Research Article: Electroencephalographic abnormalities and clinical phenotypes in children with autism spectrum disorder: a single center cohort study
Abstract:
Electroencephalographic (EEG) abnormalities are frequently observed in children with autism spectrum disorder (ASD), even in the absence of clinical seizures. However, the clinical significance of different EEG patterns in ASD remains incompletely understood.
To investigate associations between EEG abnormalities and selected clinical characteristics in children with ASD.
This study analyzed medical records of 180 children with ASD hospitalized at the Pediatric Neurology Department at the Upper Silesian Child Health Center in Katowice. Patients were stratified by epilepsy diagnosis and EEG characteristics (normal, non-paroxysmal changes, paroxysmal changes). Clinical variables analyzed included developmental milestones, intellectual disability severity, sleep disturbances, hyperactivity, sensory integration disorders, aggressive behaviors, and motor deficits. Statistical analysis employed Mann–Whitney U test, Kruskal-Wallis test, and Fisher’s exact test as appropriate.
Sleep disorders showed significant association with EEG pattern type ( p =?0.041), occurring most frequently in patients with non-paroxysmal changes (20%) compared to those with paroxysmal changes (5.9%) and normal recordings (7%). Children with comorbid epilepsy demonstrated significantly higher rates and severity of intellectual disability compared to those without epilepsy ( p =?0.004 and p =?0.007, respectively). Paroxysmal abnormalities were more prevalent in the epilepsy group (62% versus 38%, p =?0.01). After adjusting for age, no significant associations were found between epilepsy diagnosis or EEG abnormalities and speech delay, aggression, sensory integration disorders, or motor deficits.
Non-paroxysmal EEG abnormalities may represent a distinct neurophysiological correlate of sleep disorders in children with ASD. Comorbid epilepsy is strongly associated with intellectual disability severity, supporting the need for comprehensive neurological evaluation in this population. While broad categorical EEG patterns did not reveal significant associations with most clinical manifestations in our sample, more granular EEG analysis may detect subtle correlations not apparent with our simplified classification approach.
Introduction:
Electroencephalographic (EEG) abnormalities are frequently observed in children with autism spectrum disorder (ASD), even in the absence of clinical seizures. However, the clinical significance of different EEG patterns in ASD remains incompletely understood.
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