J. V. Haxby, M. I. Gobbini, M. L. Furey, A. Ishai, J. L. Schouten et al., Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex, Science, vol.293, issue.5539, p.2425, 2001.
DOI : 10.1126/science.1063736

Y. Kamitani and F. Tong, Decoding the visual and subjective contents of the human brain, Nature Neuroscience, vol.15, issue.5, p.679, 2005.
DOI : 10.1097/00004728-199801000-00027

J. Haynes and G. Rees, Decoding mental states from brain activity in humans, Nature Reviews Neuroscience, vol.16, issue.7, p.523, 2006.
DOI : 10.1038/nrn1931

Y. Fan, N. Batmanghelich, C. M. Clark, and C. Davatzikos, Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline, NeuroImage, vol.39, issue.4, p.1731, 2008.
DOI : 10.1016/j.neuroimage.2007.10.031

M. K. Carroll, G. A. Cecchi, I. Rish, R. Garg, and A. R. Rao, Prediction and interpretation of distributed neural activity with sparse models, NeuroImage, vol.44, issue.1, p.112, 2009.
DOI : 10.1016/j.neuroimage.2008.08.020

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

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

A. Gramfort, B. Thirion, and G. Varoquaux, Identifying Predictive Regions from fMRI with TV-L1 Prior, 2013 International Workshop on Pattern Recognition in Neuroimaging, 2013.
DOI : 10.1109/PRNI.2013.14
URL : https://hal.archives-ouvertes.fr/hal-00839984

A. Abraham, E. Dohmatob, B. Thirion, D. Samaras, and G. Varoquaux, Extracting Brain Regions from Rest fMRI with Total-Variation Constrained Dictionary Learning, 2013.
DOI : 10.1007/978-3-642-40763-5_75
URL : https://hal.archives-ouvertes.fr/hal-00853242

I. Daubechies, M. Defrise, and C. Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Communications on Pure and Applied Mathematics, vol.58, issue.11, pp.1413-1457, 2004.
DOI : 10.1002/cpa.20042

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, p.183, 2009.
DOI : 10.1137/080716542

J. A. Suykens, T. Van-gestel, J. De-brabanter, B. De, J. Moor et al., Least squares support vector machines, World Scientific, 2002.
DOI : 10.1142/5089

A. Beck and M. Teboulle, Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems, IEEE Transactions on Image Processing, vol.18, issue.11, p.2419, 2009.
DOI : 10.1109/TIP.2009.2028250

C. L. Guo-xun-yuan and C. Ho, An improved glmnet for l1-regularized logistic regression, 2012.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Machine Learning, 2010.
DOI : 10.1561/2200000016

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

A. Lewis and M. Overton, Nonsmooth optimization via BFGS, 2008.
DOI : 10.1007/s10107-012-0514-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.365.712

J. Burke, A. Lewis, and M. Overton, A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization, SIAM Journal on Optimization, vol.15, issue.3, pp.751-779, 2005.
DOI : 10.1137/030601296

J. Bobin, S. Becker, and E. Candes, A fast and accurate first-order method for sparse recovery, SIAM J Imaging Sciences, 2011.

Y. Nesterov, Smooth minimization of non-smooth functions, Ser. A 103, p.127152, 2005.
DOI : 10.1007/s10107-004-0552-5

A. Beck and M. Teboulle, Smoothing and First Order Methods: A Unified Framework, SIAM Journal on Optimization, vol.22, issue.2, pp.557-580, 2012.
DOI : 10.1137/100818327
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.307.7067

C. Zhu, R. H. Byrd, P. Lu, and J. Nocedal, Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization, ACM Transactions on Mathematical Software, vol.23, issue.4, 1994.
DOI : 10.1145/279232.279236

Y. Nesterov, A method of solving a convex programming problem with convergence rate, 1983.

S. M. Tom, C. R. Fox, C. Trepe, and R. A. Poldrack, The Neural Basis of Loss Aversion in Decision-Making Under Risk, Science, vol.315, issue.5811, pp.515-518, 2007.
DOI : 10.1126/science.1134239

K. Jimura and R. A. 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