External and Internal Signal-to-noise Ratios Alter Timing and Location of Cortical Activities During Speech-in-noise Perception

• Published in: bioRxiv
• Main Authors: Subong Kim, Schwalje, Adam T, Liu, Andrew S, Gander, Phillip E, Mcmurray, Bob, Griffiths, Timothy D, Choi, Inyong
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 28.10.2019
• Subjects: Cortex; EEG; Frontal gyrus; Noise; Speech perception
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/817460

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. Characterizing central functions that exhibit individual differences during a SiN task and finding their relative contributions to predicting SiN performance can reveal key neural mechanisms of SiN understanding. Here, we elucidated a few cortical functions involved during a SiN task and their hierarchical relationship using both within- and across-subject approaches. Through our within-subject analysis of source-localized electroencephalography, we demonstrated how acoustic signal-to-noise ratio (SNR) alters neural activities along the auditory-motor pathway, or dorsal stream, of speech perception. In quieter noise, left supramarginal gyrus (SMG, BA40) exhibited dominant activity at an early timing (~300 ms after word onset). In contrast, in louder noise, left inferior frontal gyrus (IFG, BA44) showed dominant activity at a later timing (~700 ms). Further, through an individual differences approach, we showed that listeners show different neural sensitivity to the background noise and target speech, reflected in the amplitude ratio of cortical responses to speech and noise, named as an "internal SNR." We found the "softer noise" pattern of activity in listeners with better internal SNR, who also performed better. This result implies that how well a listener "unmask" target speech from noise determines the subsequent speech analysis and SiN performance.