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Mapping the Heterogeneous Brain Structural Phenotype of Autism Spectrum Disorder Using the Normative Model

  • Xiaolong Shan
    Affiliations
    Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
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  • Lucina Q. Uddin
    Affiliations
    Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
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  • Jinming Xiao
    Affiliations
    Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
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  • Changchun He
    Affiliations
    Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
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  • Zihan Ling
    Affiliations
    Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
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  • Lei Li
    Affiliations
    Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
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  • Xinyue Huang
    Affiliations
    Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
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  • Huafu Chen
    Affiliations
    Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
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  • Xujun Duan
    Correspondence
    Address correspondence to Xujun Duan, Ph.D.
    Affiliations
    Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
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      Abstract

      Background

      Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by substantial clinical and biological heterogeneity. Quantitative and individualized metrics for delineating the heterogeneity of brain structure in ASD are still lacking. Likewise, the extent to which brain structural metrics of ASD deviate from typical development (TD) and whether deviations can be used for parsing brain structural phenotypes of ASD is unclear.

      Methods

      T1-weighted magnetic resonance imaging data from the Autism Brain Imaging Data Exchange (ABIDE) II (nTD = 564) were used to generate a normative model to map brain structure deviations of ABIDE I subjects (nTD = 560, nASD = 496). Voxel-based morphometry was used to compute gray matter volume. Non-negative matrix factorization was employed to decompose the gray matter matrix into 6 factors and weights. These weights were used for normative modeling to estimate the factor deviations. Then, clustering analysis was used to identify ASD subtypes.

      Results

      Compared with TD, ASD showed increased weights and deviations in 5 factors. Three subtypes with distinct neuroanatomical deviation patterns were identified. ASD subtype 1 and subtype 3 showed positive deviations, whereas ASD subtype 2 showed negative deviations. Distinct clinical manifestations in social communication deficits were identified among the three subtypes.

      Conclusions

      Our findings suggest that individuals with ASD have heterogeneous deviation patterns in brain structure. The results highlight the need to test for subtypes in neuroimaging studies of ASD. This study also presents a framework for understanding neuroanatomical heterogeneity in this increasingly prevalent neurodevelopmental disorder.

      Keywords

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