W. D. Penny, K. J. Friston, J. T. Ashburner, S. J. Kiebel, and T. E. Nichols, Statistical Parametric Mapping: The Analysis of Functional Brain Images, 2007.

R. A. Poldrack, J. A. Mumford, and T. E. Nichols, Handbook of Functional MRI Data Analysis, 2011.
DOI : 10.1017/CBO9780511895029

D. H. Hubel and T. N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex, The Journal of Physiology, vol.160, issue.1, 1962.
DOI : 10.1113/jphysiol.1962.sp006837

N. K. Logothetis, J. Pauls, and T. Poggio, Shape representation in the inferior temporal cortex of monkeys, Current Biology, vol.5, issue.5, p.552, 1995.
DOI : 10.1016/S0960-9822(95)00108-4

A. P. Georgopoulos, A. B. Schwartz, and R. E. Kettner, Neuronal population coding of movement direction, Science, vol.233, issue.4771, p.1416, 1986.
DOI : 10.1126/science.3749885

J. Freeman, G. J. Brouwer, D. J. Heeger, and E. P. Merriam, Orientation Decoding Depends on Maps, Not Columns, Journal of Neuroscience, vol.31, issue.13, p.4792, 2011.
DOI : 10.1523/JNEUROSCI.5160-10.2011

G. Tononi, G. M. Edelman, and O. Sporns, Complexity and coherency: integrating information in the brain, Trends in Cognitive Sciences, vol.2, issue.12, p.474, 1998.
DOI : 10.1016/S1364-6613(98)01259-5

T. Naselaris, K. N. Kay, S. Nishimoto, and J. L. Gallant, Encoding and decoding in fMRI, NeuroImage, vol.56, issue.2, p.400, 2011.
DOI : 10.1016/j.neuroimage.2010.07.073

S. Edelman, K. Grill-spector, T. Kushnir, and R. Malach, Toward direct visualization of the internal shape representation space by fMRI, Psychobiology, vol.26, p.309, 1998.

O. Doherty, J. P. Hampton, A. Kim, and H. , Model-Based fMRI and Its Application to Reward Learning and Decision Making, Annals of the New York Academy of Sciences, vol.22, issue.1, p.35, 2007.
DOI : 10.1016/j.conb.2006.03.006

K. N. Kay, T. Naselaris, R. J. Prenger, and J. L. Gallant, Identifying natural images from human brain activity, Nature, vol.79, issue.7185, p.352, 2008.
DOI : 10.1038/nature06713

T. M. Mitchell, S. V. Shinkareva, A. Carlson, K. Chang, V. L. Malave et al., Predicting Human Brain Activity Associated with the Meanings of Nouns, Science, vol.320, issue.5880, p.1191, 2008.
DOI : 10.1126/science.1152876

S. Nishimoto, A. T. Vu, T. Naselaris, Y. Benjamini, B. Yu et al., Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies, Current Biology, vol.21, issue.19, p.1641, 2011.
DOI : 10.1016/j.cub.2011.08.031

A. G. Huth, S. Nishimoto, A. T. Vu, and J. L. Gallant, A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain, Neuron, vol.76, issue.6, p.1210, 2012.
DOI : 10.1016/j.neuron.2012.10.014

D. Marr, Vision: A Computational Investigation Into the Human Representation and Processing of Visual Information, 1982.
DOI : 10.7551/mitpress/9780262514620.001.0001

Y. Lecun, K. Kavukcuoglu, and C. Farabet, Convolutional networks and applications in vision, Proceedings of 2010 IEEE International Symposium on Circuits and Systems, p.253, 2010.
DOI : 10.1109/ISCAS.2010.5537907

D. L. Yamins, H. Hong, C. F. Cadieu, E. A. Solomon, D. Seibert et al., Performance-optimized hierarchical models predict neural responses in higher visual cortex, Proceedings of the National Academy of Sciences, vol.111, issue.23, p.201403112, 2014.
DOI : 10.1073/pnas.1403112111

S. O. Dumoulin and B. A. Wandell, Population receptive field estimates in human visual cortex, NeuroImage, vol.39, issue.2, pp.647-660, 2008.
DOI : 10.1016/j.neuroimage.2007.09.034

L. Abbott, Decoding neuronal firing and modelling neural networks, Quarterly Reviews of Biophysics, vol.53, issue.03, p.291, 1994.
DOI : 10.1016/0166-2236(86)90053-6

S. Dehaene, L. Clec-'h, G. Cohen, L. Poline, J. Van-de-moortele et al., Inferring behavior from functional brain images, Nature Neuroscience, vol.388, issue.7, p.549, 1998.
DOI : 10.1038/2785

URL : https://hal.archives-ouvertes.fr/hal-00349936

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, p.678, 2006.
DOI : 10.1002/hbm.20210

J. V. Haxby, I. M. 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

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

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, p.424, 2006.
DOI : 10.1016/j.tics.2006.07.005

M. V. Peelen and P. E. Downing, Using multi-voxel pattern analysis of fMRI data to interpret overlapping functional activations, Trends in Cognitive Sciences, vol.11, issue.1, 2007.
DOI : 10.1016/j.tics.2006.10.009

R. Poldrack, Can cognitive processes be inferred from neuroimaging data? Trends in cognitive sciences 10, p.59, 2006.

R. A. Poldrack, Inferring Mental States from Neuroimaging Data: From Reverse Inference to Large-Scale Decoding, Neuron, vol.72, issue.5, p.692, 2011.
DOI : 10.1016/j.neuron.2011.11.001

T. D. Wager, L. Y. Atlas, M. A. Lindquist, M. Roy, C. Woo et al., An fMRI-Based Neurologic Signature of Physical Pain, New England Journal of Medicine, vol.368, issue.15, p.1388, 2013.
DOI : 10.1056/NEJMoa1204471

R. A. Poldrack, Y. O. Halchenko, and S. J. Hanson, Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals, Psychological Science, vol.28, issue.11, p.1364, 2009.
DOI : 10.1111/j.1467-9280.2009.02460.x

Y. Schwartz, B. Thirion, and G. Varoquaux, Mapping cognitive ontologies to and from the brain, p.NIPS, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00904763

R. A. Poldrack, D. M. Barch, J. P. Mitchell, T. D. Wager, A. D. Wagner et al., Toward open sharing of task-based fMRI data: the OpenfMRI project, Frontiers in Neuroinformatics, vol.7, 2013.
DOI : 10.3389/fninf.2013.00012

T. Yarkoni, R. A. Poldrack, T. E. Nichols, D. C. Van-essen, and T. D. Wager, Large-scale automated synthesis of human functional neuroimaging data, Nature Methods, vol.98, issue.8, p.665, 2011.
DOI : 10.1073/pnas.1102693108

S. Haufe, F. Meinecke, K. Görgen, S. Dähne, J. Haynes et al., On the interpretation of weight vectors of linear models in multivariate neuroimaging, NeuroImage, vol.87, pp.96-110, 2014.
DOI : 10.1016/j.neuroimage.2013.10.067

G. Varoquaux, A. Gramfort, and B. Thirion, Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering, p.1375, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00705192

L. Grosenick, B. Klingenberg, K. Katovich, B. Knutson, and J. E. Taylor, Interpretable whole-brain prediction analysis with GraphNet, NeuroImage, vol.72, 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, B. Thirion, and G. Varoquaux, Identifying Predictive Regions from fMRI with TV-L1 Prior, 2013 International Workshop on Pattern Recognition in Neuroimaging, p.17, 2013.
DOI : 10.1109/PRNI.2013.14

URL : https://hal.archives-ouvertes.fr/hal-00839984

J. V. Haxby, A. C. Connolly, and J. S. Guntupalli, Decoding Neural Representational Spaces Using Multivariate Pattern Analysis, Annual Review of Neuroscience, vol.37, issue.1, 2014.
DOI : 10.1146/annurev-neuro-062012-170325

T. Davis and R. A. Poldrack, Measuring neural representations with fMRI: practices and pitfalls, Annals of the New York Academy of Sciences, vol.52, issue.1, p.108, 2013.
DOI : 10.1111/nyas.12156

S. Dehaene and L. Naccache, Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework, Cognition, vol.79, issue.1-2, 2001.
DOI : 10.1016/S0010-0277(00)00123-2

B. B. Biswal, M. Mennes, X. N. Zuo, S. Gohel, C. Kelly et al., Toward discovery science of human brain function, Proceedings of the National Academy of Sciences, vol.107, issue.10, p.4734, 2010.
DOI : 10.1073/pnas.0911855107

M. Greicius, Resting-state functional connectivity in neuropsychiatric disorders, Current Opinion in Neurology, vol.24, issue.4, p.424, 2008.
DOI : 10.1097/WCO.0b013e328306f2c5

S. Sadaghiani, G. Hesselmann, K. J. Friston, and A. Kleinschmidt, The relation of ongoing brain activity, evoked neural responses, and cognition, Frontiers in Systems Neuroscience, vol.4, 2010.
DOI : 10.3389/fnsys.2010.00020

O. Sporns, G. Tononi, and R. Kotter, The Human Connectome: A Structural Description of the Human Brain, PLoS Computational Biology, vol.2, issue.4, p.42, 2005.
DOI : 1539-2791(2004)002[0019:IDAESF]2.0.CO;2

G. Varoquaux and R. C. Craddock, Learning and comparing functional connectomes across subjects, NeuroImage, vol.80, p.405, 2013.
DOI : 10.1016/j.neuroimage.2013.04.007

URL : https://hal.archives-ouvertes.fr/hal-00812911

G. Varoquaux, A. Gramfort, J. B. Poline, and B. Thirion, Markov models for fMRI correlation structure: Is brain functional connectivity small world, or decomposable into networks?, Journal of Physiology-Paris, vol.106, issue.5-6, p.212, 2012.
DOI : 10.1016/j.jphysparis.2012.01.001

URL : https://hal.archives-ouvertes.fr/hal-00665340

G. Varoquaux, A. Gramfort, J. B. Poline, and B. Thirion, Brain covariance selection: better individual functional connectivity models using population prior, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00512451

S. M. Smith, K. L. Miller, G. Salimi-khorshidi, M. Webster, C. F. Beckmann et al., Network modelling methods for FMRI, NeuroImage, vol.54, issue.2, 2011.
DOI : 10.1016/j.neuroimage.2010.08.063

B. Biswal, F. Zerrin-yetkin, V. M. Haughton, and J. S. Hyde, Functional connectivity in the motor cortex of resting human brain using echo-planar mri, Magnetic Resonance in Medicine, vol.13, issue.4, p.53719, 1995.
DOI : 10.1002/mrm.1910340409

M. E. Raichle, A. M. Macleod, A. Z. Snyder, W. J. Powers, D. A. Gusnard et al., A default mode of brain function, Proceedings of the National Academy of Sciences 98, p.676, 2001.
DOI : 10.1073/pnas.98.2.676

V. Kiviniemi, J. H. Kantola, J. Jauhiainen, A. Hyvärinen, and O. Tervonen, Independent component analysis of nondeterministic fMRI signal sources, NeuroImage, vol.19, issue.2, p.253, 2003.
DOI : 10.1016/S1053-8119(03)00097-1

C. F. Beckmann, M. Deluca, J. T. Devlin, and S. M. Smith, Investigations into resting-state connectivity using independent component analysis, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.8, issue.2-3, p.1001, 2005.
DOI : 10.1002/(SICI)1097-0193(1999)8:2/3<151::AID-HBM13>3.0.CO;2-5

V. Kiviniemi, T. Starck, J. Remes, X. Long, J. Nikkinen et al., Functional segmentation of the brain cortex using high model order group PICA, Human Brain Mapping, vol.447, issue.12, p.3865, 2009.
DOI : 10.1002/hbm.20813

B. T. Yeo, F. M. Krienen, J. Sepulcre, and M. R. Sabuncu, The organization of the human cerebral cortex estimated by intrinsic functional connectivity, J Neurophysio, vol.106, p.1125, 2011.

R. C. Craddock, G. A. James, P. E. Holtzheimer, X. P. Hu, and H. S. Mayberg, A whole brain fMRI atlas generated via spatially constrained spectral clustering, Human Brain Mapping, vol.22, issue.Pt 1, p.1914, 2012.
DOI : 10.1002/hbm.21333

G. Varoquaux, A. Gramfort, F. Pedregosa, V. Michel, and B. Thirion, Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity, Inf Proc Med Imag, p.562, 2011.
DOI : 10.1007/978-3-642-22092-0_46

URL : https://hal.archives-ouvertes.fr/inria-00588898

A. Abraham, E. Dohmatob, B. Thirion, D. Samaras, and G. Varoquaux, Extracting Brain Regions from Rest fMRI with Total-Variation Constrained Dictionary Learning, p.607, 2013.
DOI : 10.1007/978-3-642-40763-5_75

URL : https://hal.archives-ouvertes.fr/hal-00853242

B. Thirion, G. Varoquaux, E. Dohmatob, and J. Poline, Which fMRI clustering gives good brain parcellations? Name, Frontiers in Neuroscience, vol.8, p.167, 2014.

S. M. Smith, P. T. Fox, K. L. Miller, D. C. Glahn, P. M. Fox et al., Correspondence of the brain's functional architecture during activation and rest, Proceedings of the National Academy of Sciences, vol.106, issue.31, p.13040, 2009.
DOI : 10.1073/pnas.0905267106

G. Varoquaux, Y. Schwartz, P. Pinel, and B. Thirion, Cohort-Level Brain Mapping: Learning Cognitive Atoms to Single Out Specialized Regions, Information Processing in Medical Imaging, p.438, 2013.
DOI : 10.1007/978-3-642-38868-2_37

URL : https://hal.archives-ouvertes.fr/hal-00841502

G. Varoquaux, S. Sadaghiani, P. Pinel, A. Kleinschmidt, J. B. Poline et al., A group model for stable multi-subject ICA on fMRI datasets, NeuroImage, vol.51, issue.1, p.288, 2010.
DOI : 10.1016/j.neuroimage.2010.02.010

URL : https://hal.archives-ouvertes.fr/hal-00489507

S. M. Smith, M. Jenkinson, M. W. Woolrich, C. F. Beckmann, T. E. Behrens et al., Advances in functional and structural MR image analysis and implementation as FSL, NeuroImage, vol.23, pp.208-219, 2004.
DOI : 10.1016/j.neuroimage.2004.07.051

R. W. Cox, AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages, Computers and Biomedical Research, vol.29, issue.3, p.162, 1996.
DOI : 10.1006/cbmr.1996.0014

S. M. Laconte, Decoding fMRI brain states in real-time, NeuroImage, vol.56, issue.2, p.440, 2011.
DOI : 10.1016/j.neuroimage.2010.06.052

M. Hanke, Y. O. Halchenko, P. B. Sederberg, S. J. Hanson, J. V. Haxby et al., PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data, Neuroinformatics, vol.12, issue.1, p.37, 2009.
DOI : 10.1007/s12021-008-9041-y

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, p.2825, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

A. Abraham, F. Pedregosa, M. Eickenberg, P. Gervais, A. Mueller et al., Machine learning for neuroimaging with scikit-learn, Frontiers in Neuroinformatics, vol.8, 2014.
DOI : 10.3389/fninf.2014.00014

URL : https://hal.archives-ouvertes.fr/hal-01093971