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Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data

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Issue Date
2022-01
Publisher
Kluwer Academic/Plenum Publishers
Citation
Journal of Autism and Developmental Disorders
Abstract
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Multimodal imaging studies targeting preschoolers and low-functioning autism spectrum disorder (ASD) patients are scarce. We applied machine learning classifiers to parameters from T1-weighted MRI and DTI data of 58 children with ASD (age 3–6 years) and 48 typically developing controls (TDC). Classification performance reached an accuracy, sensitivity, and specificity of 88.8%, 93.0%, and 83.8%, respectively. The most prominent features were the cortical thickness of the right inferior occipital gyrus, mean diffusivity of the middle cerebellar peduncle, and nodal efficiency of the left posterior cingulate gyrus. Machine learning-based analysis of MRI data was useful in distinguishing low-functioning ASD preschoolers from TDCs. Combination of T1 and DTI improved classification accuracy about 10%, and large-scale multi-modal MRI studies are warranted for external validation.
ISSN
0162-3257
URI
https://hdl.handle.net/10371/184057
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College of Medicine/School of Medicine (의과대학/대학원)Dept. of Medicine (의학과)Journal Papers (저널논문_의학과)
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