We present SpaceNet, a multivariate method for brain decoding and segmentation. SpaceNet uses priors like TV (Total Variation). SpaceNet uses priors like TV (Total Variation) [Michel et al. 2011], TV-L1 [Baldassarre et al. 2012, Gramfort et al. 2013], and GraphNet / Smooth-Lasso [Hebiri et al. 2011, Grosenick et al. 2013] to regularize / penalize classification and regression problems in brain imaging. The result are brain maps which are both sparse (i.e regression coefficients are zero everywhere, except at predictive voxels) and structured (blobby). The superiority of such priors over methods without structured priors like the Lasso, SVM, ANOVA, Ridge, etc. for yielding more interpretable maps and improved classification / prediction scores is now well-established [Baldassarre et al. 2012, Gramfort et al. 2013, Grosenick et al. 2013]. In addition, such priors lead to state-of-the-art methods for extracting brain atlases [Abraham et al. 2013].