Multivariate pattern analysis reveals common neural patterns across individuals during touch observation.

In a recent study we found that multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data could predict which of several touch-implying video clips a subject saw, only using voxels from primary somatosensory cortex. Here, we re-analyzed the same dataset using cross-individual MVPA to locate patterns of information that were common across participants' brains. In this procedure a classifier learned to distinguish the neural patterns evoked by each stimulus based on the data from a sub-group of the subjects and was then tested on data from an individual that was not part of that sub-group. We found prediction performance to be significantly above chance both when using voxels from the whole brain and when only using voxels from the postcentral gyrus. SVM voxel weight maps established based on the whole-brain analysis as well as a separate searchlight analysis suggested foci of especially high information content in medial and lateral occipital cortex and around the intraparietal sulcus. Classification across individuals appeared to rely on similar brain areas as classification within individuals. These data show that observing touch leads to stimulus-specific patterns of activity in sensorimotor networks and that these patterns are similar across individuals. More generally, the results suggest that cross-individual MVPA can succeed even when applied to restricted regions of interest.