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bioRxiv · 10.1101/2022.06.19.496753

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

Yuan, B.·Xie, H.·Wang, Z.·Xu, Y.·Zhang, H.·Liu, J.·Chen, L.·Li, C.·Tan, S.·Lin, Z.·Hu, X.·Gu, T.·Lu, J.·Liu, D.-Q.·Wu, J.
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Abstract

Modern linguistic theories and network science propose that the language and speech processing is 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 frame-wise time-varying language networks and investigate the temporal reoccurring patterns. We found that the time-varying language networks in the resting-state robustly clustered into four low-dimensional states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of primary auditory processing and lexical-phonological processing, motor and speech production processing, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and subserved as a baseline state. Temporally, the first three states appeared exclusively in limited time bins ([~]15%), and most of the time (> 55%), the language network kept inactive in state 4. 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 the resting-state, which forms a dynamic "meta-networking" (network of networks) framework. HighlightsO_LIThe time-varying language network in the resting-state is robustly clustered into four low-dimensional states. C_LIO_LISpatially, the first three dFC states are cognitively meaningful, which highly resemble the neurobiology of primary auditory processing and lexical-phonological representation, speech production processing, and semantic processing, respectively. C_LIO_LITemporally, the first three states appeared exclusively in limited time bins ([~]15%), and most of the time (> 55%), the language network kept inactive in state 4. C_LIO_LIA dynamic "meta-networking" framework of language network in the resting-state is proposed. C_LI

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Yuan, B., Xie, H., Wang, Z., Xu, Y., Zhang, H., Liu, J., Chen, L., Li, C., Tan, S., Lin, Z., Hu, X., Gu, T., Lu, J., Liu, D.-Q., Wu, J.. 2022-06-20. The domain-separation low-dimensional language network dynamics in the resting-state support the flexible functional segregation and integration during language and speech processing. https://doi.org/10.1101/2022.06.19.496753

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