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bioRxiv · 10.1101/2022.03.31.486594

Unraveling the functional attributes of the language connectome: crucial subnetworks, flexibility and variability.

Abstract

Language processing is a highly integrative function, intertwining linguistic operations (processing the language code intentionally used for communication) and extra-linguistic processes (e.g., attention monitoring, predictive inference, long-term memory). This synergetic cognitive architecture requires a distributed and specialized neural substrate. Brain systems have mostly been examined at rest. However, task-related functional connectivity provides additional and valuable information about how information is processed when various cognitive states are involved. We gathered thirteen language fMRI tasks in a unique database of one hundred and fifty neurotypical adults (InLang database). The tasks were designed to assess a wide range of linguistic processes and subprocesses. From this database, we applied network theory as a computational tool to model the task-related functional connectome of language (LANG). The organization of this data-driven neurocognitive atlas of language is examined at multiple levels, uncovering its major components (or crucial subnetworks) and its anatomical and functional correlates. Furthermore, we estimate its reconfiguration as a function of linguistic demand (flexibility), or several factors such as age or gender (variability). By accounting for the multifaceted nature of language and modulating factors, this study can contribute to enrich and refine existing neurocognitive models of language. The LANG atlas can also be considered as a reference for comparative or clinical studies, involving a variety of patients and conditions.

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BibTeXRIS

Roger, E., Rodrigues de Almeida, L., Loevenbruck, H., Perrone-Bertolotti, M., Cousin, E., Schwartz, J.-L., Perrier, P., Dohen, M., Vilain, A., Baraduc, P., Achard, S., Baciu, M.. 2022-04-01. Unraveling the functional attributes of the language connectome: crucial subnetworks, flexibility and variability.. https://doi.org/10.1101/2022.03.31.486594

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