Cortical predictors and correlates of cochlear implant outcome: a longitudinal study using functional near-infrared spectroscopy

While cochlear implants (CIs) have transformed the lives of hundreds of thousands of profoundly deaf individuals worldwide, the ability of individual patients to understand speech through their CI varies widely. The longitudinal study reported in this thesis aimed to investigate the role of cortical function and plasticity in helping to predict and explain variability in individual CI outcome. Speech-evoked cortical activations, measured using functional near-infrared spectroscopy (fNIRS), and behavioural measures of speech perception were acquired in adult CI recipients before implantation through to six months after CI activation. As anticipated, cochlear implantation enabled the significant recovery of auditory speech perception over the first six months of CI use, yet individual performance varied widely across CI users. fNIRS enabled the measurement of speech-evoked cortical activations from bilateral superior temporal cortex (STC) that were free from CI-generated artefacts. Cortical activation to visual speech measured before implantation was able to significantly predict future CI outcome measured following six months of CI use. This pre-implant measure of brain activity provided unique predictive value above that of well-established clinical characteristics, including the age-at-onset and duration of deafness. When examining changes in cortical activation from before to after implantation, a greater increase in STC activation to visual speech was found to be related to better CI outcome. These visual-related changes also predicted changes in STC activation to auditory speech. These results highlight the importance of visual speech cues and coupling between the auditory and visual modalities during the recovery of auditory function with a CI. Together, the findings demonstrate the potential of fNIRS as a brain-imaging tool that is uniquely well-suited for use in cochlear implantation. Future applications of this technology could help to explain individual variability in CI success and to deliver more accurate clinical prognoses for CI candidates