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.
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California.
Biol Psychiatry. 2022 Jun 1;91(11):967-976. doi: 10.1016/j.biopsych.2022.01.011. Epub 2022 Jan 31.
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 (n = 564) were used to generate a normative model to map brain structure deviations of ABIDE I subjects (n = 560, n = 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.
自闭症谱系障碍（ASD）是一种复杂的神经发育障碍，具有显著的临床和生物学异质性。目前仍缺乏用于描绘 ASD 中脑结构异质性的定量和个体化指标。同样，ASD 的脑结构指标与典型发育（TD）的偏离程度，以及这些偏离是否可用于解析 ASD 的脑结构表型尚不清楚。
利用自闭症脑影像数据交换（ABIDE）二期（ABIDE II）（n=564）的 T1 加权磁共振成像数据，生成一个规范模型，以映射 ABIDE I 期（n=560）和 ABIDE II 期（n=496）的大脑结构偏差。体素形态计量学用于计算灰质体积。非负矩阵分解用于将灰质矩阵分解为 6 个因子和权重。这些权重用于规范建模以估计因子偏差。然后，聚类分析用于识别 ASD 亚型。
与 TD 相比，ASD 显示 5 个因子的权重和偏差增加。确定了 3 种具有明显神经解剖偏差模式的亚型。ASD 亚型 1 和亚型 3 表现出正偏差，而 ASD 亚型 2 表现出负偏差。在这 3 种亚型中，识别出社会沟通缺陷的不同临床表现。
我们的发现表明，ASD 患者的大脑结构存在异质性的偏差模式。结果强调了在 ASD 的神经影像学研究中需要对亚型进行测试。这项研究还为理解这种日益流行的神经发育障碍的神经解剖学异质性提供了一个框架。
