O. Yamashita, M. Sato, T. Yoshioka, F. Tong, and Y. Kamitani, Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns, NeuroImage, vol.42, issue.4, p.1414, 2008.
DOI : 10.1016/j.neuroimage.2008.05.050

N. Kriegeskorte, R. Goebel, and P. Bandettini, Information-based functional brain mapping, Proceedings of the National Academy of Sciences, vol.103, issue.10, p.3863, 2006.
DOI : 10.1073/pnas.0600244103
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1383651

B. Gaonkar and C. Davatzikos, Deriving Statistical Significance Maps for SVM Based Image Classification and Group Comparisons, Proc. MICCAI conf, p.723, 2012.
DOI : 10.1007/978-3-642-33415-3_89

K. Jimura and R. Poldrack, Analyses of regional-average activation and multivoxel pattern information tell complementary stories, Neuropsychologia, vol.50, issue.4, p.544, 2012.
DOI : 10.1016/j.neuropsychologia.2011.11.007

L. Baldassarre, J. Mourao-miranda, and M. Pontil, Structured Sparsity Models for Brain Decoding from fMRI Data, 2012 Second International Workshop on Pattern Recognition in NeuroImaging, p.5, 2012.
DOI : 10.1109/PRNI.2012.31

]. L. Grosenick, B. Klingenberg, K. Katovich, B. Knutson, and J. E. Taylor, Interpretable whole-brain prediction analysis with GraphNet, NeuroImage, vol.72, issue.6, p.304, 2013.
DOI : 10.1016/j.neuroimage.2012.12.062

V. Michel, A. Gramfort, G. Varoquaux, E. Eger, and B. Thirion, Total Variation Regularization for fMRI-Based Prediction of Behavior, IEEE Transactions on Medical Imaging, vol.30, issue.7, p.1328, 2011.
DOI : 10.1109/TMI.2011.2113378

A. Gramfort, M. Kowalski, and M. Hämäläinen, Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods, Physics in Medicine and Biology, vol.57, issue.7, 1937.
DOI : 10.1088/0031-9155/57/7/1937
URL : https://hal.archives-ouvertes.fr/hal-00690774

A. Chambolle and T. Pock, A First-Order Primal-Dual Algorithm for Convex Problems with??Applications to Imaging, Journal of Mathematical Imaging and Vision, vol.60, issue.5, p.120, 2011.
DOI : 10.1007/s10851-010-0251-1
URL : https://hal.archives-ouvertes.fr/hal-00490826

F. Pedregosa, G. Varoquaux, and A. Gramfort, Scikitlearn: Machine Learning in Python, Journal of Machine Learning Research, vol.12, p.2825, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

G. Varoquaux, A. Gramfort, and B. Thirion, Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering, ICML conf, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00705192