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      Neural preservation underlies speech improvement from auditory deprivation in young cochlear implant recipients

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          Abstract

          <p id="d10156717e402">The ability to accurately predict speech improvement for young children who use cochlear implants (CIs) would be a first step in the development of a personalized therapy to enhance language development. Despite decades of outcome research, no useful clinical prediction tool exists. An accurate predictive model that relies on routinely obtained presurgical neuroanatomic data has the potential to transform clinical practice while enhancing our understanding of neural organization resulting from auditory deprivation. Using presurgical MRI neuroanatomical data and multivariate pattern analysis techniques, we found that neural systems that were unaffected by auditory deprivation best predicted young CI candidates’ postsurgical speech-perception outcomes. Our study provides an example of how research in cognitive neuroscience can inform basic science and lead to clinical application. </p><p class="first" id="d10156717e405">Although cochlear implantation enables some children to attain age-appropriate speech and language development, communicative delays persist in others, and outcomes are quite variable and difficult to predict, even for children implanted early in life. To understand the neurobiological basis of this variability, we used presurgical neural morphological data obtained from MRI of individual pediatric cochlear implant (CI) candidates implanted younger than 3.5 years to predict variability of their speech-perception improvement after surgery. We first compared neuroanatomical density and spatial pattern similarity of CI candidates to that of age-matched children with normal hearing, which allowed us to detail neuroanatomical networks that were either affected or unaffected by auditory deprivation. This information enables us to build machine-learning models to predict the individual children’s speech development following CI. We found that regions of the brain that were unaffected by auditory deprivation, in particular the auditory association and cognitive brain regions, produced the highest accuracy, specificity, and sensitivity in patient classification and the most precise prediction results. These findings suggest that brain areas unaffected by auditory deprivation are critical to developing closer to typical speech outcomes. Moreover, the findings suggest that determination of the type of neural reorganization caused by auditory deprivation before implantation is valuable for predicting post-CI language outcomes for young children. </p>

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          Most cited references37

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          The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.
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            Despite intensive work on language-brain relations, and a fairly impressive accumulation of knowledge over the last several decades, there has been little progress in developing large-scale models of the functional anatomy of language that integrate neuropsychological, neuroimaging, and psycholinguistic data. Drawing on relatively recent developments in the cortical organization of vision, and on data from a variety of sources, we propose a new framework for understanding aspects of the functional anatomy of language which moves towards remedying this situation. The framework posits that early cortical stages of speech perception involve auditory fields in the superior temporal gyrus bilaterally (although asymmetrically). This cortical processing system then diverges into two broad processing streams, a ventral stream, which is involved in mapping sound onto meaning, and a dorsal stream, which is involved in mapping sound onto articulatory-based representations. The ventral stream projects ventro-laterally toward inferior posterior temporal cortex (posterior middle temporal gyrus) which serves as an interface between sound-based representations of speech in the superior temporal gyrus (again bilaterally) and widely distributed conceptual representations. The dorsal stream projects dorso-posteriorly involving a region in the posterior Sylvian fissure at the parietal-temporal boundary (area Spt), and ultimately projecting to frontal regions. This network provides a mechanism for the development and maintenance of "parity" between auditory and motor representations of speech. Although the proposed dorsal stream represents a very tight connection between processes involved in speech perception and speech production, it does not appear to be a critical component of the speech perception process under normal (ecologically natural) listening conditions, that is, when speech input is mapped onto a conceptual representation. We also propose some degree of bi-directionality in both the dorsal and ventral pathways. We discuss some recent empirical tests of this framework that utilize a range of methods. We also show how damage to different components of this framework can account for the major symptom clusters of the fluent aphasias, and discuss some recent evidence concerning how sentence-level processing might be integrated into the framework.
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              Individual variability in functional connectivity architecture of the human brain.

              The fact that people think or behave differently from one another is rooted in individual differences in brain anatomy and connectivity. Here, we used repeated-measurement resting-state functional MRI to explore intersubject variability in connectivity. Individual differences in functional connectivity were heterogeneous across the cortex, with significantly higher variability in heteromodal association cortex and lower variability in unimodal cortices. Intersubject variability in connectivity was significantly correlated with the degree of evolutionary cortical expansion, suggesting a potential evolutionary root of functional variability. The connectivity variability was also related to variability in sulcal depth but not cortical thickness, positively correlated with the degree of long-range connectivity but negatively correlated with local connectivity. A meta-analysis further revealed that regions predicting individual differences in cognitive domains are predominantly located in regions of high connectivity variability. Our findings have potential implications for understanding brain evolution and development, guiding intervention, and interpreting statistical maps in neuroimaging. Copyright © 2013 Elsevier Inc. All rights reserved.
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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
                January 30 2018
                January 30 2018
                : 115
                : 5
                : E1022-E1031
                Article
                10.1073/pnas.1717603115
                5798370
                29339512
                ff0762ce-9f72-4ad3-85a6-92f130a12de3
                © 2018

                http://www.pnas.org/site/misc/userlicense.xhtml

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