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      Adaptive benefit of cross-modal plasticity following cochlear implantation in deaf adults

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      Proceedings of the National Academy of Sciences
      Proceedings of the National Academy of Sciences

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          Abstract

          <p id="d5629563e244">Following sensory deprivation, the sensory brain regions can become colonized by the other intact sensory modalities. In deaf individuals, evidence suggests that visual language recruits auditory brain regions and may limit hearing restoration with a cochlear implant. This suggestion underpins current rehabilitative recommendations that deaf individuals undergoing cochlear implantation should avoid using visual language. However, here we show the opposite: Recruitment of auditory brain regions by visual speech after implantation is associated with better speech understanding with a cochlear implant. This suggests adaptive benefits of visual communication because visual speech may serve to optimize, rather than hinder, restoration of hearing following implantation. These findings have implications for both neuroscientific theory and the clinical rehabilitation of cochlear implant patients worldwide. </p><p class="first" id="d5629563e247">It has been suggested that visual language is maladaptive for hearing restoration with a cochlear implant (CI) due to cross-modal recruitment of auditory brain regions. Rehabilitative guidelines therefore discourage the use of visual language. However, neuroscientific understanding of cross-modal plasticity following cochlear implantation has been restricted due to incompatibility between established neuroimaging techniques and the surgically implanted electronic and magnetic components of the CI. As a solution to this problem, here we used functional near-infrared spectroscopy (fNIRS), a noninvasive optical neuroimaging method that is fully compatible with a CI and safe for repeated testing. The aim of this study was to examine cross-modal activation of auditory brain regions by visual speech from before to after implantation and its relation to CI success. Using fNIRS, we examined activation of superior temporal cortex to visual speech in the same profoundly deaf adults both before and 6 mo after implantation. Patients’ ability to understand auditory speech with their CI was also measured following 6 mo of CI use. Contrary to existing theory, the results demonstrate that increased cross-modal activation of auditory brain regions by visual speech from before to after implantation is associated with better speech understanding with a CI. Furthermore, activation of auditory cortex by visual and auditory speech developed in synchrony after implantation. Together these findings suggest that cross-modal plasticity by visual speech does not exert previously assumed maladaptive effects on CI success, but instead provides adaptive benefits to the restoration of hearing after implantation through an audiovisual mechanism. </p>

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          Most cited references33

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          HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain.

          Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data.
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            Design and construction of a realistic digital brain phantom.

            After conception and implementation of any new medical image processing algorithm, validation is an important step to ensure that the procedure fulfills all requirements set forth at the initial design stage. Although the algorithm must be evaluated on real data, a comprehensive validation requires the additional use of simulated data since it is impossible to establish ground truth with in vivo data. Experiments with simulated data permit controlled evaluation over a wide range of conditions (e.g., different levels of noise, contrast, intensity artefacts, or geometric distortion). Such considerations have become increasingly important with the rapid growth of neuroimaging, i.e., computational analysis of brain structure and function using brain scanning methods such as positron emission tomography and magnetic resonance imaging. Since simple objects such as ellipsoids or parallelepipedes do not reflect the complexity of natural brain anatomy, we present the design and creation of a realistic, high-resolution, digital, volumetric phantom of the human brain. This three-dimensional digital brain phantom is made up of ten volumetric data sets that define the spatial distribution for different tissues (e.g., grey matter, white matter, muscle, skin, etc.), where voxel intensity is proportional to the fraction of tissue within the voxel. The digital brain phantom can be used to simulate tomographic images of the head. Since the contribution of each tissue type to each voxel in the brain phantom is known, it can be used as the gold standard to test analysis algorithms such as classification procedures which seek to identify the tissue "type" of each image voxel. Furthermore, since the same anatomical phantom may be used to drive simulators for different modalities, it is the ideal tool to test intermodality registration algorithms. The brain phantom and simulated MR images have been made publicly available on the Internet (http://www.bic.mni.mcgill.ca/brainweb).
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              Wavelet-based motion artifact removal for functional near-infrared spectroscopy.

              Functional near-infrared spectroscopy (fNIRS) is a powerful tool for monitoring brain functional activities. Due to its non-invasive and non-restraining nature, fNIRS has found broad applications in brain functional studies. However, for fNIRS to work well, it is important to reduce its sensitivity to motion artifacts. We propose a new wavelet-based method for removing motion artifacts from fNIRS signals. The method relies on differences between artifacts and fNIRS signal in terms of duration and amplitude and is specifically designed for spike artifacts. We assume a gaussian distribution for the wavelet coefficients corresponding to the underlying hemodynamic signal in detail levels and identify the artifact coefficients using this distribution. An input parameter controls the intensity of artifact attenuation in trade-off with the level of distortion introduced in the signal. The method only modifies wavelet coefficients in levels adaptively selected based on the degree of contamination with motion artifact. To demonstrate the feasibility of the method, we tested it on experimental fNIRS data collected from three infant subjects. Normalized mean-square error and artifact energy attenuation were used as criteria for performance evaluation. The results show 18.29 and 16.42 dB attenuation in motion artifacts energy for 700 and 830 nm wavelength signals in a total of 29 motion events with no more than -16.7 dB distortion in terms of normalized mean-square error in the artifact-free regions of the signal.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                September 19 2017
                September 19 2017
                August 14 2017
                : 114
                : 38
                : 10256-10261
                Article
                10.1073/pnas.1704785114
                5617272
                28808014
                91d4e6b5-f6bc-4c2a-a2f0-24e4497c4d4a
                © 2017
                History

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