Grant: Project Grant

Cochlear implants (CIs) are a highly effective treatment for individuals with hearing loss, yet outcomes vary significantly. While some users regain speech perception with ease, others struggle despite similar clinical profiles. This variability is thought to result from differences in brain plasticity-the brain's ability to reorganize following auditory deprivation and rehabilitation. Understanding these neural adaptations is key to predicting CI success and improving rehabilitation strategies. Our research aims to identify neurophysiological markers that influence CI outcomes by integrating clinical data, neuroimaging, and machine learning (ML). We will follow 150 adults undergoing CI at the McGill University Health Centre, assessing how their brains adapt after implantation. Pre-implantation, we will evaluate how CI candidates process visual information, as cross-modal reorganization-where visual processing engages auditory regions-can influence auditory recovery. Post-implantation, we will track auditory adaptation and neural changes over time to understand the mechanisms driving successful speech perception. To achieve this, we will use advanced neuroimaging techniques: anatomical and functional MRI (pre-CI) will examine brain structure and connectivity, while electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) (pre- and post-CI) will monitor real-time neural changes. A core innovation of our study is the development of ML models to analyze this multimodal dataset, integrating clinical, behavioral, and neural data to predict CI success. By linking brain plasticity to individualized rehabilitation approaches, our research will provide critical insights into why some individuals benefit more than others from CIs. Ultimately, our findings are expected to enhance CI programming, support the optimization of rehabilitation strategies, and contribute to improved hearing outcomes, thereby supporting a better quality of life for CI users.