Quantifying spatial uncertainty of visual area boundaries in neuroimaging data

Functional magnetic resonance imaging (fMRI) of the human brain has provided much information about visual cortex. These insights hinge on researchers' ability to identify cortical areas based on stimulus selectivity and retinotopic mapping. However, border identification around regions of interest or between retinotopic maps is often performed without characterizing the degree of certainty associated with the location of these key features; ideally, assertions about the location of boundaries would have an associated spatial confidence interval. We describe an approach that allows researchers to transform estimates of error in the intensive dimension (i.e., activation of voxels) to the spatial dimension (i.e., the location of features evident in patterns across voxels). We implement the approach by bootstrapping, with applications to: (1) the location of human MT+ and (2) the location of the V1/V2 boundary. The transformation of intensive to spatial error furnishes graphical, intuitive characterizations of spatial uncertainty akin to error bars on the borders of visual areas, instead of the conventional practice of computing and thresholding p-values for voxels. This approach provides a general, unbiased arena for evaluating: (1) competing conceptions of visual area organization; (2) analysis technique efficacy; and (3) data quality.