Functional connectivity within the voice perception network and its behavioural relevance

Abstract

Recognizing who is speaking is a cognitive ability characterized by considerable individual differences, which could relate to the inter-individual variability observed in voice-elicited BOLD activity. Since voice perception is sustained by a complex brain network involving temporal voice areas (TVAs) and, even if less consistently, extra-temporal regions such as frontal cortices, functional connectivity (FC) during an fMRI voice localizer (passive listening of voices vs non-voices) has been computed within twelve temporal and frontal voice-sensitive regions ("voice patches") individually defined for each subject (N = 90) to account for inter-individual variability. Results revealed that voice patches were positively co-activated during voice listening and that they were characterized by different FC pattern depending on the location (anterior/posterior) and the hemisphere. Importantly, FC between right frontal and temporal voice patches was behaviorally relevant: FC significantly increased with voice recognition abilities as measured in a voice recognition test performed outside the scanner. Hence, this study highlights the importance of frontal regions in voice perception and it supports the idea that looking at FC between stimulus-specific and higher-order frontal regions can help understanding individual differences in processing social stimuli such as voices.

Keywords: Auditory cortex; Functional connectivity; Individual differences; Voice perception; Voice recognition; fMRI.

Fig. 1

Random effect analysis in 92 subjects. Suprathreshold clusters showing higher activation for the contrasts V vs NV (N = 92, p < 0.05 FWE voxel-level corrected, extent threshold = 0 mm³). The black-light blue points represent approximate location of the six TVAs. The frontal clusters where individual FVAs were defined are instead in the light blue circles (a = anterior; m = middle; p = posterior).

Fig. 2

Functional connectivity within the voice perception network. Axial view of the ROI-to-ROI FC (left; p-FDR seed-level corrected < 0.05) and connectivity matrix reporting t-values for the contrast V vs NV (right). The black squares in the connectivity matrix are used to visualize non-significant correlations at p-FDR seed-level corrected <0.05 or same-seed correlation. Note that both colorbars report t-test values between two seeds (DOF = 89), but the minimum value in the left colorbar is negative (−7.24) while in the connectivity matrix is 0. Lh = left hemisphere; rh = right hemisphere.

Fig. 3

Bar graphs representing differences in ROI-to-ROI FC between posterior and anterior TVAs (left graph) and between left and right FVAs (right graph). Error bars represent 95% confidence intervals (*p-FDR corrected (two-sided) < 0.05; **p-FDR corrected (two-sided) < 0.001).

Fig. 4

Correlation between FC and voice recognition scores. 1st row: distribution of percent correct responses for voices (left) and of the difference between voices and bells recognition (right); 2nd row: illustration of the FC between regions showing significant correlation with voice recognition scores; 3rd/4th row: scatterplots of the significant correlations between ROI-to-ROI FC and the two different scores (R² = coefficient of determination of Pearson correlation coefficient).