Summary Objective GABRA1 mutations have been identified in patients with familial juvenile myoclonic epilepsy, sporadic childhood absence epilepsy, and idiopathic familial generalized epilepsy. In addition, de novo GABRA1 mutations were recently reported in a patient with infantile spasms and four patients with Dravet syndrome. Those reports suggest that GABRA1 mutations are associated with infantile epilepsy including early onset epileptic encephalopathies. In this study, we searched for GABRA1 mutations in patients with infantile epilepsy to investigate the phenotypic spectrum of GABRA1 mutations. Methods In total, 526 and 145 patients with infantile epilepsy were analyzed by whole‐exome sequencing and GABRA1‐targeted resequencing, respectively. Results We identified five de novo missense GABRA1 mutations in six unrelated patients. A p.R112Q mutation in the long extracellular N‐terminus was identified in a patient with infantile epilepsy; p.P260L, p.M263T, and p.M263I in transmembrane spanning domain 1 (TM1) were identified in three unrelated patients with West syndrome and a patient with Ohtahara syndrome, respectively; and p.V287L in TM2 was identified in a patient with unclassified early onset epileptic encephalopathy. Four of these mutations have not been observed previously. Significance Our study suggests that de novo GABRA1 mutations can cause early onset epileptic encephalopathies, including Ohtahara syndrome and West syndrome.

MI during interictal recording may provide useful information for the prediction of postoperative seizure outcome.

Researchers have looked for rapidly- and objectively-measurable electrophysiology biomarkers that accurately localize the epileptogenic zone. Promising candidates include interictal high-frequency oscillation and phase-amplitude coupling. Investigators have independently created the toolboxes that compute the high-frequency oscillation rate and the severity of phase-amplitude coupling. This study of 135 patients determined what toolboxes and analytic approaches would optimally classify patients achieving postoperative seizure control. Four different detector toolboxes computed the rate of high-frequency oscillation at ≥ 80 Hz at intracranial EEG channels. Another toolbox calculated the modulation index reflecting the strength of phase-amplitude coupling between high-frequency oscillation and slow-wave at 3-4 Hz. We defined the completeness of resection of interictally-abnormal regions as the subtraction of high-frequency oscillation rate (or modulation index) averaged across all preserved sites from that averaged across all resected sites. We computed the outcome classification accuracy of the logistic regression-based standard model considering clinical, ictal intracranial EEG, and neuroimaging variables alone. We then determined how well the incorporation of high-frequency oscillation/modulation index would improve the standard model mentioned above. To assess the anatomical variability across nonepileptic sites, we generated the normative atlas of detector-specific high-frequency oscillation and modulation index. Each atlas allowed us to compute the statistical deviation of high-frequency oscillation/modulation index from the nonepileptic mean. We determined whether the model accuracy would be improved by incorporating absolute or normalized high-frequency oscillation/modulation index as a biomarker assessing interictally-abnormal regions. We finally determined whether the model accuracy would be improved by selectively incorporating high-frequency oscillation verified to have high-frequency oscillatory components unattributable to a high-pass filtering effect. Ninety-five patients achieved successful seizure control, defined as International League Against Epilepsy class 1 outcome. Multivariate logistic regression analysis demonstrated that complete resection of interictally-abnormal regions additively increased the chance of success. The model accuracy was further improved by incorporating z-score normalized high-frequency oscillation/modulation index or selective incorporation of verified high-frequency oscillation. The standard model had a classification accuracy of 0.75. Incorporation of normalized high-frequency oscillation/modulation index or verified high-frequency oscillation improved the classification accuracy up to 0.82. These outcome prediction models survived the cross-validation process and demonstrated an agreement between the model-based likelihood of success and the observed success on an individual basis. Interictal high-frequency oscillation and modulation index had a comparably additive utility in epilepsy presurgical evaluation. Our empirical data support the theoretical notion that the prediction of postoperative seizure outcomes can be optimized with the consideration of both interictal and ictal abnormalities.