Abstract
Recent advances in retinotopic modeling have led to the development of retinotopic template maps that depend only on the sulcal topology of the individual subject yet predict individual retinotopy with high accuracy (Benson et al., 2014, PLoS Comput. Biol. 10:e1003538). These templates are constructed via registration of the aggregate retinotopic map of many subjects to a retinotopic model and thus provide a strong prior for the retinotopic organization of an individual subject. We ask whether empirical retinotopic data measured in individual subjects combined with the template prior have greater prediction accuracy than template maps alone, empirical maps alone, and smoothed empirical maps. We examine the quality of retinotopic predictions in 28 subjects whose V1-V3 retinotopic maps were measured out to 10° (21 subjects), 20° (6 subjects), and 48° of eccentricity (1 subject) using fMRI. Refined templates were produced by registering the empirical map to a retinotopic model using the registration of the aggregate-based template map as the starting position for the registration. When the refined map derived from a low quality partial scan of a subject is compared to the empirical map derived from the remaining scan, we found that the refined templates (median absolute errors: 0.67° eccentricity, 20.34° polar angle) were more accurate than aggregate-based template maps (1.34° eccentricity, 20.55° polar angle). Additionally, the ability to predict the retinotopic organization outside of the extent of the stimulus is slightly improved in the refined templates (0.44° eccentricity, 18.03° polar angle) versus the aggregate templates (1.44° eccentricity, 21.10° polar angle). Finally, the refined maps have better representation of the vertical meridia than either empirical or smoothed empirical maps and eliminate the edge biases from smoothing. Combining a template model with measured data provides a better representation of an individual’s retinotopic map than either data or template alone.
Meeting abstract presented at VSS 2015