Autism Spectrum Disorder as Early Neurodevelopmental Disorder: Evidence from the Brain Imaging Abnormalities in 2–3 Years Old Toddlers / Zhou XIAO in Journal of Autism and Developmental Disorders, 44-7 (July 2014)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 44-7 (July 2014) . - p.1633-1640 Titre : Autism Spectrum Disorder as Early Neurodevelopmental Disorder: Evidence from the Brain Imaging Abnormalities in 2–3 Years Old Toddlers Type de document : Texte imprimé et/ou numérique Auteurs : Zhou XIAO, Auteur ; Ting QIU, Auteur ; Xiaoyan KE, Auteur ; Xiang XIAO, Auteur ; Ting XIAO, Auteur ; Fengjing LIANG, Auteur ; Bing ZOU, Auteur ; Haiqing HUANG, Auteur ; Hui FANG, Auteur ; Kangkang CHU, Auteur ; Jiuping ZHANG, Auteur ; Yijun LIU, Auteur Article en page(s) : p.1633-1640 Langues : Anglais (eng) Mots-clés : Autism spectrum disorder  Toddler  Magnetic resonance imaging  Voxel based morphometry  Diffusion tensor imaging Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that occurs within the first 3 years of life, which is marked by social skills and communication deficits along with stereotyped repetitive behavior. Although great efforts have been made to clarify the underlying neuroanatomical abnormalities and brain-behavior relationships in adolescents and adults with ASD, literature is still limited in information about the neurobiology of ASD in the early age of life. Brain images of 50 toddlers with ASD and 28 age, gender, and developmental quotient matched toddlers with developmental delay (DD) (control group) between ages 2 and 3 years were captured using combined magnetic resonance-based structural imaging and diffusion tensor imaging (DTI). Structural magnetic resonance imaging was applied to assess overall gray matter (GM) and white matter (WM) volumes, and regional alterations were assessed by voxel-based morphometry. DTI was used to investigate the white matter tract integrity. Compared with DD, significant increases were observed in ASD, primarily in global GM and WM volumes and in right superior temporal gyrus regional GM and WM volumes. Higher fractional anisotropy value was also observed in the corpus callosum, posterior cingulate cortex, and limbic lobes of ASD. The converging findings of structural and white matter abnormalities in ASD suggest that alterations in neural-anatomy of different brain regions may be involved in behavioral and cognitive deficits associated with ASD, especially in an early age of 2–3 years old toddlers. En ligne : http://dx.doi.org/10.1007/s10803-014-2033-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=236 [article] Autism Spectrum Disorder as Early Neurodevelopmental Disorder: Evidence from the Brain Imaging Abnormalities in 2–3 Years Old Toddlers [Texte imprimé et/ou numérique] / Zhou XIAO, Auteur ; Ting QIU, Auteur ; Xiaoyan KE, Auteur ; Xiang XIAO, Auteur ; Ting XIAO, Auteur ; Fengjing LIANG, Auteur ; Bing ZOU, Auteur ; Haiqing HUANG, Auteur ; Hui FANG, Auteur ; Kangkang CHU, Auteur ; Jiuping ZHANG, Auteur ; Yijun LIU, Auteur . - p.1633-1640. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 44-7 (July 2014) . - p.1633-1640 Mots-clés : Autism spectrum disorder  Toddler  Magnetic resonance imaging  Voxel based morphometry  Diffusion tensor imaging Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that occurs within the first 3 years of life, which is marked by social skills and communication deficits along with stereotyped repetitive behavior. Although great efforts have been made to clarify the underlying neuroanatomical abnormalities and brain-behavior relationships in adolescents and adults with ASD, literature is still limited in information about the neurobiology of ASD in the early age of life. Brain images of 50 toddlers with ASD and 28 age, gender, and developmental quotient matched toddlers with developmental delay (DD) (control group) between ages 2 and 3 years were captured using combined magnetic resonance-based structural imaging and diffusion tensor imaging (DTI). Structural magnetic resonance imaging was applied to assess overall gray matter (GM) and white matter (WM) volumes, and regional alterations were assessed by voxel-based morphometry. DTI was used to investigate the white matter tract integrity. Compared with DD, significant increases were observed in ASD, primarily in global GM and WM volumes and in right superior temporal gyrus regional GM and WM volumes. Higher fractional anisotropy value was also observed in the corpus callosum, posterior cingulate cortex, and limbic lobes of ASD. The converging findings of structural and white matter abnormalities in ASD suggest that alterations in neural-anatomy of different brain regions may be involved in behavioral and cognitive deficits associated with ASD, especially in an early age of 2–3 years old toddlers. En ligne : http://dx.doi.org/10.1007/s10803-014-2033-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=236

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

Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder / Yun JIAO in Journal of Autism and Developmental Disorders, 42-6 (June 2012)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 42-6 (June 2012) . - p.971-983 Titre : Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder Type de document : Texte imprimé et/ou numérique Auteurs : Yun JIAO, Auteur ; Rong CHEN, Auteur ; Xiaoyan KE, Auteur ; Lu CHENG, Auteur ; Kangkang CHU, Auteur ; Zuhong LU, Auteur ; Edward H. HERSKOVITS, Auteur Année de publication : 2012 Article en page(s) : p.971-983 Langues : Anglais (eng) Mots-clés : Autism-spectrum disorder  Single-nucleotide polymorphisms  Diagnostic model  Genotype-phenotype analysis  Data mining Index. décimale : PER Périodiques Résumé : Autism is widely believed to be a heterogeneous disorder; diagnosis is currently based solely on clinical criteria, although genetic, as well as environmental, influences are thought to be prominent factors in the etiology of most forms of autism. Our goal is to determine whether a predictive model based on single-nucleotide polymorphisms (SNPs) can predict symptom severity of autism spectrum disorder (ASD). We divided 118 ASD children into a mild/moderate autism group (n = 65) and a severe autism group (n = 53), based on the Childhood Autism Rating Scale (CARS). For each child, we obtained 29 SNPs of 9 ASD-related genes. To generate predictive models, we employed three machine-learning techniques: decision stumps (DSs), alternating decision trees (ADTrees), and FlexTrees. DS and FlexTree generated modestly better classifiers, with accuracy = 67%, sensitivity = 0.88 and specificity = 0.42. The SNP rs878960 in GABRB3 was selected by all models, and was related associated with CARS assessment. Our results suggest that SNPs have the potential to offer accurate classification of ASD symptom severity. En ligne : http://dx.doi.org/10.1007/s10803-011-1327-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=156 [article] Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder [Texte imprimé et/ou numérique] / Yun JIAO, Auteur ; Rong CHEN, Auteur ; Xiaoyan KE, Auteur ; Lu CHENG, Auteur ; Kangkang CHU, Auteur ; Zuhong LU, Auteur ; Edward H. HERSKOVITS, Auteur . - 2012 . - p.971-983. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 42-6 (June 2012) . - p.971-983 Mots-clés : Autism-spectrum disorder  Single-nucleotide polymorphisms  Diagnostic model  Genotype-phenotype analysis  Data mining Index. décimale : PER Périodiques Résumé : Autism is widely believed to be a heterogeneous disorder; diagnosis is currently based solely on clinical criteria, although genetic, as well as environmental, influences are thought to be prominent factors in the etiology of most forms of autism. Our goal is to determine whether a predictive model based on single-nucleotide polymorphisms (SNPs) can predict symptom severity of autism spectrum disorder (ASD). We divided 118 ASD children into a mild/moderate autism group (n = 65) and a severe autism group (n = 53), based on the Childhood Autism Rating Scale (CARS). For each child, we obtained 29 SNPs of 9 ASD-related genes. To generate predictive models, we employed three machine-learning techniques: decision stumps (DSs), alternating decision trees (ADTrees), and FlexTrees. DS and FlexTree generated modestly better classifiers, with accuracy = 67%, sensitivity = 0.88 and specificity = 0.42. The SNP rs878960 in GABRB3 was selected by all models, and was related associated with CARS assessment. Our results suggest that SNPs have the potential to offer accurate classification of ASD symptom severity. En ligne : http://dx.doi.org/10.1007/s10803-011-1327-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=156

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