High-Res fMRI PCA Analysis of Face Recognizing Cortex
A recent study by Grill-Spector, Sayres, and Ress uses high-resolution fMRI imaging to explore the fusiform face area, a part of the temporal lobe known to activate when looking at faces.
They do a PCA analysis on their study and find 3 principal components that account for 95% of the variance, and the components related to 1) faces, 2) sculptures/cars and 3) animals. From the study:
Our results suggest two hypotheses for the functional organization in this part of cortex. First, the face- and nonface-selective subregions may be part of a common cortical region, which processes both face and nonface stimuli. Alternatively, the face-selective subregions may constitute the ‘‘true FFA’’ (and may contain only highly selective face neurons), whereas the other subregions may comprise a segregated subsystem. However, the fact that face-selective patches are not spatially contiguous on the cortex (Fig. 5) raises the question of which of them might be considered the FFA, or whether these spatially segregated subregions might behave functionally as a computational unit. Future studies may elucidate whether these face patches are interconnected, which would allow them to operate as one computational unit (for example, by studying connectivity between subregions in the FFA).
The study also contrasts the use of high-resolution fMRI, capable of resolving at 1mm voxels with the standard fMRI, capable of resolving at 3mm.