Yuan Binke, Xie Hui, Wang Zhihao, Xu Yangwen, Zhang Hanqing, Liu Jiaxuan, Chen Lifeng, Li Chaoqun, Tan Shiyao, Lin Zonghui, Hu Xin, Gu Tianyi, Lu Junfeng, Liu Dongqiang, Wu Jinsong
Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China.
Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China; Department of Psychology, The University of Hong Kong, Hong Kong, China.
Neuroimage. 2023 Jul 1;274:120132. doi: 10.1016/j.neuroimage.2023.120132. Epub 2023 Apr 25.
Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.
现代语言学理论和网络科学提出，语言和言语处理被组织成层次化、分离的大规模子网，其核心是背侧（语音）流和腹侧（语义）流。在接受性和表达性语言或言语任务中，这两个流被不对称地调用，表现出灵活的功能分离和整合。我们假设这两个流的功能分离是由潜在的网络分离所支持的。采用动态条件相关方法构建逐帧时变语言网络，并采用k均值聚类来研究时间上反复出现的模式。我们发现，静息状态下的逐帧语言网络动态被稳健地聚类为四种状态，这些状态以域分离的方式动态重新配置。在空间上，前三种状态的枢纽分布分别与语音感知和词汇语音处理、言语产生以及语义处理的神经生物学高度相似。第四种状态的特点是功能连接最弱，被视为基线状态。在时间上，前三种状态仅出现在有限的时间间隔内（约15%），而在大多数时间（>55%），状态4占主导地位。基于机器学习的动态功能连接-语言学预测分析表明，四种状态的动态功能连接显著预测了个体的语言表现。这些发现表明静息状态下语言网络动态的域分离方式，它形成了一个动态的“元网络”框架，以支持语言和言语处理过程中的灵活功能分离和整合。
