Abstract
Speech-in-noise (SIN) perception is a complex cognitive skill that affects social, vocational, and educational activities. Poor SIN ability particularly affects young and elderly populations, yet varies considerably even among healthy young adults with normal hearing. Although SIN skills are known to be influenced by top-down processes that can selectively enhance lower-level sound representations, the complementary role of feed-forward mechanisms and their relationship to musical training is poorly understood. Using a paradigm that minimizes the main top-down factors that have been implicated in SIN performance such as working memory, we aimed to better understand how robust encoding of periodicity in the auditory system (as measured by the frequency-following response) contributes to SIN perception. Using magnetoencephalograpy, we found that the strength of encoding at the fundamental frequency in the brainstem, thalamus, and cortex is correlated with SIN accuracy. The amplitude of the slower cortical P2 wave was previously also shown to be related to SIN accuracy and FFR strength; we use MEG source localization to show that the P2 wave originates in a temporal region anterior to that of the cortical FFR. We also confirm that the observed enhancements were related to the extent and timing of musicianship. These results are consistent with the hypothesis that basic feed-forward sound encoding affects SIN perception by providing better information to later processing stages, and that modifying this process may be one mechanism through which musical training might enhance the auditory networks that subserve both musical and language functions.
Highlights
Understanding the neural bases of good speech-in-noise (SIN) perception during development, adulthood, and into old age is both clinically and scientifically important
Differences in the strength and fidelity of the fundamental frequency (f0) of the frequency-following response (FFR) have been linked to SIN perception such that increased FFR amplitude is associated with better performance
We considered the spatial relationship between FFR-f0 generators and the source of the SIN-sensitive P2 wave by inspecting the FFR-f0 > Baseline and P2 > Baseline maps in the MEG data, and calculated Spearman’s correlations between the FFR-f0 strength from each auditory cortex regions of interest (ROIs) (MEG) and the amplitude of the P2 wave measured with EEG
Summary
Understanding the neural bases of good speech-in-noise (SIN) perception during development, adulthood, and into old age is both clinically and scientifically important. Enhancements and deficits of neural correlates that are related to SIN perception are most consistently observed either when the FFR is measured in very challenging listening conditions (e.g., Parbery-Clark et al, 2009b), in the degree of degradation of the FFR signal between quiet and noisy conditions (e.g., Cunningham et al, 2001; Parbery-Clark et al, 2011a; Song et al, 2011), or in the magnitude of enhancement that is conferred by predictability within a sound stream (e.g., Parbery-Clark et al, 2011c). F0 representation in the FFR may be enhanced by training (Song et al, 2008, 2012) and is often observed to be stronger among musicians even to sounds presented in silence (e.g., Musacchia et al, 2007), suggesting that learning mechanisms related to identifying task-relevant features and possibly attention might act to bias and enhance incoming acoustic information and suppress noise (Suga, 2012) Enhancements and deficits of neural correlates that are related to SIN perception are most consistently observed either when the FFR is measured in very challenging listening conditions (e.g., Parbery-Clark et al, 2009b), in the degree of degradation of the FFR signal between quiet and noisy conditions (e.g., Cunningham et al, 2001; Parbery-Clark et al, 2011a; Song et al, 2011), or in the magnitude of enhancement that is conferred by predictability within a sound stream (e.g., Parbery-Clark et al, 2011c). f0 representation in the FFR may be enhanced by training (Song et al, 2008, 2012) and is often observed to be stronger among musicians even to sounds presented in silence (e.g., Musacchia et al, 2007), suggesting that learning mechanisms related to identifying task-relevant features and possibly attention might act to bias and enhance incoming acoustic information and suppress noise (Suga, 2012)
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