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.
Original language | English (US) |
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Pages (from-to) | E1022-E1031 |
Journal | Proceedings of the National Academy of Sciences of the United States of America |
Volume | 115 |
Issue number | 5 |
DOIs | |
State | Published - Jan 30 2018 |
Funding
ACKNOWLEDGMENTS. We thank Haejung Shin, Beth Tournis, and members of the cochlear implant team at the Ann & Robert H. Lurie Children’s Hospital of Chicago for their support of this research. This research is supported by grants from Knowles Hearing Center at Northwestern University and Ann & Robert H. Lurie Children’s Hospital of Chicago, and donations from the Global Parent Child Resource Centre Limited and the Dr. Stanley Ho Medical Development Foundation awarded to The Chinese University of Hong Kong. We thank Haejung Shin, Beth Tournis, and members of the cochlear implant team at the Ann & Robert H. Lurie Children’s Hospital of Chicago for their support of this research. This research is supported by grants from Knowles Hearing Center at Northwestern University and Ann & Robert H. Lurie Children’s Hospital of Chicago, and donations from the Global Parent Child Resource Centre Limited and the Dr. Stanley Ho Medical Development Foundation awarded to The Chinese University of Hong Kong.
Keywords
- Auditory deprivation
- Cochlear implant
- Machine learning
- Neural preservation
- Prediction
ASJC Scopus subject areas
- General