1.. _space_net: 2 3========================================================== 4SpaceNet: decoding with spatial structure for better maps 5========================================================== 6 7The SpaceNet decoder 8===================== 9 10:class:`nilearn.decoding.SpaceNetRegressor` and :class:`nilearn.decoding.SpaceNetClassifier` 11implements spatial penalties which improve brain decoding power as well as decoder maps: 12 13* penalty="tvl1": priors inspired from TV (Total Variation) [:footcite:t:`michel:inria-00563468`], TV-L1 [:footcite:t:`Baldassarre2012`], [:footcite:t:`gramfort:hal-00839984`]. 14 15* penalty="graph-net": GraphNet prior [:footcite:t:`GROSENICK2013304`]. 16 17These regularize :term:`classification` and :term:`regression` 18problems in brain imaging. The results are brain maps which are both 19sparse (i.e regression coefficients are zero everywhere, except at 20predictive :term:`voxels<voxel>`) and structured (blobby). The superiority of TV-L1 21over methods without structured priors like the Lasso, :term:`SVM`, :term:`ANOVA`, 22Ridge, etc. for yielding more interpretable maps and improved 23prediction scores is now well established [:footcite:t:`Baldassarre2012`], [:footcite:t:`gramfort:hal-00839984`], [:footcite:t:`GROSENICK2013304`]. 24 25Note that TV-L1 prior leads to a difficult optimization problem, and so can be slow to run. 26Under the hood, a few heuristics are used to make things a bit faster. These include: 27 28- Feature preprocessing, where an F-test is used to eliminate 29 non-predictive :term:`voxels<voxel>`, thus reducing the size of the brain 30 mask in a principled way. 31- Continuation is used along the regularization path, where the 32 solution of the optimization problem for a given value of the 33 regularization parameter `alpha` is used as initialization 34 for the next regularization (smaller) value on the regularization 35 grid. 36 37**Implementation:** See [:footcite:t:`dohmatob:hal-01147731`] and [:footcite:t:`dohmatob:hal-00991743`] for technical details regarding the implementation of SpaceNet. 38 39Related example 40=============== 41 42:ref:`Age prediction on OASIS dataset with SpaceNet <sphx_glr_auto_examples_02_decoding_plot_oasis_vbm_space_net.py>`. 43 44.. figure:: ../auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_space_net_002.png 45 46.. note:: 47 48 Empirical comparisons using this method have been removed from 49 documentation in version 0.7 to keep its computational cost low. You can 50 easily try SpaceNet instead of FREM in :ref:`mixed gambles study <sphx_glr_auto_examples_02_decoding_plot_mixed_gambles_frem.py>` or :ref:`Haxby study <sphx_glr_auto_examples_02_decoding_plot_haxby_frem.py>`. 51 52.. seealso:: 53 54 :ref:`FREM <frem>`, a pipeline ensembling many models that yields very 55 good decoding performance at a lower computational cost. 56 57References 58========== 59 60.. footbibliography:: 61