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

Xiaolong Shan; Lucina Q Uddin; Jinming Xiao; Changchun He; Zihan Ling; Lei Li; Xinyue Huang; Huafu Chen; Xujun Duan

doi:10.1016/j.biopsych.2022.01.011

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

Biol Psychiatry. 2022 Jun 1;91(11):967-976. doi: 10.1016/j.biopsych.2022.01.011. Epub 2022 Jan 31.

Authors

Xiaolong Shan¹, Lucina Q Uddin², Jinming Xiao¹, Changchun He¹, Zihan Ling¹, Lei Li¹, Xinyue Huang¹, Huafu Chen¹, Xujun Duan³

Affiliations

¹ 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.
² Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California.
³ 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. Electronic address: duanxujun@uestc.edu.cn.

PMID: 35367047
DOI: 10.1016/j.biopsych.2022.01.011

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 (n_TD = 564) were used to generate a normative model to map brain structure deviations of ABIDE I subjects (n_TD = 560, n_ASD = 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: Deviation; Gray matter volume; Neuroanatomical heterogeneity; Neurodevelopmental disorders; Non-negative matrix factorization.

MeSH terms

Autism Spectrum Disorder* / diagnostic imaging
Brain / diagnostic imaging
Brain Mapping
Gray Matter / diagnostic imaging
Humans
Magnetic Resonance Imaging / methods
Neuroimaging
Phenotype

Grants and funding

R01 MH107549/MH/NIMH NIH HHS/United States