Impressive advances in cochlear implant (CI) technology in recent years have placed within our grasp the possibility of developing spoken language. However, one remaining question is why CI children as a group still perform below their normal-hearing peers and why performance varies across individuals. In searching for the sources of language development variability in young CI children, our proposed study will also seek to construct clinically useful models that capitalize on pre-surgical neural and behavioral measures for predicting post-surgical language development outcomes at the individual level. At the Lurie Children's Hospital of Chicago, we have collected pre-surgical neural and behavioral measures and post-surgical speech outcome data from about 130 (70 prospectively and 60 retrospectively) subjects over the past five years. We also conducted a preliminary study that demonstrates that individual patients can be classified into better or poorer language improvers six months post-surgery by a machine-learning algorithm that makes predictions based on neuroanatomical measures established pre-surgery. Our proposed study will seek to extend this preliminary research by: (1) continuing data collection and analyzing a larger set of neural measures and outcome data up to two years post-surgery, and (2) testing the generalizability of the predictive models built in Chicago to CI patients of similar characteristics at the Mayo Clinic and the University of Michigan in order to evaluate the models' broader clinical utility. Our studies will be constructed to test two competing hypotheses. The neural preservation hypothesis postulates that brain regions that are unaffected by hearing loss contribute most to language development outcomes, and that they most likely encompass auditory association areas and cognitive brain regions. The neural cycling hypothesis argues that post-surgical language development relies on the recovery of the brain regions that are most affected by hearing loss, most likely in the primary auditory cortical regions. If successful, our predictive models could immediately be deployed to augment current clinical practice, because they concern patient outcome at the individual level. Ultimately, our predictive information will allow clinicians and parents to plan more carefully for post-surgical therapy and to identify personalized management strategies for individual children. Our study directly addresses Priority Area 3 in Hearing and Balance Research of the latest NIDCD Strategic Plan.
Although cochlear implants have allowed better spoken language development in early-deafened children, the language and cognitive abilities of these children continue to lag behind those of their normal-hearing peers. The proposed study will seek to use pre-surgical neural and behavioral measures to predict post-surgical language outcomes in young children who have received a CI in order to assist in better post-surgical planning and the optimization of therapy.
