Abstract
AbstractBackgroundCochlear implants (CIs) have revolutionized hearing restoration for individuals with severe or profound hearing loss. However, a substantial and unexplained variability persists in CI outcomes, even when considering subject-specific factors such as age and the duration of deafness. In this study, we explore the utility of resting-state functional near-infrared spectroscopy (fNIRS) recordings to predict speech understanding outcomes before and after CI implantation. Our hypothesis revolves around resting-state functional connectivity (FC) as a reflection of brain plasticity post-hearing loss and implantation. Specifically, we hypothesized that the average clustering coefficient in resting FC networks can capture this variation among CI users.MethodsTwenty-two cochlear implant candidates participated in this study. Resting-state fNIRS data were collected pre-implantation and at one month, three months, and one year post-implantation. Speech understanding performance was assessed using CNC words in quiet and BKB sentences in noise one year post-implantation. Resting-state functional connectivity networks were constructed using regularized partial correlation, and the average clustering coefficient was measured in the signed weighted networks as a predictive measure for implantation outcomes.ResultsOur findings demonstrate a significant correlation between the average clustering coefficient in resting-state functional networks and speech understanding outcomes. Importantly, our analysis reveals that this measure provides unique information not accounted for by subject-specific factors such as age and duration of deafness.ConclusionThis approach utilizes an easily deployable resting-state functional brain imaging metric to predict speech understanding outcomes in implant recipients. The results indicate that the average clustering coefficient, both pre and post implantation, correlates with speech understanding outcomes.
Publisher
Cold Spring Harbor Laboratory