Subtyping Autism Spectrum Disorder With a Population Graph-Based Dual Autoencoder: Revealing Two Distinct Biotypes.

ABSTRACT:

Aim

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by significant heterogeneity in clinical symptoms and underlying neurobiology. This study aimed to identify distinct ASD biotypes and uncover their neurobiological underpinnings using a novel graph-based subtyping approach.

Methods

Resting-state fMRI and clinical data from 443 males with ASD (17.22 ± 8.63 years) were analyzed. We proposed a population graph-based dual autoencoder for subtyping (PG-DAS), a deep clustering framework that integrates imaging data and nonimaging data to extract deep features for biotype identification. Statistical analyses were conducted to compare clinical scores and functional connectivity patterns between biotypes. Correlation analyses examined the associations b

SUBMITTER: Li X 

PROVIDER: S-EPMC12675305 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature