Using Thresholds in Noise to Identify Hidden Hearing Loss in Humans

Area of Science:

• Auditory Neuroscience
• Audiology
• Statistical Modeling

Background:

• Hidden hearing loss (HHL) is a condition suggested by animal studies involving noise-induced synaptopathy, potentially affecting auditory nerve fibers crucial for processing moderate to high-level sounds.
• The effects of synaptopathy may be more pronounced in suprathreshold auditory measures, particularly in noisy environments, as these fibers are more resistant to masking.

Purpose of the Study:

• To develop a statistical model for estimating HHL in humans.
• To utilize thresholds in noise as the primary outcome variable, with measures of auditory pathway integrity as explanatory variables.

Main Methods:

• The study included 13 adults with normal hearing and 20 with normal hearing at 1 kHz and sensorineural hearing loss at 4 kHz.
• Thresholds in noise were measured, and the "thresholds-in-noise residual" (variance not explained by thresholds in quiet) was the outcome.
• Explanatory variables included auditory brainstem response (ABR) and electrocochleography (ECoG) measures, distortion product otoacoustic emissions (DPOAEs), and categorical loudness scaling.

Main Results:

• The statistical model successfully predicted the thresholds-in-noise residual, accounting for 61% of the variance at 1 kHz (p < 0.01) and 48% at 4 kHz (p < 0.01).

Conclusions:

• Measures of thresholds in noise, the summating potential to action potential ratio, and ABR amplitudes show promise for predicting HHL in humans.
• This statistical approach offers a quantifiable method for estimating HHL, consistent with inner hair cell and auditory nerve pathology underlying suprathreshold auditory deficits.