自闭症谱系障碍
自闭症
神经影像学
体素
心理学
大脑大小
神经发育障碍
遗传异质性
规范性
磁共振成像
神经科学
表型
医学
发展心理学
生物
计算机科学
遗传学
人工智能
哲学
放射科
认识论
基因
作者
Xiaolong Shan,Lucina Q. Uddin,Jinming Xiao,Changchun He,Zihan Ling,Lei Li,Xinyue Huang,Huafu Chen,Xujun Duan
标识
DOI:10.1016/j.biopsych.2022.01.011
摘要
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.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.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.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.
科研通智能强力驱动
Strongly Powered by AbleSci AI