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揭示自闭症特征与主要功能连接组之间的多变量关联

Jong-Eun Lee^1,2, Kyoungseob Byeon³, Sunghun Kim^2,4

¹Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea.

Neuroinformatics

|April 1, 2025

概括

这项研究揭示了大脑网络模式 (梯度) 如何与自闭症谱系障碍 (ASD) 的社会,行为和认知挑战有关. 这些发现支持理解ASD的维度方法.

科学领域:

神经成像是一种神经成像.
发育神经科学的发展神经科学.
精神病学是一个精神病学.

背景情况:

自闭症谱系障碍 (ASD) 呈现异质特征,需要超越分类诊断的维度方法.
以前对ASD的神经成像研究的重点是单变量分析,限制了对大脑网络复杂性的理解 (连接病态).

研究的目的:

调查高维神经成像特征与多种自闭症表型之间的多变量关联.
探索功能性大脑连接梯度与ASD的社会,行为和认知问题的关系.

主要方法:

利用了309名参与者的休息状态功能磁共振成像 (fMRI) 数据 (168名患有自闭症,141名对照).
生成了低维的功能连接表示 (梯度) 并应用了稀疏的正规相关性分析 (SCCA).
评估了三种已识别的皮层梯度和社会,行为和认知问题的表型措施之间的关联.

主要成果:

确定了三个关键的功能梯度,代表不同的皮层组织模式.
发现了特定梯度和ASD表型的相关维度之间的显著多变量关联:社会障碍,内化/外化问题和元认知问题.
结果在独立验证数据集中部分复制,证实了可靠性.

结论:

高维神经成像和表型数据的多变量分析为ASD提供了一个有希望的维度方法.
确定了相关的维度,为ASD异质性的神经生物学基础提供了新的见解.
这种方法可以提高对自闭症谱系障碍的诊断和理解.

关键词:

自闭症谱系障碍自闭症谱系障碍功能梯度梯度是指功能梯度的梯度.多变量分析多变量分析.稀少的正规相关性分析分析.

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