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. 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, 2009.
DOI : 10.1109/TIP.2009.2028250

A. Beck and M. Teboulle, A fast iterative shrinkage-thresholding algorithm with application to linear inverse problems, SIAM, vol.2, pp.183-202, 2009.

E. Candes and J. Romberg, Signal recovery from random projections, Wavelet Applications in Signal and Image Processing XI, SPIE, p.76, 2005.

T. F. Chan and L. A. Vese, Active contours without edges. Image processing, IEEE transactions on, vol.10, p.266, 2001.
DOI : 10.1109/83.902291
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.1828

P. L. Combettes and J. C. Pesquet, Proximal Splitting Methods in Signal Processing, Inverse Problems in Science and Engineering, 2011.
DOI : 10.1007/978-1-4419-9569-8_10
URL : https://hal.archives-ouvertes.fr/hal-00643807

E. Dohmatob, A. Gramfort, B. Thirion, and G. Varoquaux, Benchmarking solvers for TV-l1 least-squares and logistic regression in brain imaging, PRNI, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00991743

S. Durrleman, M. Prastawa, G. Gerig, and S. Joshi, Optimal data-driven sparse parameterization of diffeomorphisms for population analysis, In: IPMI. p, p.123, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00818405

A. Gramfort, B. Thirion, and G. Varoquaux, Identifying Predictive Regions from fMRI with TV-L1 Prior, 2013 International Workshop on Pattern Recognition in Neuroimaging, pp.17-20, 2013.
DOI : 10.1109/PRNI.2013.14
URL : https://hal.archives-ouvertes.fr/hal-00839984

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

J. Haxby, I. Gobbini, and M. Furey, 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

B. M. Kandel, D. A. Wolk, J. C. Gee, and B. Avants, Predicting Cognitive Data from Medical Images Using Sparse Linear Regression, p.86, 2013.
DOI : 10.1007/978-3-642-38868-2_8

D. S. Marcus, T. H. Wang, and J. Parker, Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults, Journal of Cognitive Neuroscience, vol.58, issue.9, p.1498, 2007.
DOI : 10.1109/42.906424

V. Michel, A. Gramfort, and G. Varoquaux, 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

B. Ng, A. Vahdat, and G. Hamarneh, Generalized Sparse Classifiers for Decoding Cognitive States in fMRI, Machine Learning in Medical Imaging, p.108, 2010.
DOI : 10.1007/978-3-642-15948-0_14

T. Pock, A. Chambolle, D. Cremers, and H. Bischof, A convex relaxation approach for computing minimal partitions, 2009 IEEE Conference on Computer Vision and Pattern Recognition, p.810, 2009.
DOI : 10.1109/CVPR.2009.5206604

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, p.259, 1992.
DOI : 10.1016/0167-2789(92)90242-F

M. Schmidt, N. L. Roux, and F. R. Bach, Convergence rates of inexact proximalgradient methods for convex optimization, p.1458, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00618152

C. Stonnington, C. Chu, and S. Klöppel, Predicting clinical scores from magnetic resonance scans in Alzheimer's disease, NeuroImage, vol.51, issue.4, p.1405, 2010.
DOI : 10.1016/j.neuroimage.2010.03.051

S. M. Tom, C. R. Fox, C. Trepel, and R. A. Poldrack, The Neural Basis of Loss Aversion in Decision-Making Under Risk, Science, vol.315, issue.5811, pp.515-523, 2007.
DOI : 10.1126/science.1134239

O. Yamashita, M. A. Sato, and T. Yoshioka, 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