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      Evidence for a Spoken Word Lexicon in the Auditory Ventral Stream

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          Abstract

          The existence of a neural representation for whole words (i.e., a lexicon) is a common feature of many models of speech processing. Prior studies have provided evidence for a visual lexicon containing representations of whole written words in an area of the ventral visual stream known as the visual word form area. Similar experimental support for an auditory lexicon containing representations of spoken words has yet to be shown. Using functional magnetic resonance imaging rapid adaptation techniques, we provide evidence for an auditory lexicon in the auditory word form area in the human left anterior superior temporal gyrus that contains representations highly selective for individual spoken words. Furthermore, we show that familiarization with novel auditory words sharpens the selectivity of their representations in the auditory word form area. These findings reveal strong parallels in how the brain represents written and spoken words, showing convergent processing strategies across modalities in the visual and auditory ventral streams.

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          Most cited references70

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          Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks.

          Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.
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            A component based noise correction method (CompCor) for BOLD and perfusion based fMRI.

            A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.
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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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                Author and article information

                Contributors
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                Journal
                Neurobiol Lang (Camb)
                Neurobiol Lang (Camb)
                nol
                Neurobiology of Language
                MIT Press (One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA journals-info@mit.edu )
                2641-4368
                2023
                20 July 2023
                : 4
                : 3
                : 420-434
                Affiliations
                [1]Department of Neuroscience, Georgetown University Medical Center, Washington, DC, USA
                [2]Department of Speech, Language, and Hearing Sciences, San Diego State University, San Diego, CA, USA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                * Corresponding Author: srd49@ 123456georgetown.edu

                Handling Editor: Sophie Scott

                Author information
                https://orcid.org/0000-0002-4164-5812
                https://orcid.org/0000-0001-7695-8002
                Article
                nol_a_00108
                10.1162/nol_a_00108
                10426387
                471df713-7344-4cd3-9fd4-39a1adc8ec5a
                © 2023 Massachusetts Institute of Technology

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/.

                History
                : 12 December 2022
                : 27 April 2023
                Page count
                Pages: 15
                Funding
                Funded by: National Science Foundation, DOI 10.13039/100000001;
                Award ID: BCS-1756313
                Award Recipient :
                Funded by: National Science Foundation, DOI 10.13039/100000001;
                Award ID: ACI-1548562
                Award Recipient :
                Funded by: Foundation for the National Institutes of Health, DOI 10.13039/100000009;
                Award ID: 1S10OD023561
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                Damera, S. R., Chang, L., Nikolov, P. P., Mattei, J. A., Banerjee, S., Glezer, L. S., Cox, P. H., Jiang, X., Rauschecker, J. P., & Riesenhuber, M. (2023). Evidence for a spoken word lexicon in the auditory ventral stream. Neurobiology of Language, 4(3), 420–434. https://doi.org/10.1162/nol_a_00108

                auditory lexicon,auditory ventral stream,speech recognition,superior temporal gyrus

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