Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning

• Published in: Autism research Vol. 17; no. 10; p. 1962
• Main Authors: Xu, Guomei, Geng, Guohong, Wang, Ankang, Li, Zhangyong, Liu, Zhichao, Liu, Yanping, Hu, Jun, Wang, Wei, Li, Xinwei
• Format: Journal Article
• Language: English
• Published: United States 01.10.2024
• Subjects: Adolescent; Autism Spectrum Disorder - classification; Autism Spectrum Disorder - diagnostic imaging; Autism Spectrum Disorder - physiopathology; Brain - diagnostic imaging; Brain - physiopathology; Case-Control Studies; Child; Gray Matter - diagnostic imaging; Humans; Magnetic Resonance Imaging - methods; Male; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Supervised Machine Learning; Young Adult; gray matter network; autism spectrum disorders; semi‐supervised machine learning; graph theory; heterogeneity

Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter brain networks and provide new insights from a graph...