Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder / Xiang XIAO in Autism Research, 10-4 (April 2017)  

Public ISBD [article]  inAutism Research > 10-4 (April 2017) . - p.620-630 Titre : Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder Type de document : Texte imprimé et/ou numérique Auteurs : Xiang XIAO, Auteur ; Hui FANG, Auteur ; Jiansheng WU, Auteur ; ChaoYong XIAO, Auteur ; Ting XIAO, Auteur ; Lu QIAN, Auteur ; Fengjing LIANG, Auteur ; Zhou XIAO, Auteur ; Kangkang CHU, Auteur ; Xiaoyan KE, Auteur Article en page(s) : p.620-630 Langues : Anglais (eng) Mots-clés : autism spectrum disorder  toddler  magnetic resonance imaging  cortical thickness  predictive model Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder mainly showed atypical social interaction, communication, and restricted, repetitive patterns of behavior, interests and activities. Now clinic diagnosis of ASD is mostly based on psychological evaluation, clinical observation and medical history. All these behavioral indexes could not avoid defects such as subjectivity and reporter-dependency. Therefore researchers devoted themselves to seek relatively stable biomarkers of ASD as supplementary diagnostic evidence. The goal of present study is to generate relatively stable predictive model based on anatomical brain features by using machine learning technique. Forty-six ASD children and thirty-nine development delay children aged from 18 to 37 months were evolved in. As a result, the predictive model generated by regional average cortical thickness of regions with top 20 highest importance of random forest classifier showed best diagnostic performance. And random forest was proved to be the optimal approach for neuroimaging data mining in small size set and thickness-based classification outperformed volume-based classification and surface area-based classification in ASD. The brain regions selected by the models might attract attention and the idea of considering biomarkers as a supplementary evidence of ASD diagnosis worth exploring. En ligne : http://dx.doi.org/10.1002/aur.1711 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=307 [article] Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder [Texte imprimé et/ou numérique] / Xiang XIAO, Auteur ; Hui FANG, Auteur ; Jiansheng WU, Auteur ; ChaoYong XIAO, Auteur ; Ting XIAO, Auteur ; Lu QIAN, Auteur ; Fengjing LIANG, Auteur ; Zhou XIAO, Auteur ; Kangkang CHU, Auteur ; Xiaoyan KE, Auteur . - p.620-630. Langues : Anglais (eng) in Autism Research > 10-4 (April 2017) . - p.620-630 Mots-clés : autism spectrum disorder  toddler  magnetic resonance imaging  cortical thickness  predictive model Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder mainly showed atypical social interaction, communication, and restricted, repetitive patterns of behavior, interests and activities. Now clinic diagnosis of ASD is mostly based on psychological evaluation, clinical observation and medical history. All these behavioral indexes could not avoid defects such as subjectivity and reporter-dependency. Therefore researchers devoted themselves to seek relatively stable biomarkers of ASD as supplementary diagnostic evidence. The goal of present study is to generate relatively stable predictive model based on anatomical brain features by using machine learning technique. Forty-six ASD children and thirty-nine development delay children aged from 18 to 37 months were evolved in. As a result, the predictive model generated by regional average cortical thickness of regions with top 20 highest importance of random forest classifier showed best diagnostic performance. And random forest was proved to be the optimal approach for neuroimaging data mining in small size set and thickness-based classification outperformed volume-based classification and surface area-based classification in ASD. The brain regions selected by the models might attract attention and the idea of considering biomarkers as a supplementary evidence of ASD diagnosis worth exploring. En ligne : http://dx.doi.org/10.1002/aur.1711 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=307

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