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      Cortical organization restored by cochlear implantation in young children with single sided deafness

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          Abstract

          Early treatment of single sided deafness in children has been recommended to protect from neurodevelopmental preference for the better hearing ear and from social and educational deficits. A fairly homogeneous group of five young children (≤3.6 years of age) with normal right sided hearing who received a cochlear implant to treat deafness in their left ears were studied. Etiology of deafness was largely cytomegalovirus ( n = 4); one child had an enlarged vestibular aqueduct. Multi-channel electroencephalography of cortical evoked activity was measured repeatedly over time at: 1) acute (0.5 ± 0.7 weeks); 2) early chronic (1.1 ± 0.2 months); and 3) chronic (5.8 ± 3.4 months) cochlear implant stimulation. Results indicated consistent responses from the normal right ear with marked changes in activity from the implanted left ear. Atypical distribution of peak amplitude activity from the implanted ear at acute stimulation marked abnormal lateralization of activity to the ipsilateral left auditory cortex and recruitment of extra-temporal areas including left frontal cortex. These abnormalities resolved with chronic implant use and contralateral aural preference emerged in both auditory cortices. These findings indicate that early implantation in young children with single sided deafness can rapidly restore bilateral auditory input to the cortex needed to improve binaural hearing.

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            Signal processing in magnetoencephalography.

            The subject of this article is detection of brain magnetic fields, or magnetoencephalography (MEG). The brain fields are many orders of magnitude smaller than the environmental magnetic noise and their measurement represent a significant metrological challenge. The only detectors capable of resolving such small fields and at the same time handling the large dynamic range of the environmental noise are superconducting quantum interference devices (or SQUIDs). The SQUIDs are coupled to the brain magnetic fields using combinations of superconducting coils called flux transformers (primary sensors). The environmental noise is attenuated by a combination of shielding, primary sensor geometry, and synthetic methods. One of the most successful synthetic methods for noise elimination is synthetic higher-order gradiometers. How the gradiometers can be synthesized is shown and examples of their noise cancellation effectiveness are given. The MEG signals measured on the scalp surface must be interpreted and converted into information about the distribution of currents within the brain. This task is complicated by the fact that such inversion is nonunique. Additional mathematical simplifications, constraints, or assumptions must be employed to obtain useful source images. Methods for the interpretation of the MEG signals include the popular point current dipole, minimum norm methods, spatial filtering, beamformers, MUSIC, and Bayesian techniques. The use of synthetic aperture magnetometry (a class of beamformers) is illustrated in examples of interictal epileptic spiking and voluntary hand-motor activity. Copyright 2001 Elsevier Science.
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              Template-O-Matic: a toolbox for creating customized pediatric templates.

              Processing pediatric neuroimaging data is a challenge due to pervasive morphological changes that occur in the human brain during normal development. This is of special relevance when reference data is used as part of the processing approach, as in spatial normalization and tissue segmentation. Current approaches construct reference data (templates) by averaging brain images from a control group of subjects, or by creating custom templates from the group under study. In this technical note, we describe a new, and generalized method of constructing such appropriate reference data by statistically analyzing a large sample (n=404) of healthy children, as acquired during the NIH MRI study of normal brain development. After eliminating non-contributing demographic variables, we modeled the effects of age (first, second, and third-order terms) and gender, for each voxel in gray matter and white matter. By appropriate weighting with the parameter estimates from these analyses, complete tissue maps can be generated automatically from this database to match a pediatric population selected for study. The algorithm is implemented in the form of a toolbox for the SPM5 image data processing suite, which we term Template-O-Matic. We compare the performance of this approach with the current method of template generation and discuss the implications of our approach.
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                Author and article information

                Contributors
                melissa.polonenko@mail.utoronto.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 December 2017
                4 December 2017
                2017
                : 7
                : 16900
                Affiliations
                [1 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Institute of Medical Sciences, , The University of Toronto, ; Toronto, ON M5S 1A8 Canada
                [2 ]ISNI 0000 0004 0473 9646, GRID grid.42327.30, Neurosciences and Mental Health, , The Hospital for Sick Children, ; Toronto, ON M5G 1X8 Canada
                [3 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Otolaryngology – Head & Neck Surgery, , The University of Toronto, ; Toronto, ON M5G 2N2 Canada
                [4 ]ISNI 0000 0004 0473 9646, GRID grid.42327.30, Otolaryngology – Head & Neck Surgery, , The Hospital for Sick Children, ; Toronto, ON M5G 1X8 Canada
                Author information
                http://orcid.org/0000-0003-1914-6117
                Article
                17129
                10.1038/s41598-017-17129-z
                5715123
                29203800
                a54a2ff3-96e0-4931-b2c9-aee5e9645fb2
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 May 2017
                : 22 November 2017
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