One in ten adult patients who seek help from an audiologist for speech perception difficulties have clinically normal hearing. This line of work aims to understand the neural basis of speech perception challenges in younger, middle-aged, and older adults that cannot be explained by hearing thresholds on an audiogram. In McHaney et al., 2021, we used EEG to show that cortical tracking of speech by delta band oscillations is a robust neural correlate of speech-in-noise comprehension in older adults beyond the effects of audibility and working memory. In subsequent studies we systematically examined bottom-up sensory and top-down cognitive processes involved in speech perception through a comprehensive audiological and neurophysiological assessment. We demonstrated that middle-aged adults with normal hearing show less distinctive neural encoding of phonemes (Guo, McHaney et al., 2025) and less specialized cortical processing of suprasegmental prosodic features in the auditory cortex (McHaney et al., revisions in review) compared to younger adults. These findings are consistent with age-related neural dedifferentiation in middle-aged adults, which has previously only been considered in older adults.

To examine a mechanistic cause for such age-related neural dedifferentiation in middle-age, we used a cross-species design and linked electrophysiological measures of perceptual decline in middle-aged adults to histologically confirmed cochlear neural degeneration in a Mongolian gerbil animal model (Zink, Zhen, McHaney et al., 2025). We also found that this perceptual decline in humans resulted in a concomitant increase in listening effort, as indexed by pupillary responses. These findings offer a neural basis for early listening fatigue and conversational disengagement in middle-age.

Although challenges with phoneme encoding, prosody encoding, and listening effort emerge in adults with clinically normal audiograms, they have direct implications for individuals who use hearing aids or cochlear implants. Hearing aids and cochlear implants are designed to optimize audibility, yet this line of research shows that speech perception difficulties can stem from degraded neural representations of phonemes and prosody, as well as increased reliance on cognitive resources. These neural and cognitive bottlenecks may limit the extent to which amplification or electrical stimulation alone can restore effortless speech understanding. By identifying markers of early neural decline and linking them to listening effort, this line of work provides a framework for developing new fitting strategies, individualized signal processing approaches, and clinical assessments that move beyond the audiogram. Collectively, understanding the neural basis of speech perception challenges in midlife will help us optimize hearing aid and cochlear implant outcomes by targeting the specific deficits that current devices cannot fully resolve.

During my PhD studies, I was awarded a $145,000 fellowship from the National Institutes of Health to promote diversity in biomedical research to investigate speech perception in noise in middle-aged adults, and I was also supported by a NIH Institutional Training Grant in Auditory and Vestibular Neuroscience that led to this work. This line of work has resulted in several publications:

1. McHaney, J. R., Gnanateja, G. N., Smayda, K. E., Zinszer, B. D., & Chandrasekaran, B. (2021). Cortical Tracking of Speech in Delta Band Relates to Individual Differences in Speech in Noise Comprehension in Older Adults. Ear and Hearing, 42(2), 343-354.

2. Guo, Z.^, McHaney, J. R.^, Parthasarathy, A., McFarlane, K. A., & Chandrasekaran, B. (2025). Reduced neural distinctiveness of speech representations in the middle-aged brain. Neurobiology of Language. (^co-first authors).

3. Zink, M. E.^, Zhen, L.^, McHaney, J. R.^, Klara, J., Yurasits, K., Cancel, V., Flemm, O., Mitchell, C., Datta, J., Chandrasekaran, B., & Parthasarathy, A. (2025). Increased listening effort and cochlear neural degeneration underlie behavioral deficits in speech perception in noise in normal hearing middle-aged adults. eLife, 13, RP102823. (^co-first authors).

4. McHaney, J. R., Hancock, K. E., Polley, D. B., & Parthasarathy, A. (2024). Sensory representations and pupil-indexed listening effort provide complementary contributions to multi-talker speech intelligibility. Scientific Reports, 14(1), 30882.

5. Cancel, V. E.^, McHaney, J. R.^, Milne, V. Palmer, C., & Parthasarathy, A. (2023). A data-driven approach to identify a rapid screener for auditory processing disorder testing referrals in adults. Scientific Reports, 13, 13636. (^co-first authors).

Traditional audiometry often misses early neural deficits that may contribute to hearing difficulties. This line of work aims to address this unmet clinical need by developing non-invasive diagnostic tools that can assay neural speech processing abilities in the absence of hearing loss on an audiogram.This research was inspired by a study that I co-led, where I retrospectively analyzed 50,000 audiology patient records from the University of Pittsburgh Medical Center clinics. We found that 15% of patients had normal hearing thresholds in both ears, and nearly half of these patients had chief complaints of speech perception in noise difficulties (Cancel, McHaney et al., 2023). We found that scores on the Words in Noise test, Randomized Dichotic Digits Test, and QuickSIN, combined with a normal audiogram, reliably identified patients who received a referral for an auditory processing disorder diagnosis. These results provided strong evidence to support a rapid screener for auditory processing disorder in patients with chief complaints of speech-in-noise difficulties in the absence of hearing loss.

To translate mechanistic insights into potential rehabilitation, I have investigated whether neuromodulation strategies such as vagus nerve stimulation could enhance speech processing difficulties. In Llanos, McHaney et al., 2020, we used transcutaneous vagus nerve stimulation (tVNS) as a method for enhancing behavioral attention to harder-to-learn non-native speech sounds. I then used pupillometry in McHaney et al., 2021 and 2023 to gauge the mechanism of action for tVNS-induced behavioral enhancement. My work with tVNS and pupillometry demonstrates that neuromodulation can enhance speech processing and modulate the cognitive effort needed for listening. These findings have implications for enhancing attention to speech sounds for speech perception in noise difficulties.

Associated References:

1. Llanos, F., McHaney, J. R., Schuerman, W. L., Yi, H. G., Leonard, M. K., & Chandrasekaran, B. (2020). Non-invasive peripheral nerve stimulation selectively enhances speech category learning in adults. npj Science of Learning, 5(1), 1-11.

2. McHaney, J. R., Tessmer, R., Roark, C. L., & Chandrasekaran, B. (2021). Working memory relates to individual differences in speech category learning: Insights from computational modeling and pupillometry. Brain and Language, 222, 105010.

3. McHaney, J. R., Schuerman, W. L., Leonard, M. K., & Chandrasekaran, B. (2023). Transcutaneous vagus nerve stimulation modulates pupillary responses during non-native speech category learning. Journal of Speech, Language, and Hearing Research, 66(10), 3825-3843.

4. Cancel, V. E.^, McHaney, J. R., Milne, V. Palmer, C., & Parthasarathy, A. (2023). A data-driven approach to identify a rapid screener for auditory processing disorder testing referrals in adults. Scientific Reports, 13, 13636. (^co-first authors).

5. Guo, Z.^, McHaney, J. R.^, Parthasarathy, A., McFarlane, K. A., & Chandrasekaran, B. (2025). Reduced neural distinctiveness of speech representations in the middle-aged brain. Neurobiology of Language, 6, nol_a_00169. (^co-first authors).

6. McHaney, J. R., Guo, Z., Gnanateja, G. N., Parthasarathy, A., & Chandrasekaran, B. (revisions in review). Neural Coding of Fundamental Frequency and Processing of Discrete Pitch Accents in Middle-age.

Speech perception in noise relies on both bottom-up sensory encoding and top-down cognitive-decisional strategies. With increasing age, speech perception in noise is associated with increased activity in a compensatory network of frontal-motor regions and reduced activity in core auditory sensory-perceptual network. In short, when listening to speech in noise, younger adults rely on clear and stable (bottom-up) speech representations in core auditory regions and older adults tend to leverage higher-level (top-down) compensatory mechanisms that require additional working memory and cognitive resources. While neuroimaging methodologies have the potential to identify compensatory network changes, these methods are not a scalable solution for clinical diagnostics. In this line of work, I aim to understand the sensory-cognitive deficits that drive speech perceptual challenges under different listening conditions using neurobiologically-grounded computational approaches that model behavior. The overarching goal is to develop behavioral-based assessments of speech perception in noise abilities that can complement clinical audiology to inform rehabilitation approaches and counseling.

Associated References:

1. McHaney, J. R., Roark, C. L., McGinley, M. J., & Chandrasekaran, B. (2024). Combining pupillometry and drift-diffusion models reveals auditory category learning dynamics. bioRxiv (in revision). doi: 10.1101/2024.04.16.589753

2. McHaney, J. R. (2023). Sensory and Cognitive Factors Underlying Self-Perceived Listening Difficulties in Adults with Normal Hearing Thresholds (Doctoral dissertation, University of Pittsburgh).

3. Roark, C. L., Paulon, G., Rebaudo, G., McHaney, J. R., Sarkar, A., & Chandrasekaran, B. (2024). Individual differences in working memory impact the trajectory of non-native speech category learning. PLOS ONE, 19(6), e0297917.

4. Mukhopadhyay, M., McHaney, J. R., Chandrasekaran, B., & Sarkar, A. (2024). Bayesian semiparametric longitudinal inverse-probit mixed models for category learning. Psychometrika, 1-25.