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The role of neuronal oscillations in the processing of speech has recently come to prominence. Since resting-state (RS) brain activity has been shown to predict both task-related brain activation and behavioural performance, we set out to establish whether inter-individual differences in spectrally-resolved RS-MEG power are associated with variations in words-in-noise recognition in a sample of 88 participants made available by the Human Connectome Project. Positive associations with resilience to noise were observed with power in the range 21 and 29 Hz in a number of areas along the left temporal gyrus and temporo-parietal association areas peaking in left posterior superior temporal gyrus (pSTG). Significant associations were also found in the right posterior superior temporal gyrus in the frequency range 30–40 Hz. We propose that individual differences in words-in-noise performance are related to baseline excitability levels of the neural substrates of phonological processing.

The role of neuronal oscillations in the processing of speech has recently come to prominence. Since resting-state (RS) brain activity has been shown to predict both task-related brain activation and behavioural performance, we set out to establish whether inter-individual differences in spectrally-resolved RS-MEG power are associated with variations in speech in noise recognition. In a frequency range between 21 and 29 Hz, significant positive correlations were observed between voxelwise power and resilience to noise in a large left-lateralised perisylvian cluster spanning from inferior frontal gyrus to temporo-parietal junction and peaking in left posterior superior temporal gyrus (pSTG). Smaller areas of association were also found around the right pSTG (21-29Hz) and bilateral pSTG (30-40Hz). These findings are spatially and spectrally consistent with the neural substrates of phonological processing. We propose that increased RS power in auditory cortices and the left perisylvian region can partly explain improved resilience to noise.HighlightsPower of resting MEG activity predicts Words-In-Noise recognition performanceSignificant associations in higher beta and lower gamma frequency bandStrongest in left-lateralised perisylvian cluster peaking in posterior STGEffects are spectrally and spatially consistent with phoneme-level processing