Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data.
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). Classi...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 53; no. 1; pp. 25 - 38 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161607305&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161607305 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Jan2023 vid: 53 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161607305 154482127 161607305 161607305 10.1007/s10803-021-05368-z 161607305 ppf: 25 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data. aug: au: Kim, Johanna Inhyang Bang, Sungkyu Yang, Jin-Ju Kwon, Heejin Jang, Soomin Roh, Sungwon Kim, Seok Hyeon Kim, Mi Jung Lee, Hyun Ju Lee, Jong-Min Kim, Bung-Nyun affil: Department of Psychiatry, Hanyang University Medical Center, 222-1 Wangsimni-ro, Sungdong-gu, 04763, Seoul, Republic of Korea sug: subj: Autism Spectrum Disorder Diagnosis Autism Spectrum Disorder Classification Magnetic Resonance Imaging Methods Diagnosis, Computer Assisted Image Processing, Computer Assisted Machine Learning Neuroradiography Human Child, Preschool Sensitivity and Specificity Descriptive Statistics Prefrontal Cortex Comparative Studies Funding Source Child Child, Preschool: 2-5 years Child: 6-12 years ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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