New research has discovered distinct subtypes of autism spectrum disorder (ASD) based on brain activity and behavior, according to a study by Weill Cornell Medicine investigators. By using machine learning to analyze neuroimaging data, the researchers identified patterns of brain connections associated with different behavioral traits in individuals with ASD. These findings offer new insights into the condition and could lead to improved diagnosis and personalized treatments for ASD.
ASD is a complex neurodevelopmental disorder characterized by difficulties in social interaction, communication, and repetitive behaviors. The study aimed to determine if there are different subgroups within ASD and to understand the underlying genetic pathways. By integrating neuroimaging data with gene expression and proteomics, the researchers identified four clinically distinct groups of individuals with ASD, each exhibiting unique brain connection patterns and behavioral characteristics.