Neural preservation underlies speech improvement from auditory deprivation in young cochlear implant recipients
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摘要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.
著者Gangyi Feng, Erin M. Ingvalson, Tina M. Grieco-Calub, Megan Y. Roberts, Maura E. Ryan, Patrick Birmingham, Delilah Burrowes, Nancy M. Young, Patrick C. M. Wong
期刊名稱Proceedings of the National Academy of Sciences
出版年份2018
月份1
日期30
卷號115
期次5
出版社NATL ACAD SCIENCES
頁次E1022 - E1031
國際標準期刊號0027-8424
語言英式英語
關鍵詞neural preservation,cochlear implant,prediction,auditory deprivation,machine learning
Web of Science 學科類別Multidisciplinary Sciences;Science & Technology - Other Topics

上次更新時間 2020-05-07 於 01:00