There is now a huge literature on methods for variable selection that use spike-and-slab priors. Such methods, in particular, have been quite successful for applications in a variety of different fields. High-throughput genomics and neuroimaging are two of such examples. There, novel methodological questions are being generated, requiring the integration of different concepts, methods, tools and data types. These have in particular motivated the development of variable selection priors that, for example, go beyond the independence assumptions of a simple Bernoulli prior on the inclusion indicators. In this talk I will review various prior constructions that incorporate information about structural dependencies among the variables. I will look in particular at models for neuroimaging applications, where specific structural information is incorporated into the prior probability models. I will also present models that incorporate information on connectivity among brain regions.