Spectral Grouping of Electrically Encoded Sound Predicts Speech-in-Noise Performance in Cochlear Implantees

• Published in: Journal of the Association for Research in Otolaryngology 2023-12, Vol.24 (6), p.607-617
• Main Authors: Choi, Inyong, Gander, Phillip E., Berger, Joel I., Woo, Jihwan, Choy, Matthew H., Hong, Jean, Colby, Sarah, McMurray, Bob, Griffiths, Timothy D.
• Format: Article
• Language: English
• Subjects: Auditory perception; Cochlea; Cochlear Implantation; Cochlear Implants; Cognitive ability; Hearing; Medicine; Medicine & Public Health; Neurobiology; Neurosciences; Noise; Otorhinolaryngology; Psychophysics; Regression analysis; Research Article; Speech; Speech Perception; Stochasticity
• ISSN: 1438-7573; 1525-3961; 1438-7573
• DOI: 10.1007/s10162-023-00918-x

Objectives Cochlear implant (CI) users exhibit large variability in understanding speech in noise. Past work in CI users found that spectral and temporal resolution correlates with speech-in-noise ability, but a large portion of variance remains unexplained. Recent work on normal-hearing listeners showed that the ability to group temporally and spectrally coherent tones in a complex auditory scene predicts speech-in-noise ability independently of the audiogram, highlighting a central mechanism for auditory scene analysis that contributes to speech-in-noise. The current study examined whether the auditory grouping ability also contributes to speech-in-noise understanding in CI users. Design Forty-seven post-lingually deafened CI users were tested with psychophysical measures of spectral and temporal resolution, a stochastic figure-ground task that depends on the detection of a figure by grouping multiple fixed frequency elements against a random background, and a sentence-in-noise measure. Multiple linear regression was used to predict sentence-in-noise performance from the other tasks. Results No co-linearity was found between any predictor variables. All three predictors (spectral and temporal resolution plus the figure-ground task) exhibited significant contribution in the multiple linear regression model, indicating that the auditory grouping ability in a complex auditory scene explains a further proportion of variance in CI users’ speech-in-noise performance that was not explained by spectral and temporal resolution. Conclusion Measures of cross-frequency grouping reflect an auditory cognitive mechanism that determines speech-in-noise understanding independently of cochlear function. Such measures are easily implemented clinically as predictors of CI success and suggest potential strategies for rehabilitation based on training with non-speech stimuli.