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 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] / 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

Developmental Shift From Intrinsic Hyper- to Hypo-Connectivity Occurring at Pre-Adolescence in Autism Spectrum Disorder / Xiaolong SHAN in Autism Research, 18-11 (November 2025)  

Public ISBD [article]  inAutism Research > 18-11 (November 2025) . - p.2254-2264 Titre : Developmental Shift From Intrinsic Hyper- to Hypo-Connectivity Occurring at Pre-Adolescence in Autism Spectrum Disorder Type de document : texte imprimé Auteurs : Xiaolong SHAN, Auteur ; Ya LI, Auteur ; Jinming XIAO, Auteur ; Xiaotian WANG, Auteur ; Xinyue HUANG, Auteur ; Lei LI, Auteur ; Yu FENG, Auteur ; Weixin ZHAO, Auteur ; Huafu CHEN, Auteur ; Xujun DUAN, Auteur Article en page(s) : p.2254-2264 Langues : Anglais (eng) Mots-clés : autism spectrum disorder  developmental shift  functional connectivity  hyper-connectivity  hypo-connectivity Index. décimale : PER Périodiques Résumé : ABSTRACT Accumulating evidence suggests that hyper-connectivity is more characteristic of young children with autism spectrum disorder (ASD), while hypo-connectivity begins to emerge in adolescence and persists into adulthood. Despite increasing efforts being invested to explore the altered functional connectivity in ASD, the timing of the shift from intrinsic hyper-to hypo-connectivity of large-scale brain functional networks remains unclear. Here, we systematically depict the development of intrinsic functional connectivity in 800 participants from the Autism Brain Imaging Data Exchange. We first use independent component analyses to identify the large-scale brain functional networks. Then, we utilize the locally estimated scatterplot smoothing algorithm to fit the developmental trajectory of brain functional networks. Finally, we develop a ?sliding threshold? method to detect the age stage at which the shift from hyper- to hypo-connectivity occurs in ASD. We identify six large-scale brain functional networks, including the default mode network (DMN), fronto-parietal network (FPN), salience network (SAN), auditory network (AN), somatomotor network (SMN), and visual network (VN). We find that primary networks (AN, SMN, and VN) undergo the shift from hyper- to hypo-connectivity earlier than high-order networks (DMN, FPN, and SAN) in ASD. At pre-adolescence, the SMN, AN, VN, DMN, SAN, and FPN undergo the shift from hyper- to hypo-connectivity in sequence in ASD. Our findings shed light on the age-related changes of intrinsic functional connectivity in ASD, highlighting the need for conceptualizing functional connectivity in ASD from a developmental perspective. En ligne : https://doi.org/10.1002/aur.70117 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=571 [article] Developmental Shift From Intrinsic Hyper- to Hypo-Connectivity Occurring at Pre-Adolescence in Autism Spectrum Disorder [texte imprimé] / Xiaolong SHAN, Auteur ; Ya LI, Auteur ; Jinming XIAO, Auteur ; Xiaotian WANG, Auteur ; Xinyue HUANG, Auteur ; Lei LI, Auteur ; Yu FENG, Auteur ; Weixin ZHAO, Auteur ; Huafu CHEN, Auteur ; Xujun DUAN, Auteur . - p.2254-2264. Langues : Anglais (eng) in Autism Research > 18-11 (November 2025) . - p.2254-2264 Mots-clés : autism spectrum disorder  developmental shift  functional connectivity  hyper-connectivity  hypo-connectivity Index. décimale : PER Périodiques Résumé : ABSTRACT Accumulating evidence suggests that hyper-connectivity is more characteristic of young children with autism spectrum disorder (ASD), while hypo-connectivity begins to emerge in adolescence and persists into adulthood. Despite increasing efforts being invested to explore the altered functional connectivity in ASD, the timing of the shift from intrinsic hyper-to hypo-connectivity of large-scale brain functional networks remains unclear. Here, we systematically depict the development of intrinsic functional connectivity in 800 participants from the Autism Brain Imaging Data Exchange. We first use independent component analyses to identify the large-scale brain functional networks. Then, we utilize the locally estimated scatterplot smoothing algorithm to fit the developmental trajectory of brain functional networks. Finally, we develop a ?sliding threshold? method to detect the age stage at which the shift from hyper- to hypo-connectivity occurs in ASD. We identify six large-scale brain functional networks, including the default mode network (DMN), fronto-parietal network (FPN), salience network (SAN), auditory network (AN), somatomotor network (SMN), and visual network (VN). We find that primary networks (AN, SMN, and VN) undergo the shift from hyper- to hypo-connectivity earlier than high-order networks (DMN, FPN, and SAN) in ASD. At pre-adolescence, the SMN, AN, VN, DMN, SAN, and FPN undergo the shift from hyper- to hypo-connectivity in sequence in ASD. Our findings shed light on the age-related changes of intrinsic functional connectivity in ASD, highlighting the need for conceptualizing functional connectivity in ASD from a developmental perspective. En ligne : https://doi.org/10.1002/aur.70117 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=571

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