Author
Feng, G., Ingvalson, E. M., Grieco-Calub, T. M., Roberts, M. Y., Ryan, M. E., Birmingham, P., … & Wong, P. C. (2018). Neural preservation underlies speech improvement from auditory deprivation in young cochlear implant recipients. Proceedings of the National Academy of Sciences, https://doi.org/10.1073/pnas.1717603115
Significance
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.
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.
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