Atypical effective connectivity of thalamo-cortical circuits in autism spectrum disorder / Heng CHEN in Autism Research, 9-11 (November 2016)  

Public ISBD [article]  inAutism Research > 9-11 (November 2016) . - p.1183-1190 Titre : Atypical effective connectivity of thalamo-cortical circuits in autism spectrum disorder Type de document : Texte imprimé et/ou numérique Auteurs : Heng CHEN, Auteur ; Lucina Q. UDDIN, Auteur ; Youxue ZHANG, Auteur ; Xujun DUAN, Auteur ; Huafu CHEN, Auteur Article en page(s) : p.1183-1190 Langues : Anglais (eng) Mots-clés : autism spectrum disorder  thalamus  brain development  granger causality analysis Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is a neurodevelopment disorder characterized by atypical connectivity within and across multiple brain systems. We aimed to explore information transmission from the sensory periphery to information processing centers of the brain across thalamo-cortical circuits in ASD. A large multicenter dataset from the autism brain imaging data exchange was utilized. A thalamus template derived from the Automatic Anatomic Labeling atlas was subdivided into six subregions corresponding to six cortical regions using a “winner-takes-all” strategy. Granger causality analysis (GCA) was then applied to calculate effective connectivity from subregions of the thalamus to the corresponding cortical regions. Results demonstrate reduced effective connectivity from the thalamus to left prefrontal cortex (P?=?0.023), right posterior parietal cortex (P?=?0.03), and bilateral temporal cortex (left: P?=?0.014; right: P?=?0.015) in ASD compared with healthy control (HC) participants. The GCA values of the thalamus-bilateral temporal cortex connections were significantly negatively correlated with communication scores as assessed by the autism diagnostic observation schedule in the ASD group (left: P?=?0.037; right: P?=?0.007). Age-related analyses showed that the strengths of the thalamus-bilateral temporal cortex connections were significantly positively correlated with age in the HC group (left: P?=?0.013; right: P?=?0.016), but not in the ASD group (left: P?=?0.506; right: P?=?0.219). These results demonstrate impaired thalamo-cortical information transmission in ASD and suggest that atypical development of thalamus-temporal cortex connections may relate to communication deficits in the disorder. En ligne : http://dx.doi.org/10.1002/aur.1614 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=297 [article] Atypical effective connectivity of thalamo-cortical circuits in autism spectrum disorder [Texte imprimé et/ou numérique] / Heng CHEN, Auteur ; Lucina Q. UDDIN, Auteur ; Youxue ZHANG, Auteur ; Xujun DUAN, Auteur ; Huafu CHEN, Auteur . - p.1183-1190. Langues : Anglais (eng) in Autism Research > 9-11 (November 2016) . - p.1183-1190 Mots-clés : autism spectrum disorder  thalamus  brain development  granger causality analysis Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is a neurodevelopment disorder characterized by atypical connectivity within and across multiple brain systems. We aimed to explore information transmission from the sensory periphery to information processing centers of the brain across thalamo-cortical circuits in ASD. A large multicenter dataset from the autism brain imaging data exchange was utilized. A thalamus template derived from the Automatic Anatomic Labeling atlas was subdivided into six subregions corresponding to six cortical regions using a “winner-takes-all” strategy. Granger causality analysis (GCA) was then applied to calculate effective connectivity from subregions of the thalamus to the corresponding cortical regions. Results demonstrate reduced effective connectivity from the thalamus to left prefrontal cortex (P?=?0.023), right posterior parietal cortex (P?=?0.03), and bilateral temporal cortex (left: P?=?0.014; right: P?=?0.015) in ASD compared with healthy control (HC) participants. The GCA values of the thalamus-bilateral temporal cortex connections were significantly negatively correlated with communication scores as assessed by the autism diagnostic observation schedule in the ASD group (left: P?=?0.037; right: P?=?0.007). Age-related analyses showed that the strengths of the thalamus-bilateral temporal cortex connections were significantly positively correlated with age in the HC group (left: P?=?0.013; right: P?=?0.016), but not in the ASD group (left: P?=?0.506; right: P?=?0.219). These results demonstrate impaired thalamo-cortical information transmission in ASD and suggest that atypical development of thalamus-temporal cortex connections may relate to communication deficits in the disorder. En ligne : http://dx.doi.org/10.1002/aur.1614 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=297

Developmental prediction modeling based on diffusion tensor imaging uncovering age-dependent heterogeneity in early childhood autistic brain / Yating MING ; Weixing ZHAO ; Rui FENG ; Yuanyue ZHOU ; Lijie WU ; Jia WANG ; Jinming XIAO ; Lei LI ; Xiaolong SHAN ; Jing CAO ; Xiaodong KANG ; Huafu CHEN ; Xujun DUAN in Molecular Autism, 14 (2023)  

Public ISBD [article]  inMolecular Autism > 14 (2023) . - 41 p. Titre : Developmental prediction modeling based on diffusion tensor imaging uncovering age-dependent heterogeneity in early childhood autistic brain Type de document : Texte imprimé et/ou numérique Auteurs : Yating MING, Auteur ; Weixing ZHAO, Auteur ; Rui FENG, Auteur ; Yuanyue ZHOU, Auteur ; Lijie WU, Auteur ; Jia WANG, Auteur ; Jinming XIAO, Auteur ; Lei LI, Auteur ; Xiaolong SHAN, Auteur ; Jing CAO, Auteur ; Xiaodong KANG, Auteur ; Huafu CHEN, Auteur ; Xujun DUAN, Auteur Article en page(s) : 41 p. Langues : Anglais (eng) Mots-clés : Child  Humans  Child, Preschool  Diffusion Tensor Imaging/methods  *Autistic Disorder/diagnostic imaging  Brain/diagnostic imaging  *White Matter/diagnostic imaging  Cluster Analysis Index. décimale : PER Périodiques Résumé : OBJECTIVE: There has been increasing evidence for atypical white matter (WM) microstructure in autistic people, but findings have been divergent. The development of autistic people in early childhood is clouded by the concurrently rapid brain growth, which might lead to the inconsistent findings of atypical WM microstructure in autism. Here, we aimed to reveal the developmental nature of autistic children and delineate atypical WM microstructure throughout early childhood while taking developmental considerations into account. METHOD: In this study, diffusion tensor imaging was acquired from two independent cohorts, containing 91 autistic children and 100 typically developing children (TDC), aged 4-7 years. Developmental prediction modeling using support vector regression based on TDC participants was conducted to estimate the WM atypical development index of autistic children. Then, subgroups of autistic children were identified by using the k-means clustering method and were compared to each other on the basis of demographic information, WM atypical development index, and autistic trait by using two-sample t-test. Relationship of the WM atypical development index with age was estimated by using partial correlation. Furthermore, we performed threshold-free cluster enhancement-based two-sample t-test for the group comparison in WM microstructures of each subgroup of autistic children with the rematched subsets of TDC. RESULTS: We clustered autistic children into two subgroups according to WM atypical development index. The two subgroups exhibited distinct developmental stages and age-dependent diversity. WM atypical development index was found negatively associated with age. Moreover, an inverse pattern of atypical WM microstructures and different clinical manifestations in the two stages, with subgroup 1 showing overgrowth with low level of autistic traits and subgroup 2 exhibiting delayed maturation with high level of autistic traits, were revealed. CONCLUSION: This study illustrated age-dependent heterogeneity in early childhood autistic children and delineated developmental stage-specific difference that ranged from an overgrowth pattern to a delayed pattern. Trial registration This study has been registered at ClinicalTrials.gov (Identifier: NCT02807766) on June 21, 2016 ( https://clinicaltrials.gov/ct2/show/NCT02807766 ). En ligne : https://dx.doi.org/10.1186/s13229-023-00573-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=518 [article] Developmental prediction modeling based on diffusion tensor imaging uncovering age-dependent heterogeneity in early childhood autistic brain [Texte imprimé et/ou numérique] / Yating MING, Auteur ; Weixing ZHAO, Auteur ; Rui FENG, Auteur ; Yuanyue ZHOU, Auteur ; Lijie WU, Auteur ; Jia WANG, Auteur ; Jinming XIAO, Auteur ; Lei LI, Auteur ; Xiaolong SHAN, Auteur ; Jing CAO, Auteur ; Xiaodong KANG, Auteur ; Huafu CHEN, Auteur ; Xujun DUAN, Auteur . - 41 p. Langues : Anglais (eng) in Molecular Autism > 14 (2023) . - 41 p. Mots-clés : Child  Humans  Child, Preschool  Diffusion Tensor Imaging/methods  *Autistic Disorder/diagnostic imaging  Brain/diagnostic imaging  *White Matter/diagnostic imaging  Cluster Analysis Index. décimale : PER Périodiques Résumé : OBJECTIVE: There has been increasing evidence for atypical white matter (WM) microstructure in autistic people, but findings have been divergent. The development of autistic people in early childhood is clouded by the concurrently rapid brain growth, which might lead to the inconsistent findings of atypical WM microstructure in autism. Here, we aimed to reveal the developmental nature of autistic children and delineate atypical WM microstructure throughout early childhood while taking developmental considerations into account. METHOD: In this study, diffusion tensor imaging was acquired from two independent cohorts, containing 91 autistic children and 100 typically developing children (TDC), aged 4-7 years. Developmental prediction modeling using support vector regression based on TDC participants was conducted to estimate the WM atypical development index of autistic children. Then, subgroups of autistic children were identified by using the k-means clustering method and were compared to each other on the basis of demographic information, WM atypical development index, and autistic trait by using two-sample t-test. Relationship of the WM atypical development index with age was estimated by using partial correlation. Furthermore, we performed threshold-free cluster enhancement-based two-sample t-test for the group comparison in WM microstructures of each subgroup of autistic children with the rematched subsets of TDC. RESULTS: We clustered autistic children into two subgroups according to WM atypical development index. The two subgroups exhibited distinct developmental stages and age-dependent diversity. WM atypical development index was found negatively associated with age. Moreover, an inverse pattern of atypical WM microstructures and different clinical manifestations in the two stages, with subgroup 1 showing overgrowth with low level of autistic traits and subgroup 2 exhibiting delayed maturation with high level of autistic traits, were revealed. CONCLUSION: This study illustrated age-dependent heterogeneity in early childhood autistic children and delineated developmental stage-specific difference that ranged from an overgrowth pattern to a delayed pattern. Trial registration This study has been registered at ClinicalTrials.gov (Identifier: NCT02807766) on June 21, 2016 ( https://clinicaltrials.gov/ct2/show/NCT02807766 ). En ligne : https://dx.doi.org/10.1186/s13229-023-00573-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=518

Shared atypical default mode and salience network functional connectivity between autism and schizophrenia / Heng CHEN in Autism Research, 10-11 (November 2017)  

Public ISBD [article]  inAutism Research > 10-11 (November 2017) . - p.1776-1786 Titre : Shared atypical default mode and salience network functional connectivity between autism and schizophrenia Type de document : Texte imprimé et/ou numérique Auteurs : Heng CHEN, Auteur ; Lucina Q. UDDIN, Auteur ; Xujun DUAN, Auteur ; Junjie ZHENG, Auteur ; Zhiliang LONG, Auteur ; Youxue ZHANG, Auteur ; Xiaonan GUO, Auteur ; Yan ZHANG, Auteur ; Jingping ZHAO, Auteur ; Huafu CHEN, Auteur Article en page(s) : p.1776-1786 Langues : Anglais (eng) Mots-clés : schizophrenia  autism spectrum disorder  functional connectivity  multivariate pattern analysis  default mode network  salience network Index. décimale : PER Périodiques Résumé : Schizophrenia and autism spectrum disorder (ASD) are two prevalent neurodevelopmental disorders sharing some similar genetic basis and clinical features. The extent to which they share common neural substrates remains unclear. Resting-state fMRI data were collected from 35 drug-naïve adolescent participants with first-episode schizophrenia (15.6?±?1.8 years old) and 31 healthy controls (15.4?±?1.6 years old). Data from 22 participants with ASD (13.1?±?3.1 years old) and 21 healthy controls (12.9?±?2.9 years old) were downloaded from the Autism Brain Imaging Data Exchange. Resting-state functional networks were constructed using predefined regions of interest. Multivariate pattern analysis combined with multi-task regression feature selection methods were conducted in two datasets separately. Classification between individuals with disorders and controls was achieved with high accuracy (schizophrenia dataset: accuracy?=?83%; ASD dataset: accuracy?=?80%). Shared atypical brain connections contributing to classification were mostly present in the default mode network (DMN) and salience network (SN). These functional connections were further related to severity of social deficits in ASD (p?=?0.002). Distinct atypical connections were also more related to the DMN and SN, but showed different atypical connectivity patterns between the two disorders. These results suggest some common neural mechanisms contributing to schizophrenia and ASD, and may aid in understanding the pathology of these two neurodevelopmental disorders. Autism Res 2017, 10: 1776–1786. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Lay summary Autism spectrum disorder (ASD) and schizophrenia are two common neurodevelopmental disorders which share several genetic and behavioral features. The present study identified common neural mechanisms contributing to ASD and schizophrenia using resting-state functional MRI data. The results may help to understand the pathology of these two neurodevelopmental disorders. En ligne : http://dx.doi.org/10.1002/aur.1834 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=322 [article] Shared atypical default mode and salience network functional connectivity between autism and schizophrenia [Texte imprimé et/ou numérique] / Heng CHEN, Auteur ; Lucina Q. UDDIN, Auteur ; Xujun DUAN, Auteur ; Junjie ZHENG, Auteur ; Zhiliang LONG, Auteur ; Youxue ZHANG, Auteur ; Xiaonan GUO, Auteur ; Yan ZHANG, Auteur ; Jingping ZHAO, Auteur ; Huafu CHEN, Auteur . - p.1776-1786. Langues : Anglais (eng) in Autism Research > 10-11 (November 2017) . - p.1776-1786 Mots-clés : schizophrenia  autism spectrum disorder  functional connectivity  multivariate pattern analysis  default mode network  salience network Index. décimale : PER Périodiques Résumé : Schizophrenia and autism spectrum disorder (ASD) are two prevalent neurodevelopmental disorders sharing some similar genetic basis and clinical features. The extent to which they share common neural substrates remains unclear. Resting-state fMRI data were collected from 35 drug-naïve adolescent participants with first-episode schizophrenia (15.6?±?1.8 years old) and 31 healthy controls (15.4?±?1.6 years old). Data from 22 participants with ASD (13.1?±?3.1 years old) and 21 healthy controls (12.9?±?2.9 years old) were downloaded from the Autism Brain Imaging Data Exchange. Resting-state functional networks were constructed using predefined regions of interest. Multivariate pattern analysis combined with multi-task regression feature selection methods were conducted in two datasets separately. Classification between individuals with disorders and controls was achieved with high accuracy (schizophrenia dataset: accuracy?=?83%; ASD dataset: accuracy?=?80%). Shared atypical brain connections contributing to classification were mostly present in the default mode network (DMN) and salience network (SN). These functional connections were further related to severity of social deficits in ASD (p?=?0.002). Distinct atypical connections were also more related to the DMN and SN, but showed different atypical connectivity patterns between the two disorders. These results suggest some common neural mechanisms contributing to schizophrenia and ASD, and may aid in understanding the pathology of these two neurodevelopmental disorders. Autism Res 2017, 10: 1776–1786. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Lay summary Autism spectrum disorder (ASD) and schizophrenia are two common neurodevelopmental disorders which share several genetic and behavioral features. The present study identified common neural mechanisms contributing to ASD and schizophrenia using resting-state functional MRI data. The results may help to understand the pathology of these two neurodevelopmental disorders. En ligne : http://dx.doi.org/10.1002/aur.1834 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=322

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