Cochlear implantation (CI) in prelingually deafened children has been shown to be an effective intervention for developing language and reading skill. However, there is a substantial proportion of the children receiving CI who struggle with language and reading. The current study–one of the first to implement electrical source imaging in CI population was designed to identify the neural underpinnings in two groups of CI children with good and poor language and reading skill.
Data using high density electroencephalography (EEG) under a resting state condition was obtained from 75 children, 50 with CIs having good (HL) or poor language skills (LL) and 25 normal hearing (NH) children. We identified coherent sources using dynamic imaging of coherent sources (DICS) and their effective connectivity computing time-frequency causality estimation based on temporal partial directed coherence (TPDC) in the two CI groups compared to a cohort of age and gender matched NH children.
Sources with higher coherence amplitude were observed in three frequency bands (alpha, beta and gamma) for the CI groups when compared to normal hearing children. The two groups of CI children with good (HL) and poor (LL) language ability exhibited not only different cortical and subcortical source profiles but also distinct effective connectivity between them. Additionally, a support vector machine (SVM) algorithm using these sources and their connectivity patterns for each CI group across the three frequency bands was able to predict the language and reading scores with high accuracy.
Increased coherence in the CI groups suggest overall that the oscillatory activity in some brain areas become more strongly coupled compared to the NH group. Moreover, the different sources and their connectivity patterns and their association to language and reading skill in both groups, suggest a compensatory adaptation that either facilitated or impeded language and reading development. The neural differences in the two groups of CI children may reflect potential biomarkers for predicting outcome success in CI children.