hide
Free keywords:
-
Abstract:
Introduction:
Voices and faces are among the most salient cues in human life. This is reflected in the existence of specialized cerebral modules which are specifically tuned to respond to these cues (e.g., the temporal voice area [TVA][1] and the fusiform face area [FFA][2]). While many studies have investigated cerebral voice- and face-selectivity in the presence of voices and faces, it remains unclear if there is something like voice- or face-selectivity in the absence of these cues.
Methods:
60 healthy individuals participated in the 3T-fMRI study. An rs-fMRI measurement was followed by standard localizer experiments identifying the FFA and TVA and combinedly voice- and face-sensitive regions in the posterior superior temporal sulcus (pSTS [3]). Employing the conn toolbox [4] in SPM8, seed-based analyses and multivariate multi voxel pattern analyses were used to identify resting state functional connectivity (RSFC) patterns predicting the voice- and/or face-selectivity of TVA, FFA and pSTS.
Results:
While individual voice-selectivity was partially reflected in the RSFC patterns of the respective voice-processing modules (i.e., TVA and pSTS), face-selectivity of the FFA (see Fig. 1) (and partially voice-selectivity [e.g., voice-selectivity of the bilateral TVAs; see Fig. 2) was predicted by the connectivity patterns of widespread but to some extent convergent sets of regions cortical and subcortical regions not including the respective face- or voice-selective module itself.
Conclusions:
These results emphasize that the individual cerebral propensity to respond to human voices and faces is reflected in the brain's activation patterns also in the absence of these cues like a trait marker. Similar to the under-water-perspective on an iceberg, this approach may open up interesting avenues to the investigation of voice and face processing. In this regard, the resting state connectivity patterns predictive of individual voice- and face-selectivity may aid the understanding of cerebral face- and voice-selectivity from a network perspective.
