Abstract
The ventral visual stream is comprised of numerous regions selective for specific high-level visual categories. While generating an areal map of the brain is a century-long endeavor, no approach is yet able to accurately identify functionally-selective high level visual regions on an individual subject basis in the absence of a task-based fMRI localizer. Our and others’ previous work has demonstrated a tight link between brain circuitry and function, at the fine-grain of single voxels from individual subjects and reflecting individual variation therein. Can connectivity reliably identify high-level visual functional regions of interest (fROIs), in place of well-established functional localizers? If so, a single 10-minute resting state scan could be used in lieu of myriad localizers, saving researchers an enormous amount of scanning time, effort, and funding. Further, these models illuminate the neural circuitry that best define each brain region, and are strong candidates of the underlying mechanisms that govern visual selectivity. We scanned 40 participants with functional localizers for visually-selective regions involved in the perception of faces (FFA, OFA, STS), scenes (PPA, RSC, TOS), bodies (EBA), and objects (LOC, PFS). We designed linear models to predict the location of each ROI using resting-state (functional connectivity, FC). These models were able to accurately identify face, scene, body, and object selective voxels in all cases, and could reliably localize each fROI for any given participant. These FC-ROIs were selective to the expected category of interest, similar to the fROIs identified with the functional localizer task. They also outperformed probabilistic parcels, as well as the closest matching region from other areal maps/atlases purported to reflect functional subdivisions of the brain, e.g. Glasser atlas. Thus, a single resting-state scan can efficiently replace an entire set of functional localizers for high-level vision, offering practical and scientific advantages.