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Electro-Clinical Features and Functional Connectivity Analysis in SYN1-Related Epilepsy.
Ann Neurol
August 2024
Neurology Department, University Hospital, Saint-Etienne, France.
Objective: There is currently scarce data on the electroclinical characteristics of epilepsy associated with synapsin 1 (SYN1) pathogenic variations. We examined clinical and electro-encephalographic (EEG) features in patients with epilepsy and SYN1 variants, with the aim of identifying a distinctive electroclinical pattern.
Methods: In this retrospective multicenter study, we collected and reviewed demographic, genetic, and epilepsy data of 19 male patients with SYN1 variants.
A topological classifier to characterize brain states: When shape matters more than variance.
PLoS One
October 2023
Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Catalonia, Spain.
Despite the remarkable accuracies attained by machine learning classifiers to separate complex datasets in a supervised fashion, most of their operation falls short to provide an informed intuition about the structure of data, and, what is more important, about the phenomena being characterized by the given datasets. By contrast, topological data analysis (TDA) is devoted to study the shape of data clouds by means of persistence descriptors and provides a quantitative characterization of specific topological features of the dataset under scrutiny. Here we introduce a novel TDA-based classifier that works on the principle of assessing quantifiable changes on topological metrics caused by the addition of new input to a subset of data.
View Article and Find Full Text PDFShank3 influences mammalian sleep development.
J Neurosci Res
December 2022
Department of Translational Medicine and Physiology, Sleep and Performance Research Center, Elson S. Floyd College of Medicine, Washington State University, Spokane, Washington, USA.
Sleep problems are prevalent in autism spectrum disorder (ASD), can be observed before diagnosis, and are associated with increased restricted and repetitive behaviors. Therefore, sleep abnormalities may be a core feature of the disorder, but the developmental trajectory remains unknown. Animal models provide a unique opportunity to understand sleep ontogenesis in ASD.
View Article and Find Full Text PDFA MACHINE LEARNING-BASED APPROACH TO EPILEPTIC SEIZURE PREDICTION USING ELECTRO-ENCEPHALOGRAPHIC SIGNALS.
J Eng Res (Ponta Grossa)
January 2022
Center for Collaborative Research in Health, Disparities - RCMI Program, University of Puerto Rico Medical Sciences, Campus, San Juan, Puerto Rico.
The brain is made up of billions of neurons, which control all actions performed by us. In epilepsy, the pattern order of brain signals is altered, causing epileptiform discharges in an individual's brain. Approximately 1% of the world population has epilepsy and, therefore, there is a need for studies that can help in the diagnosis and treatment of this disorder.
View Article and Find Full Text PDFAutomated Analysis of Risk Factors for Postictal Generalized EEG Suppression.
Front Neurol
May 2021
National Institute of Neurological Disorders and Stroke (NINDS) Center for Sudden Unexpected Death in Epilepsy (SUDEP) Research, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States.
Currently, there is some ambiguity over the role of postictal generalized electro-encephalographic suppression (PGES) as a biomarker in sudden unexpected death in epilepsy (SUDEP). Visual analysis of PGES, known to be subjective, may account for this. In this study, we set out to perform an analysis of PGES presence and duration using a validated signal processing tool, specifically to examine the association between PGES and seizure features previously reported to be associated with visually analyzed PGES.
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