D. D. Cox and R. L. Savoy, Functional magnetic resonance imaging (fMRI) ???brain reading???: detecting and classifying distributed patterns of fMRI activity in human visual cortex, NeuroImage, vol.19, issue.2, pp.261-270, 2003.
DOI : 10.1016/S1053-8119(03)00049-1

Y. Kamitani and F. Tong, Decoding the visual and subjective contents of the human brain, Nature Neuroscience, vol.15, issue.5, pp.679-685, 2005.
DOI : 10.1097/00004728-199801000-00027

P. Dayan and L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, 2001.

J. Haynes and G. Rees, Decoding mental states from brain activity in humans, Nature Reviews Neuroscience, vol.16, issue.7, pp.523-534, 2006.
DOI : 10.1038/nrn1931

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, pp.112-122, 2009.
DOI : 10.1016/j.neuroimage.2008.08.020

K. Friston, A. Holmes, K. Worsley, J. Poline, C. Frith et al., Statistical parametric maps in functional imaging: A general linear approach, Human Brain Mapping, vol.26, issue.4, pp.189-210, 1995.
DOI : 10.1002/hbm.460020402

D. Cordes, V. M. Haughtou, J. D. Carew, K. Arfanakis, and K. Maravilla, Hierarchical clustering to measure connectivity in fMRI resting-state data, Magnetic Resonance Imaging, vol.20, issue.4, pp.305-317, 2002.
DOI : 10.1016/S0730-725X(02)00503-9

M. Palatucci and T. Mitchell, Classification in Very High Dimensional Problems with Handfuls of Examples, Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2007.
DOI : 10.1007/978-3-540-74976-9_22

V. Michel, A. Gramfort, G. Varoquaux, and B. Thirion, Total Variation Regularization Enhances Regression-Based Brain Activity Prediction, 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging, pp.1-2, 2010.
DOI : 10.1109/WBD.2010.13

URL : https://hal.archives-ouvertes.fr/hal-00504095

N. Kriegeskorte, R. Goebel, and P. Bandettini, Information-based functional brain mapping, Proceedings of the National Academy of Sciences, vol.103, issue.10, pp.3863-3868, 2006.
DOI : 10.1073/pnas.0600244103

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1383651

G. Flandin, F. Kherif, X. Pennec, G. Malandain, N. Ayache et al., Improved Detection Sensitivity in Functional MRI Data Using a Brain Parcelling Technique, Medical Image Computing and Computer-Assisted Intervention (MICCAI'02), pp.467-474, 2002.
DOI : 10.1007/3-540-45786-0_58

URL : https://hal.archives-ouvertes.fr/inria-00615921

T. M. Mitchell, R. Hutchinson, R. S. Niculescu, F. Pereira, X. Wang et al., Learning to Decode Cognitive States from Brain Images, Machine Learning, vol.57, issue.1/2, pp.145-175, 2004.
DOI : 10.1023/B:MACH.0000035475.85309.1b

URL : http://repository.cmu.edu/cgi/viewcontent.cgi?article=2091&context=psychology

Y. Fan, D. Shen, and C. Davatzikos, Detecting cognitive states from fmri images by machine learning and multivariate classification, CVPRW '06: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop, p.89, 2006.

B. Thirion, G. Flandin, P. Pinel, A. Roche, P. Ciuciu et al., Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets, Human Brain Mapping, vol.22, issue.8, pp.678-693, 2006.
DOI : 10.1002/hbm.20210

K. Ugurbil, L. Toth, and D. Kim, How accurate is magnetic resonance imaging of brain function?, Trends in Neurosciences, vol.26, issue.2, pp.108-114, 2003.
DOI : 10.1016/S0166-2236(02)00039-5

D. Kontos, V. Megalooikonomou, D. Pokrajac, A. Lazarevic, Z. Obradovic et al., Extraction of Discriminative Functional MRI Activation Patterns and an Application to Alzheimer???s Disease, Med Image Comput Comput Assist Interv. MICCAI, pp.727-735, 2004.
DOI : 10.1007/978-3-540-30136-3_89

N. Tzourio-mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Etard et al., Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain, NeuroImage, vol.15, issue.1, pp.273-289, 2002.
DOI : 10.1006/nimg.2001.0978

M. Keller, M. Lavielle, M. Perrot, and A. Roche, Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis, p.12, 2009.
DOI : 10.1007/978-3-642-04271-3_55

B. Thyreau, B. Thirion, G. Flandin, and J. Poline, Anatomo-Functional Description of the Brain : A Probabilistic Approach, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, pp.1109-1112, 2006.
DOI : 10.1109/ICASSP.2006.1661474

S. Ghebreab, A. Smeulders, and P. Adriaans, Predicting brain states from fMRI data: Incremental functional principal component regression, Advances in Neural Information Processing Systems, pp.537-544, 2008.

L. He and I. R. Greenshields, An MRF spatial fuzzy clustering method for fMRI SPMs, Biomedical Signal Processing and Control, vol.3, issue.4, pp.327-333, 2008.
DOI : 10.1016/j.bspc.2008.06.003

P. Filzmoser, R. Baumgartner, and E. Moser, A hierarchical clustering method for analyzing functional MR images, Magnetic Resonance Imaging, vol.17, issue.6, pp.817-826, 1999.
DOI : 10.1016/S0730-725X(99)00014-4

A. Tucholka, B. Thirion, M. Perrot, P. Pinel, J. Mangin et al., Probabilistic Anatomo-Functional Parcellation of the Cortex: How Many Regions?, 11thProc. MICCAI, 2008.
DOI : 10.1007/978-3-540-85990-1_48

URL : https://hal.archives-ouvertes.fr/inria-00502805

J. Haynes and G. Rees, Predicting the orientation of invisible stimuli from activity in human primary visual cortex, Nature Neuroscience, vol.268, issue.5, pp.686-691, 2005.
DOI : 10.1162/089892900562561

P. Golland, Y. Golland, and R. Malach, Detection of Spatial Activation Patterns as Unsupervised Segmentation of fMRI Data, Med Image Comput Comput Assist Interv. MICCAI, issue.2, pp.110-118, 2007.
DOI : 10.1007/978-3-540-75757-3_14

V. Michel, E. Eger, C. Keribin, J. Poline, and B. , Thirion, A supervised clustering approach for extracting predictive information from brain activation images, MMBIA'10, p.11

J. H. Ward, Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, vol.58, issue.301, pp.236-244, 1963.
DOI : 10.1007/BF02289263

S. C. Johnson, Hierarchical clustering schemes, Psychometrika, vol.58, issue.4, pp.241-254, 1967.
DOI : 10.1007/BF02289588

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297
DOI : 10.1007/BF00994018

J. J. Oliver and D. J. Hand, On Pruning and Averaging Decision Trees, pp.430-437, 1995.
DOI : 10.1016/B978-1-55860-377-6.50060-8

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, J. Roy. Stat. Soc. B, vol.67, issue.301 6, 2005.

B. Krishnapuram, L. Carin, M. A. Figueiredo, and A. J. Hartemink, Sparse multinomial logistic regression: fast algorithms and generalization bounds, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.6, pp.957-968, 2005.
DOI : 10.1109/TPAMI.2005.127

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

G. Hughes, On the mean accuracy of statistical pattern recognizers, Information Theory, IEEE Transactions on, vol.14, issue.1, pp.55-63, 1968.

J. Friedman, T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, vol.33, issue.1
DOI : 10.18637/jss.v033.i01

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, 2001.
DOI : 10.1145/1961189.1961199

E. Eger, C. Kell, and A. Kleinschmidt, Graded Size Sensitivity of Object-Exemplar-Evoked Activity Patterns Within Human LOC Subregions, Journal of Neurophysiology, vol.100, issue.4, pp.2038-2085
DOI : 10.1152/jn.90305.2008

K. A. Norman, S. M. Polyn, G. J. Detre, and J. V. Haxby, Beyond mind-reading: multi-voxel pattern analysis of fMRI data, Trends in Cognitive Sciences, vol.10, issue.9, pp.424-430, 2006.
DOI : 10.1016/j.tics.2006.07.005