Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity.

ABSTRACT: Autism spectrum disorders (ASDs) are heterogeneous neurodevelopmental conditions. In fMRI studies, including most machine learning studies seeking to distinguish ASD from typical developing (TD) samples, cohorts differing in gender and symptom severity composition are often treated statistically as one "ASD group". Using resting-state functional connectivity (FC) data, we implemented random forest to build diagnostic classifiers in 4 ASD samples including a total of 656 participants (N_ASD = 306, N_TD = 350, ages 6-18). Groups were manipulated to titrate heterogeneity of gender and symptom severity and partially overlapped. Each sample differed on inclusionary criteria: (1) all genders, unrestricted severity range; (2) only male participants, unrestricted severity; (3)

SUBMITTER: Reiter MA 

PROVIDER: S-EPMC8210842 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature