The Group-Constrained Subject-Specific (GSS) method is designed to algorithmically discover functional regions of interest (fROIs) that are activated systematically across subjects. This method is similar to a random effects analysis, but is more tolerant of anatomical variability across subjects. Detailed information about this how method can be used to define ventral visual stream fROIs can be found here. For information about using this method for language localization, see Fedorenko et al. (2010) and this website.
|1. We overlaid individual subject's thresholded (p < 0.0001) activation maps on top of one another in a common space, creating probabilistic overlap maps. Each voxel in the overlap maps contains information about the number of subjects that show a significant effect in that voxel. We did this for four contrasts of interest (faces>objects, scenes>objects, bodies>objects, and objects>scrambled). For a faces > objects contrast, for example, the overlap map looks as follows, where the heat of each voxel corresponds to the percentage of subjects that have activation at that voxel:|
|2. Using a watershed image segmentation algorithm, the overlap maps were divided into functional "parcels" following the map's topography. We then considered all parcels in which ≥ 60% of subjects show the relevant activation. For faces > objects here are our parcels:|
|3. To use these parcels as spatial constraints to algorithmically select a fROI for each region in each subject, just intersect each parcel (black outlines) with each individual subjects' corresponding thresholded activation map. For example, for faces > objects in one subject we see:|
|In particular, the subject-specific fROIs were defined as the activation that falls within the boundaries of each parcel.|