Using Electrophysiology to Complement Speech Understanding-in-Noise Measures

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Approximately 37.5 million Americans have some problem hearing, and hearing-related problems rank as the most common service-connected disabilities. One of the chief complaints of those with hearing impairment is difficulty communicating in background noise, a challenge that is exacerbated by several health conditions that are prominent within the Veteran population (e.g., traumatic brain injury, diabetes, multiple sclerosis, and Parkinson's disease). In addition, many older individuals have difficulties understanding speech in background noise beyond what would be expected based solely on their audiometric thresholds. There is a fundamental need for improved diagnosis and treatment of speech understanding in noise difficulties. This research program sets out to establish electrophysiological correlates of speech-in-noise understanding with the goal of supplementing speech-in-noise testing. The assumption is that accurate perception in noise is dependent, in part, on the accuracy of neural coding of the auditory stimulus. By combining electrophysiological and behavioral information we can advance our understanding of perception-in-noise difficulties and predict outcomes in difficult-to-test individuals using physiological testing. The clinical significance of the research proposed is related directly to the ability to predict, more accurately diagnose, and more precisely treat perception-in-noise difficulties. An electrophysiological measure that predicts speech perception will allow for improved assessment of difficult-to-test populations and provide information about the capacity of that auditory system to encode certain stimuli. This will allow the clinician to tailor treatment strategies to the specific needs of the individual and to counsel patients more effectively in terms of the expectations they should have and the benefit they should expect as a result of specific treatments. Therefore, to further our understanding of signal-in-noise perception and neural coding, we will use brainstem, cortical, and cognitive auditory evoked potentials and behavioral speech understanding-in-noise measures with age and hearing impairment as continuous variables to characterize the effect sizes of various covariates and to improve our understanding of the relationship between brain and behavioral measures.