A network clustering based feature selection strategy for classifying autism spectrum disorder

Lingkai Tang, Sakib Mostafa, Bo Liao, Fang-Xiang Wu
2019 BMC Medical Genomics  
Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance.
doi:10.1186/s12920-019-0598-0 pmid:31888621 pmcid:PMC6936069 fatcat:ikbvwjpdkbeshblsmtsf27coey