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      Neural speech restoration at the cocktail party: Auditory cortex recovers masked speech of both attended and ignored speakers

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          Abstract

          Humans are remarkably skilled at listening to one speaker out of an acoustic mixture of several speech sources. Two speakers are easily segregated, even without binaural cues, but the neural mechanisms underlying this ability are not well understood. One possibility is that early cortical processing performs a spectrotemporal decomposition of the acoustic mixture, allowing the attended speech to be reconstructed via optimally weighted recombinations that discount spectrotemporal regions where sources heavily overlap. Using human magnetoencephalography (MEG) responses to a 2-talker mixture, we show evidence for an alternative possibility, in which early, active segregation occurs even for strongly spectrotemporally overlapping regions. Early (approximately 70-millisecond) responses to nonoverlapping spectrotemporal features are seen for both talkers. When competing talkers’ spectrotemporal features mask each other, the individual representations persist, but they occur with an approximately 20-millisecond delay. This suggests that the auditory cortex recovers acoustic features that are masked in the mixture, even if they occurred in the ignored speech. The existence of such noise-robust cortical representations, of features present in attended as well as ignored speech, suggests an active cortical stream segregation process, which could explain a range of behavioral effects of ignored background speech.

          Abstract

          How do humans focus on one speaker when several are talking? MEG responses to a continuous two-talker mixture suggest that, even though listeners attend only to one of the talkers, their auditory cortex tracks acoustic features from both speakers. This occurs even when those features are locally masked by the other speaker.

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          An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

          In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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            FreeSurfer.

            FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.
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              Nonparametric statistical testing of EEG- and MEG-data.

              In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                22 October 2020
                October 2020
                22 October 2020
                : 18
                : 10
                : e3000883
                Affiliations
                [1 ] Institute for Systems Research, University of Maryland, College Park, Maryland, United States of America
                [2 ] Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
                [3 ] Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
                [4 ] Department of Biology, University of Maryland, College Park, Maryland, United States of America
                Universidad de Salamanca, SPAIN
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-8380-639X
                https://orcid.org/0000-0002-1472-2659
                https://orcid.org/0000-0003-0858-0698
                Article
                PBIOLOGY-D-20-00156
                10.1371/journal.pbio.3000883
                7644085
                33091003
                6b257b93-3bb6-44a3-a6a8-ac29972b72f3
                © 2020 Brodbeck et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 January 2020
                : 14 September 2020
                Page count
                Figures: 5, Tables: 0, Pages: 22
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01-DC014085
                Award Recipient :
                Funded by: University of Maryland
                Award ID: Seed Grant
                Award Recipient :
                This work was supported by a National Institutes of Health grant R01-DC014085 (to JZS; https://www.nih.gov) and by a University of Maryland Seed Grant (to LEH and JZS; https://umd.edu). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Physics
                Acoustics
                Engineering and Technology
                Signal Processing
                Speech Signal Processing
                Social Sciences
                Linguistics
                Speech
                Physical Sciences
                Physics
                Acoustics
                Bioacoustics
                Biology and Life Sciences
                Bioacoustics
                Biology and Life Sciences
                Physiology
                Sensory Physiology
                Auditory System
                Auditory Cortex
                Biology and Life Sciences
                Neuroscience
                Sensory Systems
                Auditory System
                Auditory Cortex
                Biology and Life Sciences
                Anatomy
                Brain
                Auditory Cortex
                Medicine and Health Sciences
                Anatomy
                Brain
                Auditory Cortex
                Biology and Life Sciences
                Neuroscience
                Brain Mapping
                Magnetoencephalography
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Magnetoencephalography
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Magnetoencephalography
                Physical Sciences
                Physics
                Acoustics
                Acoustic Signals
                Engineering and Technology
                Signal Processing
                Audio Signal Processing
                Custom metadata
                vor-update-to-uncorrected-proof
                2020-11-05
                Preprocessed MEG recordings and stimuli are available from the Digital Repository at the University of Maryland. The MEG dataset is available at http://hdl.handle.net/1903/21109, additional files specific to this paper are available at http://hdl.handle.net/1903/26370. Subject-specific results are provided for each figure in supplementary data files.

                Life sciences
                Life sciences

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