揭示自闭症特征与主要功能连接体之间的多变量关联。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jong-Eun Lee, Kyoungseob Byeon, Sunghun Kim, Bo-Yong Park, Hyunjin Park
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引用次数: 0

摘要

自闭症谱系障碍(ASD)是一种以一系列行为和认知特征为特征的多方面神经发育疾病。由于自闭症谱系障碍的特征在个体间具有高度的异质性,在揭示自闭症谱系障碍的症状学时,首选的方法是克服分类方法的局限性的维度方法。先前的神经影像学研究表明,大规模大脑网络与自闭症表型之间存在密切联系。然而,现有的研究主要集中在单变量关联分析,这限制了我们对自闭症连接病的理解。使用来自309名参与者(168名ASD患者和141名正常发展的对照组)的静息状态功能磁共振成像数据,通过发现数据集和两个独立的验证数据集,我们确定了高维神经成像特征与多种表型测量(20或7个测量)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)评估了它们与自闭症相关的社会、行为和认知问题表型的多变量关联。我们选择了三个功能梯度,分别代表大脑的感觉-跨模式、运动-视觉和多重需求休息的皮质轴。SCCA揭示了梯度和表型测量之间的多变量关联,这被称为关联维度。我们确定了三个相互关联的维度:(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。我们的发现在两个独立的验证数据集中部分重复,表明稳健性。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希望的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Revealing the Multivariate Associations Between Autistic Traits and Principal Functional Connectome.

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.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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