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      Activities of the Right Temporo-Parieto-Occipital Junction Reflect Spatial Hearing Ability in Cochlear Implant Users

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          Abstract

          Spatial hearing is critical for us not only to orient ourselves in space, but also to follow a conversation with multiple speakers involved in a complex sound environment. The hearing ability of people who suffered from severe sensorineural hearing loss can be restored by cochlear implants (CIs), however, with a large outcome variability. Yet, the causes of the CI performance variability remain incompletely understood. Despite the CI-based restoration of the peripheral auditory input, central auditory processing might still not function fully. Here we developed a multi-modal repetition suppression (MMRS) paradigm that is capable of capturing stimulus property-specific processing, in order to identify the neural correlates of spatial hearing and potential central neural indexes useful for the rehabilitation of sound localization in CI users. To this end, 17 normal hearing and 13 CI participants underwent the MMRS task while their brain activity was recorded with a 256-channel electroencephalography (EEG). The participants were required to discriminate between the probe sound location coming from a horizontal array of loudspeakers. The EEG MMRS response following the probe sound was elicited at various brain regions and at different stages of processing. Interestingly, the more similar this differential MMRS response in the right temporo-parieto-occipital (TPO) junction in CI users was to the normal hearing group, the better was the spatial hearing performance in individual CI users. Based on this finding, we suggest that the differential MMRS response at the right TPO junction could serve as a central neural index for intact or impaired sound localization abilities.

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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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            Cortical surface-based analysis. I. Segmentation and surface reconstruction.

            Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.
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              FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

              This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                12 March 2021
                2021
                : 15
                : 613101
                Affiliations
                [1] 1MEG Center, University of Tübingen , Tübingen, Germany
                [2] 2Department of Otolaryngology, Head and Neck Surgery, Tübingen Hearing Research Centre, University of Tübingen , Tübingen, Germany
                [3] 3Comprehensive Cochlear Implant Center, ENT Clinic Tübingen, Tübingen University Hospital , Tübingen, Germany
                [4] 4Center of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen , Tübingen, Germany
                [5] 5CIMeC, Center for Mind/Brain Research, University of Trento , Rovereto, Italy
                [6] 6DiPsCo, Department of Psychology and Cognitive Science , Rovereto, Italy
                [7] 7Center of Neurology, Department of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen , Tübingen, Germany
                Author notes

                Edited by: Fei Chen, Southern University of Science and Technology, China

                Reviewed by: Dietmar Basta, Unfallkrankenhaus Berlin, Germany; Miguel Angelo Hyppolito, University of São Paulo, Brazil

                *Correspondence: Christoph Braun, christoph.braun@ 123456uni-tuebingen.de

                This article was submitted to Auditory Cognitive Neuroscience, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2021.613101
                7994335
                a20546bc-4b63-4af3-9e47-ad7f790b16d6
                Copyright © 2021 Schäfer, Vedoveli, Righetti, Gamerdinger, Knipper, Tropitzsch, Karnath, Braun and Li Hegner.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 October 2020
                : 18 February 2021
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 94, Pages: 16, Words: 0
                Categories
                Neuroscience
                Original Research

                Neurosciences
                eeg,ci,tpo junction,repetition suppression,auditory
                Neurosciences
                eeg, ci, tpo junction, repetition suppression, auditory

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