The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing

• Published in: NeuroImage (Orlando, Fla.) Vol. 274; p. 120132
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2023; Elsevier Limited; Elsevier
• Subjects: Aphasia; Brain; Brain Mapping; Datasets; Humans; Information processing; Integration; Language; Linguistic performance; Machine learning; Magnetic Resonance Imaging; Nervous system; Neural networks; Phonology; Semantics; Speech; Speech perception; Speech production; Theoretical linguistics; Time; Beijing China; China; dFC; fMRI; DCC; RVR
• DOI: 10.1016/j.neuroimage.2023.120132
• ISSN: 1053-8119

•The framewise language network dynamics in resting state are robustly clustered into four temporal-reoccurring states.•Spatially, the first three dFC states are cognitively meaningful for different processing.•Temporally, the first three states appeared in limited time bins, and state 4 appeared most of the time.•A dynamic “meta-network” framework of language network in resting state is proposed. 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.