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      Cross-Modal Re-Organization in Adults with Early Stage Hearing Loss

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          Abstract

          Cortical cross-modal re-organization, or recruitment of auditory cortical areas for visual processing, has been well-documented in deafness. However, the degree of sensory deprivation necessary to induce such cortical plasticity remains unclear. We recorded visual evoked potentials (VEP) using high-density electroencephalography in nine persons with adult-onset mild-moderate hearing loss and eight normal hearing control subjects. Behavioral auditory performance was quantified using a clinical measure of speech perception-in-noise. Relative to normal hearing controls, adults with hearing loss showed significantly larger P1, N1, and P2 VEP amplitudes, decreased N1 latency, and a novel positive component (P2’) following the P2 VEP. Current source density reconstruction of VEPs revealed a shift toward ventral stream processing including activation of auditory temporal cortex in hearing-impaired adults. The hearing loss group showed worse than normal speech perception performance in noise, which was strongly correlated with a decrease in the N1 VEP latency. Overall, our findings provide the first evidence that visual cross-modal re-organization not only begins in the early stages of hearing impairment, but may also be an important factor in determining behavioral outcomes for listeners with hearing loss, a finding which demands further investigation.

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          A standardized boundary element method volume conductor model.

          We used a 3-compartment boundary element method (BEM) model from an averaged magnetic resonance image (MRI) data set (Montreal Neurological Institute) in order to provide simple access to realistically shaped volume conductor models for source reconstruction, as compared to individually derived models. The electrode positions were transformed into the model's coordinate system, and the best fit dipole results were transformed back to the original coordinate system. The localization accuracy of the new approach was tested in a comparison with simulated data and with individual BEM models of epileptic spike data from several patients. The standard BEM model consisted of a total of 4770 nodes, which describe the smoothed cortical envelope, the outside of the skull, and the outside of the skin. The electrode positions were transformed to the model coordinate system by using 3-5 fiducials (nasion, left and right preauricular points, vertex, and inion). The transformation consisted of an averaged scaling factor and a rigid transformation (translation and rotation). The potential values at the transformed electrode positions were calculated by linear interpolation from the stored transfer matrix of the outer BEM compartment triangle net. After source reconstruction the best fit dipole results were transformed back into the original coordinate system by applying the inverse of the first transformation matrix. Test-dipoles at random locations and with random orientations inside of a highly refined reference BEM model were used to simulate noise-free data. Source reconstruction results using a spherical and the standardized BEM volume conductor model were compared to the known dipole positions. Spherical head models resulted in mislocation errors at the base of the brain. The standardized BEM model was applied to averaged and unaveraged epileptic spike data from 7 patients. Source reconstruction results were compared to those achieved by 3 spherical shell models and individual BEM models derived from the individual MRI data sets. Similar errors to that evident with simulations were noted with spherical head models. Standardized and individualized BEM models were comparable. This new approach to head modeling performed significantly better than a simple spherical shell approximation, especially in basal brain areas, including the temporal lobe. By using a standardized head for the BEM setup, it offered an easier and faster access to realistically shaped volume conductor models as compared to deriving specific models from individual 3-dimensional MRI data.
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            Review on solving the inverse problem in EEG source analysis

            In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.
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              Independent EEG Sources Are Dipolar

              Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                28 February 2014
                : 9
                : 2
                : e90594
                Affiliations
                [1 ]University of Colorado at Boulder, Department of Speech, Language and Hearing Sciences, Boulder, Colorado, United States of America
                [2 ]University of Colorado at Boulder, Institute of Cognitive Science, Boulder, Colorado, United States of America
                University of Modena and Reggio Emilia, Italy
                Author notes

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

                Conceived and designed the experiments: JC AS. Performed the experiments: JC. Analyzed the data: JC AS. Contributed reagents/materials/analysis tools: JC AS. Wrote the paper: JC AS.

                Article
                PONE-D-13-42096
                10.1371/journal.pone.0090594
                3938766
                24587400
                50332b49-fa4e-4cc3-9b59-42320e560971
                Copyright @ 2014

                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
                : 15 October 2013
                : 1 February 2014
                Page count
                Pages: 8
                Funding
                This work was supported by the National Institutes of Health (RO106257 to A.S., F31DC011970 to J.C.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Biochemistry
                Neurochemistry
                Synaptic plasticity
                Neuroscience
                Developmental neuroscience
                Synaptic plasticity
                Sensory systems
                Auditory system
                Cognitive neuroscience
                Medicine
                Clinical research design
                Epidemiology
                Epidemiology
                Clinical epidemiology
                Otorhinolaryngology
                Otology
                Hearing disorders

                Uncategorized
                Uncategorized

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