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      Audio-visual combination of syllables involves time-sensitive dynamics following from fusion failure

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          Abstract

          In face-to-face communication, audio-visual (AV) stimuli can be fused, combined or perceived as mismatching. While the left superior temporal sulcus (STS) is presumably the locus of AV integration, the process leading to combination is unknown. Based on previous modelling work, we hypothesize that combination results from a complex dynamic originating in a failure to integrate AV inputs, followed by a reconstruction of the most plausible AV sequence. In two different behavioural tasks and one MEG experiment, we observed that combination is more time demanding than fusion. Using time-/source-resolved human MEG analyses with linear and dynamic causal models, we show that both fusion and combination involve early detection of AV incongruence in the STS, whereas combination is further associated with enhanced activity of AV asynchrony-sensitive regions (auditory and inferior frontal cortices). Based on neural signal decoding, we finally show that only combination can be decoded from the IFG activity and that combination is decoded later than fusion in the STS. These results indicate that the AV speech integration outcome primarily depends on whether the STS converges or not onto an existing multimodal syllable representation, and that combination results from subsequent temporal processing, presumably the off-line re-ordering of incongruent AV stimuli.

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          The cortical organization of speech processing.

          Despite decades of research, the functional neuroanatomy of speech processing has been difficult to characterize. A major impediment to progress may have been the failure to consider task effects when mapping speech-related processing systems. We outline a dual-stream model of speech processing that remedies this situation. In this model, a ventral stream processes speech signals for comprehension, and a dorsal stream maps acoustic speech signals to frontal lobe articulatory networks. The model assumes that the ventral stream is largely bilaterally organized--although there are important computational differences between the left- and right-hemisphere systems--and that the dorsal stream is strongly left-hemisphere dominant.
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            Brainstorm: A User-Friendly Application for MEG/EEG Analysis

            Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI).
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              Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature.

              Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria. This parcellation is available in the FreeSurfer package. First, a computer-assisted hand parcellation classified each vertex as sulcal or gyral, and these were then subparcellated into 74 labels per hemisphere. Twelve datasets were used to develop rules and algorithms (reported here) that produced labels consistent with anatomical rules as well as automated computational parcellation. The final parcellation was used to build an atlas for automatically labeling the whole cerebral cortex. This atlas was used to label an additional 12 datasets, which were found to have good concordance with manual labels. This paper presents a precisely defined method for automatically labeling the cortical surface in standard terminology. Copyright 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                sophie.l.bouton@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                22 October 2020
                22 October 2020
                2020
                : 10
                : 18009
                Affiliations
                [1 ]GRID grid.8591.5, ISNI 0000 0001 2322 4988, Department of Basic Neuroscience, , University of Geneva, ; Biotech Campus, 9, Chemin des Mines, 1211 Geneva, Switzerland
                [2 ]GRID grid.425274.2, ISNI 0000 0004 0620 5939, Centre de Recherche de l’Institut du Cerveau et de la Moelle Epinière and Centre de Neuro-Imagerie de Recherche, ; 75013 Paris, France
                [3 ]GRID grid.463954.9, ISNI 0000 0004 0384 5295, Laboratoire Dynamique du Langage, CNRS and Université de Lyon UMR 5596, ; 69007 Lyon, France
                [4 ]GRID grid.412188.6, ISNI 0000 0004 0486 8632, Biomedical Signal Processing and Artificial Inteligence Laboratory, , Universidad del Norte, ; Barranquilla, Colombia
                Article
                75201
                10.1038/s41598-020-75201-7
                7583249
                33093570
                6f97bb9e-011b-45ab-999a-ba0c61b00b9e
                © The Author(s) 2020

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 March 2020
                : 5 October 2020
                Funding
                Funded by: Swiss National Science Foundation
                Award ID: SNF P300P1_167591
                Award ID: SNF 320030_149319
                Award Recipient :
                Funded by: Fondation pour l’Audition
                Award ID: RD-2016-5
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                neuroscience,cognitive neuroscience,language
                Uncategorized
                neuroscience, cognitive neuroscience, language

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