years with FS contain specific concepts linked to DS diagnosis, which can be automatically detected by software exploiting NLP. This approach could represent an innovative and sustainable methodology to decrease time of diagnosis of DS and could be transposed to other rare diseases. [ABSTRACT FROM AUTHOR]

Summary: Objective: We aimed to characterize epilepsy of infancy with migrating focal seizures (EIMFS), a rare, severe early onset developmental epilepsy related to KCNT1 mutation, and to define specific electroencephalography (EEG) markers using EEG quantitative analysis. The ultimate goal would be to improve early diagnosis and to better understand seizure onset and propagation of EIMFS as compared to other early onset developmental epilepsy. Methods: EEG of 7 EIMFS patients with KCNT1 mutations (115 seizures) and 17 patients with other early onset epilepsies (30 seizures) was included in this study. After detection of seizure onset and termination, spatiotemporal characteristics were quantified. Seizure propagation dynamics were analyzed using chronograms and phase coherence. Results: In patients with EIMFS, seizures started and were localized predominantly in temporal and occipital areas, and evolved with a stable frequency (4‐10 Hz). Inter‐ and intrahemispheric migrations were present in 60% of EIMFS seizures with high intraindividual reproducibility of temporospatial dynamics. Interhemispheric migrating seizures spread in 71% from temporal or occipital channels to the homologous contralateral ones, whereas intrahemispheric seizures involved mainly frontotemporal, temporal, and occipital channels. Causality links were present between ictal activities detected under different channels during migrating seizures. Finally, time delay index (based on delays between the different ictal onsets) and phase correlation index (based on coherence of ictal activities) allowed discrimination of EIMFS and non‐EIMFS seizures with a specificity of 91.2% and a sensitivity of 84.4%. Significance: We showed that the migrating pattern in EIMFS is not a random process, as suggested previously, and that it is a particular propagation pattern that follows the classical propagation pathways. It is notable that this study reveals specific EEG markers (time delay and phase correlation) accessible to visual evaluation, which will improve EIMFS diagnosis. [ABSTRACT FROM AUTHOR]