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Pre- and post-target cortical processes predict speech-in-noise performance

Lookup NU author(s): Professor Tim Griffiths

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Abstract

© 2020 The Author(s)Understanding speech in noise (SiN) is a complex task that recruits multiple cortical subsystems. There is a variance in individuals’ ability to understand SiN that cannot be explained by simple hearing profiles, which suggests that central factors may underlie the variance in SiN ability. Here, we elucidated a few cortical functions involved during a SiN task and their contributions to individual variance using both within- and across-subject approaches. Through our within-subject analysis of source-localized electroencephalography, we investigated how acoustic signal-to-noise ratio (SNR) alters cortical evoked responses to a target word across the speech recognition areas, finding stronger responses in left supramarginal gyrus (SMG, BA40 the dorsal lexicon area) with quieter noise. Through an individual differences approach, we found that listeners show different neural sensitivity to the background noise and target speech, reflected in the amplitude ratio of earlier auditory-cortical responses to speech and noise, named as an internal SNR. Listeners with better internal SNR showed better SiN performance. Further, we found that the post-speech time SMG activity explains a further amount of variance in SiN performance that is not accounted for by internal SNR. This result demonstrates that at least two cortical processes contribute to SiN performance independently: pre-target time processing to attenuate neural representation of background noise and post-target time processing to extract information from speech sounds.


Publication metadata

Author(s): Kim S, Schwalje AT, Liu AS, Gander PE, McMurray B, Griffiths TD, Choi I

Publication type: Article

Publication status: Published

Journal: NeuroImage

Year: 2021

Volume: 228

Online publication date: 30/12/2020

Acceptance date: 23/01/2020

ISSN (print): 1053-8119

ISSN (electronic): 1095-9572

Publisher: Academic Press Inc.

URL: https://doi.org/10.1016/j.neuroimage.2020.117699

DOI: 10.1016/j.neuroimage.2020.117699


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