Lu Huibin, Wang Sha, Gao Le, Xue Zaifa, Liu Jing, Niu Xiaoxia, Zhou Rongjuan, Guo Xiaonan
School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China.
Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, 066004, China.
Brain Imaging Behav. 2025 Feb;19(1):124-137. doi: 10.1007/s11682-024-00957-9. Epub 2024 Nov 20.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder accompanied by structural and functional changes in the brain. However, the relationship between brain structure and function in children with ASD remains largely obscure. In the current study, parallel independent component analysis (pICA) was performed to identify inter-modality associations by drawing on information from different modalities. Structural and resting-state functional magnetic resonance imaging data from 105 children with ASD and 102 typically developing children (obtained from the open-access Autism Brain Imaging Data Exchange database) were combined through the pICA framework. Features of structural and functional modalities were represented by the voxel-based morphometry (VBM) and amplitude of low-frequency fluctuations (ALFF), respectively. The relationship between the structural and functional components derived from the pICA was investigated by Pearson's correlation analysis, and between-group differences in these components were analyzed through the two-sample t-test. Finally, multivariate support vector regression analysis was used to analyze the relationship between the structural/functional components and Autism Diagnostic Observation Schedule (ADOS) subscores in the ASD group. This study found a significant association between VBM and ALFF components in ASD. Significant between-group differences were detected in the loading coefficients of the VBM component. Furthermore, the ALFF component loading coefficients predicted the subscores of communication and repetitive stereotypic behaviors of the ADOS. Likewise, the VBM component loading coefficients predicted the ADOS communication subscore in ASD. These findings provide evidence of a link between brain function and structure, yielding new insights into the neural mechanisms of ASD.
自闭症谱系障碍（ASD）是一种神经发育障碍，伴有大脑结构和功能的变化。然而，ASD儿童大脑结构与功能之间的关系在很大程度上仍不清楚。在本研究中，通过利用来自不同模态的信息，进行了并行独立成分分析（pICA）以识别模态间关联。通过pICA框架，将来自105名ASD儿童和102名发育正常儿童（数据取自开放获取的自闭症脑成像数据交换数据库）的结构和静息态功能磁共振成像数据进行了合并。结构和功能模态的特征分别由基于体素的形态学测量（VBM）和低频波动幅度（ALFF）表示。通过Pearson相关分析研究了pICA得出的结构和功能成分之间的关系，并通过两样本t检验分析了这些成分在组间的差异。最后，使用多变量支持向量回归分析来分析ASD组中结构/功能成分与自闭症诊断观察量表（ADOS）子分数之间的关系。本研究发现ASD中VBM和ALFF成分之间存在显著关联。在VBM成分的载荷系数中检测到显著的组间差异。此外，ALFF成分载荷系数预测了ADOS中沟通和重复刻板行为的子分数。同样，VBM成分载荷系数预测了ASD中ADOS的沟通子分数。这些发现为大脑功能与结构之间的联系提供了证据，为ASD的神经机制带来了新的见解。
