The Clinical Hospital of Chengdu Brain Science Institute, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, PR China.
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China.
Cereb Cortex. 2021 Jul 5;31(8):3899-3910. doi: 10.1093/cercor/bhab057.
Much recent attention has been directed toward elucidating the structure of social interaction-communication dimensions and whether and how these symptom dimensions coalesce with each other in individuals with autism spectrum disorder (ASD). However, the underlying neurobiological basis of these symptom dimensions is unknown, especially the association of social interaction and communication dimensions with brain networks. Here, we proposed a method of whole-brain network-based regression to identify the functional networks linked to these symptom dimensions in a large sample of children with ASD. Connectome-based predictive modeling (CPM) was established to explore neurobiological evidence that supports the merging of communication and social interaction deficits into one symptom dimension (social/communication deficits). Results showed that the default mode network plays a core role in communication and social interaction dimensions. A primary sensory perceptual network mainly contributed to communication deficits, and high-level cognitive networks mainly contributed to social interaction deficits. CPM revealed that the functional networks associated with these symptom dimensions can predict the merged dimension of social/communication deficits. These findings delineate a link between brain functional networks and symptom dimensions for social interaction and communication and further provide neurobiological evidence supporting the merging of communication and social interaction deficits into one symptom dimension.
最近人们对社会互动-交流维度的结构以及这些症状维度在自闭症谱系障碍(ASD)个体中彼此融合的方式和程度给予了高度关注。然而,这些症状维度的潜在神经生物学基础尚不清楚,特别是社会互动和交流维度与大脑网络的关联。在这里,我们提出了一种基于全脑网络回归的方法,以在大量 ASD 儿童样本中识别与这些症状维度相关的功能网络。基于连接组的预测模型(CPM)用于探索支持将交流和社会互动缺陷合并为一个症状维度(社交/交流缺陷)的神经生物学证据。结果表明,默认模式网络在交流和社会互动维度中起核心作用。主要感觉知觉网络主要导致交流缺陷,高级认知网络主要导致社会互动缺陷。CPM 揭示了与这些症状维度相关的功能网络可以预测社交/交流缺陷的合并维度。这些发现描绘了大脑功能网络与社会互动和交流症状维度之间的联系,并进一步提供了支持将交流和社会互动缺陷合并为一个症状维度的神经生物学证据。