Usher syndrome (USH) is an autosomal recessive disorder characterized by hearing loss, visual impairment, and in some cases, vestibular dysfunction. It is the leading cause of hereditary deaf-blindness in humans. USH causes extensive degeneration in the cochlear nerve (CN), especially in CN fibers innervating the base of the cochlea. Whereas there is no treatment for arresting this degenerative process or for restoring visual loss, the restoration of auditory input is possible with cochlear implantation. Due to the progressive deterioration in vision, using visual cues for communication will eventually become impossible. Therefore, the importance of optimizing auditory inputs through cochlear implants (CIs) for patients with USH is paramount. However, patients with USH have much higher rates of neurological, mental, or behavioral disorders than the general CI patient population, which limits their ability to provide reliable behavioral responses or sufficient verbal descriptions of their auditory perception, especially for pediatric patients. In addition, optimal programming parameters for CI users with CN damage differ from those used in typical CI users due to declined CN responsiveness to electrical stimulation. As a result, the clinical programming process in implanted patients with USH can be extremely challenging. To date, auditory neural encoding of electrical stimulation in patients with USH has not been systematically evaluated. Consequently, the field lacks evidence-based practice guidelines for managing implanted patients with USH. For patients who cannot provide reliable feedback, clinicians rely on a trial-and-error approach for adjusting CI programming settings, which ultimately may not result in appropriate programming maps for individual patients. Therefore, there is an urgent need to develop objective clinical tools for optimizing CI settings for these patients. As the first step toward developing evidence-based practice for managing patients with USH, this study evaluates local neural health, as well as the neural encoding of temporal and spectral cues at the CN in implanted patients with USH. Aim 1 will determine local CN health in patients with USH by assessing the sensitivity of the electrically evoked compound action potential to changes in interphase gap and pulse polarity. Aim 2 will determine group differences in neural encoding of temporal and spectral cues at the CN between patients with USH and patients with idiopathic hearing loss. Aim 3 will use supervised machine learning techniques to develop an objective tool for assessing the electrode-neuron interface at individual electrode locations. Results of this study have high scientific significance because they will establish how CN degeneration affects neural encoding and processing of electrical stimulation, and identify tests that distinguish the loss of spiral ganglion neurons from the loss of peripheral axons. Results of this study also have high clinical significance because they will 1) lay the groundwork for developing effective, evidence-based clinical practice guidelines for managing patients with USH, and 2) yield an objective tool for assessing the site-specific electrode-neuron interface in all CI users, which is foundational for creating optimal programming maps for individual patients.
Usher syndrome (USH) causes extensive degeneration in the cochlear nerve (CN), especially in CN fibers innervating the base of the cochlea. As the first step toward developing evidence-based practice for managing implant patients with USH, this study evaluates local neural health, as well as the neural encoding of temporal and spectral cues at the CN in implanted patients with USH. Aim 1 will determine local CN health in patients with USH by assessing the sensitivity of the electrically evoked compound action potential to changes in interphase gap and pulse polarity. Aim 2 will determine group differences in neural encoding of temporal and spectral cues at the CN between patients with USH and patients with idiopathic hearing loss. Aim 3 will use supervised machine learning techniques to develop an objective tool for assessing the electrode-neuron interface at individual electrode locations.