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Journal of Autism and Developmental Disorders

Gepubliceerd in:

04-01-2022 | Original Paper

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

Auteurs: Johanna Inhyang Kim, Sungkyu Bang, Jin-Ju Yang, Heejin Kwon, Soomin Jang, Sungwon Roh, Seok Hyeon Kim, Mi Jung Kim, Hyun Ju Lee, Jong-Min Lee, Bung-Nyun Kim

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 1/2023

Abstract

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.

vorige artikel Story Comprehension Monitoring Across Visual, Listening, and Written Modalities in Children with and Without Autism Spectrum Disorder

volgende artikel Dyadic Effect of Coping on the Perceived Impact of ASD of Children on Parental Quality of Life: Report from the ELENA Cohort

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Titel: Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data
Auteurs: Johanna Inhyang Kim
Sungkyu Bang
Jin-Ju Yang
Heejin Kwon
Soomin Jang
Sungwon Roh
Seok Hyeon Kim
Mi Jung Kim
Hyun Ju Lee
Jong-Min Lee
Bung-Nyun Kim
Publicatiedatum: 04-01-2022
Uitgeverij: Springer US
Gepubliceerd in: Journal of Autism and Developmental Disorders / Uitgave 1/2023
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-021-05368-z

Bohn Stafleu van Loghum

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Abstract

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