Literature DB >> 29339512

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

Gangyi Feng1,2, Erin M Ingvalson3,4, Tina M Grieco-Calub5,6, Megan Y Roberts5, Maura E Ryan7,8, Patrick Birmingham9,10, Delilah Burrowes7,8, Nancy M Young4,6,11, Patrick C M Wong12,2,13.   

Abstract

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.

Entities:  

Keywords:  auditory deprivation; cochlear implant; machine learning; neural preservation; prediction

Mesh:

Year:  2018        PMID: 29339512      PMCID: PMC5798370          DOI: 10.1073/pnas.1717603115

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  50 in total

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5.  Differences in brain structure in deaf persons on MR imaging studied with voxel-based morphometry.

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10.  A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.

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