From focal epilepsy to Dravet syndrome – Heterogeneity of the phenotype due to SCN1A mutations of the p.Arg1596 amino acid residue in the Nav1.1 subunit

Objective The aim of this study was to analyze the intra-/interfamilial phenotypic heterogeneity due to variants at the highly evolutionary conservative p.Arg1596 residue in the Nav1.1 subunit. Materials/participants Among patients referred for analysis of the SCN1A gene one recurrent, heritable mutation was found in families enrolled into the study. Probands from those families even clinically diagnosed with atypical Dravet syndrome (DS), generalized epilepsy with febrile seizures plus (GEFS+), and focal epilepsy, had heterozygous p.Arg1596 His/Cys missense substitutions, c.4787G>T and c.4786C>T in the SCN1A gene. Method Full clinical evaluation, including cognitive development, neurological examination, EEGs, MRI was performed in probands and affected family members in developmental age. The whole SCN1A gene sequencing was performed for all probands. The exon 25, where the identified missense substitutions are localized, was directly analyzed for the other family members. Results Mutation of the SCN1A p.1596Arg was identified in three families, in one case substitution p.Arg1596Cys and in two cases p.Arg1596His. Both mutations were previously described as pathogenic and causative for DS, GEFS+ and focal epilepsy. Spectrum of phenotypes among presented families with p.Arg1596 mutations shows heterogeneity ranged from asymptomatic cases, through FS and FS+ to GEFS+/Panayiotopoulos syndrome and epilepsies with and without febrile seizures, and epileptic encephalopathy such as DS. Phenotypes differ among patients displaying both focal and generalized epilepsies. Some patients demonstrated additionally Asperger syndrome and ataxia. Conclusion Clinical picture heterogeneity of the patients carrying mutation of the same residue indicates the involvement of the other factors influencing the SCN1A gene mutations’ penetrance

Decoding working memory-related information from repeated psychophysiological EEG experiments using convolutional and contrastive neural networks

Objective. Extracting reliable information from electroencephalogram (EEG) is difficult because the low signal-to-noise ratio and significant intersubject variability seriously hinder statistical analyses. However, recent advances in explainable machine learning open a new strategy to address this problem. Approach. The current study evaluates this approach using results from the classification and decoding of electrical brain activity associated with information retention. We designed four neural network models differing in architecture, training strategies, and input representation to classify single experimental trials of a working memory task. Main results. Our best models achieved an accuracy (ACC) of 65.29 ± 0.76 and Matthews correlation coefficient of 0.288 ± 0.018, outperforming the reference model trained on the same data. The highest correlation between classification score and behavioral performance was 0.36 (p = 0.0007). Using analysis of input perturbation, we estimated the importance of EEG channels and frequency bands in the task at hand. The set of essential features identified for each network varies. We identified a subset of features common to all models that identified brain regions and frequency bands consistent with current neurophysiological knowledge of the processes critical to attention and working memory. Finally, we proposed sanity checks to examine further the robustness of each model's set of features. Significance. Our results indicate that explainable deep learning is a powerful tool for decoding information from EEG signals. It is crucial to train and analyze a range of models to identify stable and reliable features. Our results highlight the need for explainable modeling as the model with the highest ACC appeared to use residual artifactual activity

Intrathecal Infusion of Autologous Adipose-Derived Regenerative Cells in Autoimmune Refractory Epilepsy: Evaluation of Safety and Efficacy

Objective/Purpose. Evaluation of efficacy and safety of autologous adipose-derived regenerative cells (ADRCs) treatment in autoimmune refractory epilepsy. Patients. Six patients with proven or probable autoimmune refractory epilepsy (2 with Rasmussen encephalitis, 2 with antineuronal autoantibodies in serum, and 2 with possible FIRES) were included in the project with approval of the Bioethics Committee. Method. Intrathecal injection of autologous ADRC acquired through liposuction followed by enzymatic isolation was performed. The procedure was repeated 3 times every 3 months with each patient. Neurological status, brain MRI, cognitive function, and antiepileptic effect were monitored during 12 months. Results. Immediately after the procedure, all patients were in good condition. In some cases, transient mildly elevated body temperature, pain in regions of liposuction, and slight increasing number of seizures during 24 hours were observed. During the next months, some improvements in school, social functioning, and manual performance were observed in all patients. One patient has been seizure free up to the end of trial. In other patients, frequency of seizures was different: from reduced number to the lack of improvement (3-year follow-up). Conclusion. Autologous ADRC therapy may emerge as a promising option for some patients with autoimmune refractory epilepsy. Based on our trial and other clinical data, the therapy appears to be safe and feasible. Antiepileptic efficacy proved to be various; however, some abilities improved in all children. No signs of psychomotor regression were observed during the first year following the treatment