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      Neural Activity During Audiovisual Speech Processing: Protocol For a Functional Neuroimaging Study

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          Abstract

          Background

          Functional near-infrared spectroscopy (fNIRS) studies have demonstrated associations between hearing outcomes after cochlear implantation and plastic brain changes. However, inconsistent results make it difficult to draw conclusions. A major problem is that many variables need to be controlled. To gain further understanding, a careful preparation and planning of such a functional neuroimaging task is key.

          Objective

          Using fNIRS, our main objective is to develop a well-controlled audiovisual speech comprehension task to study brain activation in individuals with normal hearing and hearing impairment (including cochlear implant users). The task should be deductible from clinically established tests, induce maximal cortical activation, use optimal coverage of relevant brain regions, and be reproducible by other research groups.

          Methods

          The protocol will consist of a 5-minute resting state and 2 stimulation periods that are 12 minutes each. During the stimulation periods, 13-second video recordings of the clinically established Oldenburg Sentence Test (OLSA) will be presented. Stimuli will be presented in 4 different modalities: (1) speech in quiet, (2) speech in noise, (3) visual only (ie, lipreading), and (4) audiovisual speech. Each stimulus type will be repeated 10 times in a counterbalanced block design. Interactive question windows will monitor speech comprehension during the task. After the measurement, we will perform a 3D scan to digitize optode positions and verify the covered anatomical locations.

          Results

          This paper reports the study protocol. Enrollment for the study started in August 2021. We expect to publish our first results by the end of 2022.

          Conclusions

          The proposed audiovisual speech comprehension task will help elucidate neural correlates to speech understanding. The comprehensive study will have the potential to provide additional information beyond the conventional clinical standards about the underlying plastic brain changes of a hearing-impaired person. It will facilitate more precise indication criteria for cochlear implantation and better planning of rehabilitation.

          International Registered Report Identifier (IRRID)

          DERR1-10.2196/38407

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          Most cited references82

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          The assessment and analysis of handedness: The Edinburgh inventory

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            A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology.

            This year marks the 20th anniversary of functional near-infrared spectroscopy and imaging (fNIRS/fNIRI). As the vast majority of commercial instruments developed until now are based on continuous wave technology, the aim of this publication is to review the current state of instrumentation and methodology of continuous wave fNIRI. For this purpose we provide an overview of the commercially available instruments and address instrumental aspects such as light sources, detectors and sensor arrangements. Methodological aspects, algorithms to calculate the concentrations of oxy- and deoxyhemoglobin and approaches for data analysis are also reviewed. From the single-location measurements of the early years, instrumentation has progressed to imaging initially in two dimensions (topography) and then three (tomography). The methods of analysis have also changed tremendously, from the simple modified Beer-Lambert law to sophisticated image reconstruction and data analysis methods used today. Due to these advances, fNIRI has become a modality that is widely used in neuroscience research and several manufacturers provide commercial instrumentation. It seems likely that fNIRI will become a clinical tool in the foreseeable future, which will enable diagnosis in single subjects. Copyright © 2013 Elsevier Inc. All rights reserved.
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              Machine learning for neuroimaging with scikit-learn

              Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.
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                Author and article information

                Contributors
                Journal
                JMIR Res Protoc
                JMIR Res Protoc
                ResProt
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                1929-0748
                June 2022
                21 June 2022
                : 11
                : 6
                : e38407
                Affiliations
                [1 ] Department of Otorhinolaryngology, Head and Neck Surgery Inselspital, Bern University Hospital, University of Bern Bern Switzerland
                [2 ] Hearing Research Laboratory ARTORG Center for Biomedical Engineering Research, University of Bern Bern Switzerland
                Author notes
                Corresponding Author: Stefan Weder stefan.weder@ 123456insel.ch
                Author information
                https://orcid.org/0000-0002-7904-0384
                https://orcid.org/0000-0001-5392-2074
                https://orcid.org/0000-0002-7090-8087
                https://orcid.org/0000-0002-2079-2047
                Article
                v11i6e38407
                10.2196/38407
                9239541
                35727624
                446a5a0e-a330-4b00-ba1e-6509aa84a1d0
                ©András Bálint, Wilhelm Wimmer, Marco Caversaccio, Stefan Weder. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 21.06.2022.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.

                History
                : 31 March 2022
                : 28 April 2022
                : 12 May 2022
                : 3 June 2022
                Categories
                Protocol
                Protocol

                hearing loss,brain plasticity,functional near-infrared spectroscopy (fnirs),cochlear implant,neuroimaging,speech understanding,comprehension,speech,brain activation,brain activity,hearing impairment,cortical activation,neural,brain,protocol,spectroscopy,cochlear,hearing

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