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      Dynamic networks differentiate the language ability of children with cochlear implants

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          Abstract

          Background

          Cochlear implantation (CI) in prelingually deafened children has been shown to be an effective intervention for developing language and reading skill. However, there is a substantial proportion of the children receiving CI who struggle with language and reading. The current study–one of the first to implement electrical source imaging in CI population was designed to identify the neural underpinnings in two groups of CI children with good and poor language and reading skill.

          Methods

          Data using high density electroencephalography (EEG) under a resting state condition was obtained from 75 children, 50 with CIs having good (HL) or poor language skills (LL) and 25 normal hearing (NH) children. We identified coherent sources using dynamic imaging of coherent sources (DICS) and their effective connectivity computing time-frequency causality estimation based on temporal partial directed coherence (TPDC) in the two CI groups compared to a cohort of age and gender matched NH children.

          Findings

          Sources with higher coherence amplitude were observed in three frequency bands (alpha, beta and gamma) for the CI groups when compared to normal hearing children. The two groups of CI children with good (HL) and poor (LL) language ability exhibited not only different cortical and subcortical source profiles but also distinct effective connectivity between them. Additionally, a support vector machine (SVM) algorithm using these sources and their connectivity patterns for each CI group across the three frequency bands was able to predict the language and reading scores with high accuracy.

          Interpretation

          Increased coherence in the CI groups suggest overall that the oscillatory activity in some brain areas become more strongly coupled compared to the NH group. Moreover, the different sources and their connectivity patterns and their association to language and reading skill in both groups, suggest a compensatory adaptation that either facilitated or impeded language and reading development. The neural differences in the two groups of CI children may reflect potential biomarkers for predicting outcome success in CI children.

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          Most cited references133

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          Support-vector networks

          Corinna Cortes, Vladimir Vapnik (1995)
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            Nonparametric statistical testing of EEG- and MEG-data.

            Eric Maris, Robert Oostenveld (2007)
            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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              Alpha-band oscillations, attention, and controlled access to stored information

              Wolfgang Klimesch (2012)
              Alpha-band oscillations are the dominant oscillations in the human brain and recent evidence suggests that they have an inhibitory function. Nonetheless, there is little doubt that alpha-band oscillations also play an active role in information processing. In this article, I suggest that alpha-band oscillations have two roles (inhibition and timing) that are closely linked to two fundamental functions of attention (suppression and selection), which enable controlled knowledge access and semantic orientation (the ability to be consciously oriented in time, space, and context). As such, alpha-band oscillations reflect one of the most basic cognitive processes and can also be shown to play a key role in the coalescence of brain activity in different frequencies.
              Article 0 comments Cited 855 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Journal
                Journal ID (nlm-ta): Front Neurosci
                Journal ID (iso-abbrev): Front Neurosci
                Journal ID (publisher-id): Front. Neurosci.
                Title: Frontiers in Neuroscience
                Publisher: Frontiers Media S.A.
                ISSN (Print): 1662-4548
                ISSN (Electronic): 1662-453X
                Publication date (Electronic): 20 June 2023
                Publication date Collection: 2023
                Volume: 17
                Electronic Location Identifier: 1141886
                Affiliations
                [1] 1Child Study Center, Yale School of Medicine, Yale University , New Haven, CT, United States
                [2] 2Department of Psychology, Concordia University , Montreal, QC, Canada
                [3] 3Hearts for Hearing Foundation , Oklahoma City, OK, United States
                [4] 4Department of Otolaryngology – Head and Neck Surgery, University of Oklahoma Medical Center , Oklahoma City, OK, United States
                [5] 5University of Oklahoma College of Medicine , Oklahoma City, OK, United States
                [6] 6Department of Neurology, Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI), Universitätsklinikum Würzburg , Würzburg, Germany
                [7] 7School of Communication Sciences and Disorders, McGill University , Montreal, QC, Canada
                Author notes

                Edited by: Hanjun Liu, Sun Yat-sen University, China

                Reviewed by: Suiping Wang, South China Normal University, China; Richard Charles Dowell, The University of Melbourne, Australia; Jiong Hu, The University of the Pacific, United States

                *Correspondence: Vincent L. Gracco, vincent.gracco@ 123456yale.edu

                †These authors have contributed equally to this work

                Article
                DOI: 10.3389/fnins.2023.1141886
                PMC ID: 10318154
                PubMed ID: 37409105
                SO-VID: bd66c5cf-ed02-45af-9e29-0ab2b3056bf9
                Copyright © Copyright © 2023 Koirala, Deroche, Wolfe, Neumann, Bien, Doan, Goldbeck, Muthuraman and Gracco.
                License:

                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
                Date received : 10 January 2023
                Date accepted : 29 May 2023
                Page count
                Figures: 6, Tables: 3, Equations: 1, References: 156, Pages: 16, Words: 13430
                Funding
                This study was supported by the funding from Oberkotter Foundation ( https://oberkotterfoundation.org/). MM was supported by the funding from Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 424778381-TRR295.
                Categories
                Subject: Neuroscience
                Subject: Original Research
                Custom metadata
                section-at-acceptance Auditory Cognitive Neuroscience

                ScienceOpen disciplines: Neurosciences
                Keywords: cochlear implant,electroencephalography (eeg),language and reading,age of intervention,electrical source imaging (esi)
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: cochlear implant, electroencephalography (eeg), language and reading, age of intervention, electrical source imaging (esi)

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