ABSTRACT
The clinical phenotype of autism spectrum disorder and epilepsy (ASD-E) is a common neurological presentation in various genetic disorders, irrespective of the underlying pathophysiological mechanisms. Here we describe the demographic and clinical profiles, coexistent neurological conditions, type of seizures, epilepsy syndrome, and EEG findings in 11 patients with ASD-E phenotype with proven genetic etiology. The commonest genetic abnormality noted was CDKL5 mutation (3), MECP2 mutation (2), and 1p36 deletion (2). The median age of onset of clinical seizures was 6 months (range, 10 days to 11 years). The most common seizure type was focal onset seizures with impaired awareness, observed in 7 (63.6%) patients followed by epileptic spasms in 4 (30.8%), generalized tonic-clonic and atonic seizures in 3 (27.3%) patients each and tonic seizures in 2 (18.2%) patients and myoclonic seizures in 1 (9.1%) patient. Focal and multifocal interictal epileptiform abnormalities were seen in 6 (54.6%) and 5 (45.5%) patients, respectively. Epileptic encephalopathy and focal epilepsy were seen in 7 (63.6%) and 4 (36.4%) patients, respectively. The diagnostic yield of genetic testing was 44% (11 of 25 patients) and when variants of unknown significance and metabolic defects were included, the yield increased to 60% (15 of 25 patients). We conclude that in patients with ASD-E phenotype with an underlying genetic basis, the clinical seizure type, epilepsy syndrome, and EEG patterns are variable. Next-generation exome sequencing and chromosomal microarray need to be considered in clinical practice as part of evaluation of children with ASD-E phenotype.
Subject(s)
Autism Spectrum Disorder , Epilepsy , Autism Spectrum Disorder/diagnosis , Autism Spectrum Disorder/genetics , Child , Child, Preschool , Electroencephalography , Epilepsy/diagnosis , Epilepsy/genetics , Genetic Profile , Humans , Infant , Infant, Newborn , PhenotypeABSTRACT
BACKGROUND AND PURPOSE: Multiparametric MRI generates different zones within the lesion that may reflect heterogeneity of tissue damage in cerebral ischemia. This study presents the application of a novel model of tissue characterization based on an angular separation between tissues obtained with the use of an objective (unsupervised) computer segmentation algorithm implementing a modified version of the Iterative Self-Organizing Data Analysis Technique (ISODATA). We test the utility of this model to identify ischemic tissue in clinical stroke. METHODS: MR parameters diffusion-, T2-, and T1-weighted imaging (DWI, T2WI, and T1WI, respectively) were obtained from 10 patients at 3 time points (30 studies) after stroke: acute (
