Parts‐based representations of perceived face movements in the superior temporal sulcus

Facial motion is a primary source of social information about other humans. Prior fMRI studies have identified regions of the superior temporal sulcus (STS) that respond specifically to perceived face movements (termed fSTS), but little is known about the nature of motion representations in these regions. Here we use fMRI and multivoxel pattern analysis to characterize the representational content of the fSTS. Participants viewed a set of specific eye and mouth movements, as well as combined eye and mouth movements. Our results demonstrate that fSTS response patterns contain information about face movements, including subtle distinctions between types of eye and mouth movements. These representations generalize across the actor performing the movement, and across small differences in visual position. Critically, patterns of response to combined movements could be well predicted by linear combinations of responses to individual eye and mouth movements, pointing to a parts‐based representation of complex face movements. These results indicate that the fSTS plays an intermediate role in the process of inferring social content from visually perceived face movements, containing a representation that is sufficiently abstract to generalize across low‐level visual details, but still tied to the kinematics of face part movements.     

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.