Abstract
A major question in human neuroscience is how cortical function is mapped onto surface anatomy. Retinotopic maps, which tile about a quarter of the cortical surface, have served as a testbed to address this question. Prior work has shown that the location and retinotopic organization of posterior visual field maps, V1-V3, tend to broadly align to the cortical folding pattern. Here, we develop a new Bayesian method to accurately model retinotopic organization in individual subjects, and demonstrate that there are substantial individual differences in the mapping between function (retinotopic organization) and structure, even after co-registration of the surface anatomies. The Bayesian method combines observation-a subject's retinotopic measurements from small amounts of fMRI time-with a prior-a retinotopic atlas produced from group-average measurements. This process, which we apply to human V1, V2, and V3, automatically draws areal boundaries, corrects discontinuities in the measured maps, and predicts validation retinotopy data more accurately than an atlas alone or, in most cases, an independent retinotopic dataset alone. The model accurately predicts map organization in the periphery, well beyond the region of visual space used for training the model. We use the Bayesian fits to characterize map properties, such as cortical magnification and the size of population receptive fields, and to assess the degree to which structure-function relationships differ between individuals. Further, we show how the method can be extended to 9 additional visual field maps. We propose that the Bayesian maps are more accurate than previous maps because they account for both regularities across subjects and individual differences in the relationship between anatomy and function.