SpaceNet: Multivariate brain decoding and segmentation

Abstract : 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].
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Poster
OHBM, Jun 2015, Honolulu, Hawaii, United States
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https://hal.inria.fr/hal-01187230
Contributeur : Elvis Dohmatob <>
Soumis le : mercredi 26 août 2015 - 12:51:51
Dernière modification le : lundi 8 janvier 2018 - 08:24:01
Document(s) archivé(s) le : vendredi 27 novembre 2015 - 10:59:28

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  • HAL Id : hal-01187230, version 1

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Elvis Dohmatob, Michael Eickenberg, Bertrand Thirion, Gaël Varoquaux. SpaceNet: Multivariate brain decoding and segmentation. OHBM, Jun 2015, Honolulu, Hawaii, United States. 〈hal-01187230〉

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