Lee Jong-Eun, Byeon Kyoungseob, Kim Sunghun, Park Bo-Yong, Park Hyunjin
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea.
Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
Neuroinformatics. 2025 Apr 1;23(2):27. doi: 10.1007/s12021-025-09720-x.
Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition characterized by a spectrum of behavioral and cognitive traits. As the characteristics of ASD are highly heterogeneous across individuals, a dimensional approach that overcomes the limitation of the categorical approach is preferred to reveal the symptomatology of ASD. Previous neuroimaging studies demonstrated strong links between large-scale brain networks and autism phenotypes. However, the existing studies have primarily focused on univariate association analysis, which limits our understanding of autism connectopathy. Using resting-state functional magnetic resonance imaging data from 309 participants (168 individuals with ASD and 141 typically developing controls) across a discovery dataset and two independent validation datasets, we identified multivariate associations between high-dimensional neuroimaging features and diverse phenotypic measures (20 or 7 measures). We generated low-dimensional representations of functional connectivity (i.e., gradients) and assessed their multivariate associations with autism-related phenotypes of social, behavioral, and cognitive problems using sparse canonical correlation analysis (SCCA). We selected three functional gradients that represented the cortical axes of the sensory-transmodal, motor-visual, and multiple demand-rests of the brain. The SCCA revealed multivariate associations between gradients and phenotypic measures, which were noted as linked dimensions. We identified three linked dimensions: the links between (1) the first gradient and social impairment, (2) the second and internalizing/externalizing problems, and (3) the third and metacognitive problems. Our findings were partially replicated in two independent validation datasets, indicating robustness. Multivariate association analysis linking high-dimensional neuroimaging and phenotypic features may offer promising avenues for establishing a dimensional approach to autism diagnosis.
自闭症谱系障碍（ASD）是一种多方面的神经发育疾病，其特征表现为一系列行为和认知特征。由于自闭症谱系障碍的特征在个体之间高度异质，因此一种能够克服分类方法局限性的维度方法更适合用于揭示自闭症谱系障碍的症状学。先前的神经影像学研究表明，大规模脑网络与自闭症表型之间存在紧密联系。然而，现有研究主要集中在单变量关联分析上，这限制了我们对自闭症连接病的理解。我们使用来自309名参与者（168名自闭症谱系障碍个体和141名发育正常的对照）的静息态功能磁共振成像数据，跨越一个发现数据集和两个独立的验证数据集，确定了高维神经影像学特征与多种表型测量（20种或7种测量）之间的多变量关联。我们生成了功能连接的低维表示（即梯度），并使用稀疏典型相关分析（SCCA）评估了它们与社会、行为和认知问题等自闭症相关表型的多变量关联。我们选择了三个功能梯度，它们代表了大脑的感觉跨模态、运动视觉以及多种需求-静息的皮质轴。典型相关分析揭示了梯度与表型测量之间的多变量关联，这些关联被视为关联维度。我们确定了三个关联维度：（1）第一个梯度与社会障碍之间的联系，（2）第二个梯度与内化/外化问题之间的联系，以及（3）第三个梯度与元认知问题之间的联系。我们的研究结果在两个独立的验证数据集中得到了部分重复，表明了其稳健性。将高维神经影像学和表型特征联系起来的多变量关联分析可能为建立自闭症诊断的维度方法提供有前景的途径。
