Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data / Johanna Inhyang KIM in Journal of Autism and Developmental Disorders, 53-1 (January 2023)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 53-1 (January 2023) . - p.25-37 Titre : Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data Type de document : Texte imprimé et/ou numérique Auteurs : Johanna Inhyang KIM, Auteur ; Sungkyu BANG, Auteur ; Jin-Ju YANG, Auteur ; Heejin KWON, Auteur ; Soomin JANG, Auteur ; Sungwon ROH, Auteur ; Seok Hyeon KIM, Auteur ; Mi Jung KIM, Auteur ; Hyun Ju LEE, Auteur ; Jong-Min LEE, Auteur ; Bung-Nyun KIM, Auteur Article en page(s) : p.25-37 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : 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. En ligne : https://doi.org/10.1007/s10803-021-05368-z Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=492 [article] Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data [Texte imprimé et/ou numérique] / Johanna Inhyang KIM, Auteur ; Sungkyu BANG, Auteur ; Jin-Ju YANG, Auteur ; Heejin KWON, Auteur ; Soomin JANG, Auteur ; Sungwon ROH, Auteur ; Seok Hyeon KIM, Auteur ; Mi Jung KIM, Auteur ; Hyun Ju LEE, Auteur ; Jong-Min LEE, Auteur ; Bung-Nyun KIM, Auteur . - p.25-37. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 53-1 (January 2023) . - p.25-37 Index. décimale : PER Périodiques Résumé : 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. En ligne : https://doi.org/10.1007/s10803-021-05368-z Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=492

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