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      Bilateral human laryngeal motor cortex in perceptual decision of lexical tone and voicing of consonant

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          Abstract

          Speech perception is believed to recruit the left motor cortex. However, the exact role of the laryngeal subregion and its right counterpart in speech perception, as well as their temporal patterns of involvement remain unclear. To address these questions, we conducted a hypothesis-driven study, utilizing transcranial magnetic stimulation on the left or right dorsal laryngeal motor cortex (dLMC) when participants performed perceptual decision on Mandarin lexical tone or consonant (voicing contrast) presented with or without noise. We used psychometric function and hierarchical drift-diffusion model to disentangle perceptual sensitivity and dynamic decision-making parameters. Results showed that bilateral dLMCs were engaged with effector specificity, and this engagement was left-lateralized with right upregulation in noise. Furthermore, the dLMC contributed to various decision stages depending on the hemisphere and task difficulty. These findings substantially advance our understanding of the hemispherical lateralization and temporal dynamics of bilateral dLMC in sensorimotor integration during speech perceptual decision-making.

          Abstract

          The role of the laryngeal motor cortex (LMC) in speech perception is poorly understood. Here, using transcranial magnetic stimulation, the authors found a causal contribution of bilateral LMC to consonant and lexical tone perception.

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          The assessment and analysis of handedness: The Edinburgh inventory

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            BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics

            The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).
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              Bayesian measures of model complexity and fit

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                Author and article information

                Contributors
                duyi@psych.ac.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                5 August 2023
                5 August 2023
                2023
                : 14
                : 4710
                Affiliations
                [1 ]GRID grid.9227.e, ISNI 0000000119573309, Institute of Psychology, CAS Key Laboratory of Behavioral Science, , Chinese Academy of Sciences, ; Beijing, 100101 China
                [2 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, Department of Psychology, , University of Chinese Academy of Sciences, ; Beijing, 100049 China
                [3 ]GRID grid.507732.4, CAS Center for Excellence in Brain Science and Intelligence Technology, ; Shanghai, 200031 China
                [4 ]GRID grid.510934.a, ISNI 0000 0005 0398 4153, Chinese Institute for Brain Research, ; Beijing, 102206 China
                Author information
                http://orcid.org/0000-0003-2604-9120
                http://orcid.org/0000-0003-1741-8596
                http://orcid.org/0000-0003-4512-5221
                Article
                40445
                10.1038/s41467-023-40445-0
                10404239
                37543659
                73288631-3303-4a3c-a279-d8aeed855e88
                © Springer Nature Limited 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 January 2023
                : 27 July 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 31822024
                Award Recipient :
                Funded by: STI 2030—Major Projects 2021ZD0201500, the Strategic Priority Research Program of Chinese Academy of Sciences XDB32010300, and the Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences E2CX3625CX.
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                decision,perception
                Uncategorized
                decision, perception

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