A. Abraham, E. Dohmatob, B. Thirion, D. Samaras, and G. Varoquaux, Extracting Brain Regions from Rest fMRI with Total-Variation Constrained Dictionary Learning, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.607-615, 2013.
DOI : 10.1007/978-3-642-40763-5_75

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

A. Abraham, F. Pedregosa, M. Eickenberg, P. Gervais, A. Muller et al., Machine learning for neuroimaging with scikit-learn. arXiv preprint, 2014.
DOI : 10.3389/fninf.2014.00014

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

R. Achanta, A. Shaji, K. Smith, P. Lucchi, S. Fua et al., SLIC Superpixels Compared to State-of-the-Art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.11, pp.2274-2282, 2012.
DOI : 10.1109/TPAMI.2012.120

D. Achlioptas, Database-friendly random projections: Johnson-Lindenstrauss with binary coins, Journal of Computer and System Sciences, vol.66, issue.4, pp.671-687, 2003.
DOI : 10.1016/S0022-0000(03)00025-4

URL : http://doi.org/10.1016/s0022-0000(03)00025-4

T. Addair, D. Dodge, W. Walter, and S. Ruppert, Large-scale seismic signal analysis with Hadoop, Computers & Geosciences, vol.66, p.145, 2014.
DOI : 10.1016/j.cageo.2014.01.014

URL : http://doi.org/10.1016/j.cageo.2014.01.014

N. Ailon and B. Chazelle, The Fast Johnson???Lindenstrauss Transform and Approximate Nearest Neighbors, SIAM Journal on Computing, vol.39, issue.1, pp.302-322, 2009.
DOI : 10.1137/060673096

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.9581

H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.
DOI : 10.1109/TAC.1974.1100705

F. Amat, E. W. Myers, and P. J. Keller, Fast and robust optical flow for time-lapse microscopy using super-voxels, Bioinformatics, vol.29, issue.3, pp.373-80, 2013.
DOI : 10.1093/bioinformatics/bts706

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3562071

S. Arlot and A. Celisse, A survey of cross-validation procedures for model selection, Statistics Surveys, vol.4, issue.0, pp.40-79, 2010.
DOI : 10.1214/09-SS054

URL : https://hal.archives-ouvertes.fr/hal-00407906

D. Arthur and S. Vassilvitskii, -means method?, Proceedings of the twenty-second annual symposium on Computational geometry , SCG '06, p.144, 2006.
DOI : 10.1145/1137856.1137880

URL : https://hal.archives-ouvertes.fr/hal-01408862

F. Bach, Bolasso, Proceedings of the 25th international conference on Machine learning, ICML '08, 2008.
DOI : 10.1145/1390156.1390161

URL : https://hal.archives-ouvertes.fr/hal-00271289

S. Badillo, T. Vincent, and P. Ciuciu, Group-level impacts of within- and between-subject hemodynamic variability in fMRI, NeuroImage, vol.82, pp.433-448, 2013.
DOI : 10.1016/j.neuroimage.2013.05.100

URL : https://hal.archives-ouvertes.fr/hal-00854481

R. G. Baraniuk and M. B. Wakin, Random Projections of Smooth Manifolds, Foundations of Computational Mathematics, vol.26, issue.1, pp.51-77, 2009.
DOI : 10.1007/s10208-007-9011-z

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.222.928

D. M. Barch, G. C. Burgess, M. P. Harms, S. E. Petersen, B. L. Schlaggar et al., Function in the human connectome: Task-fMRI and individual differences in behavior, NeuroImage, vol.80, pp.169-189, 2013.
DOI : 10.1016/j.neuroimage.2013.05.033

R. Basri and D. Jacobs, Lambertian reflectance and linear subspaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.2, pp.218-233, 2003.
DOI : 10.1109/TPAMI.2003.1177153

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.9663

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal of the Royal Statistical Society. Series B (Methodological), vol.57, pp.289-300, 1995.

O. Bousquet and A. Elissee, Stability and generalization, Journal of Machine Learning Research, vol.2, pp.499-526, 2002.

G. M. Boynton, S. A. Engel, G. H. Glover, and D. J. Heeger, Linear systems analysis of functional magnetic resonance imaging in human V1, The Journal of Neuroscience, vol.16, pp.4207-422, 1996.

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.1007/BF00058655

P. Bühlmann and B. Yu, Analyzing bagging, The Annals of Statistics, vol.30, issue.4, pp.927-961, 2002.
DOI : 10.1214/aos/1031689014

P. Bühlmann, P. Rütimann, S. Van-de-geer, and C. Zhang, Correlated variables in regression: Clustering and sparse estimation, Journal of Statistical Planning and Inference, vol.143, issue.11, pp.1835-1871, 2013.
DOI : 10.1016/j.jspi.2013.05.019

R. B. Buxton, E. C. Wong, and L. R. Frank, Dynamics of blood ow and oxygenation changes during brain activation: The balloon model

R. B. Buxton, K. Uluda?, D. J. Dubowitz, and T. T. Liu, Modeling the hemodynamic response to brain activation, NeuroImage, vol.23, pp.220-233, 2004.
DOI : 10.1016/j.neuroimage.2004.07.013

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.329.29

D. Bzdok, M. Eickenberg, O. Grisel, B. Thirion, and G. Varoquaux, Semi-supervised factored logistic regression for high-dimensional neuroimaging data, Advances in neural information processing systems, pp.3348-3356, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01211248

A. Caramazza and M. Coltheart, twenty years on, Cognitive Neuropsychology, vol.23, issue.1, pp.3-12, 2006.
DOI : 10.1080/02643290542000049

K. Chakrabarti, E. Keogh, S. Mehrotra, and M. Pazzani, Locally adaptive dimensionality reduction for indexing large time series databases, ACM Transactions on Database Systems, vol.27, issue.2, pp.188-228, 2002.
DOI : 10.1145/568518.568520

M. S. Cohen, Parametric Analysis of fMRI Data Using Linear Systems Methods, NeuroImage, vol.6, issue.2, pp.93-103, 1997.
DOI : 10.1006/nimg.1997.0278

J. R. Cole, Q. Wang, J. A. Fish, B. Chai, D. M. Mcgarrell et al., Ribosomal Database Project: data and tools for high throughput rRNA analysis, Nucleic Acids Research, vol.42, issue.D1, 2013.
DOI : 10.1093/nar/gkt1244

URL : http://doi.org/10.1093/nar/gkt1244

R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu et al., Natural language processing (almost) from scratch, Journal of Machine Learning Research, vol.12, pp.2493-2537, 2011.

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

B. Da-mota, V. Fritsch, G. Varoquaux, T. Banaschewski, G. J. Barker et al., Randomized parcellation based inference, NeuroImage, vol.89, pp.203-215, 2014.
DOI : 10.1016/j.neuroimage.2013.11.012

URL : https://hal.archives-ouvertes.fr/hal-00915243

A. Dehman, C. Ambroise, and P. Neuvial, Performance of a blockwise approach in variable selection using linkage disequilibrium information, BMC Bioinformatics, vol.95, issue.2, 2015.
DOI : 10.1093/biomet/asn007

URL : https://hal.archives-ouvertes.fr/hal-01193074

O. Demirci, V. P. Clark, V. A. Magnotta, N. C. Andreasen, J. Lauriello et al., A Review of Challenges in the Use of fMRI for Disease Classification / Characterization and A Projection Pursuit Application from A Multi-site fMRI Schizophrenia Study, Brain Imaging and Behavior, vol.23, issue.4, pp.207-226, 2008.
DOI : 10.1007/s11682-008-9028-1

J. Dem?ar, Statistical comparisons of classiers over multiple data sets, Journal of Machine learning research, vol.7, pp.1-30, 2006.

J. E. Desmond and G. H. Glover, Estimating sample size in functional MRI (fMRI) neuroimaging studies: Statistical power analyses, Journal of Neuroscience Methods, vol.118, issue.2, pp.115-128, 2002.
DOI : 10.1016/S0165-0270(02)00121-8

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.494.5483

E. Dohmatob, M. Eickenberg, B. Thirion, and G. Varoquaux, Speedingup model-selection in GraphNet via early-stopping and univariate feature-screening, pp.17-20, 2015.
DOI : 10.1109/prni.2015.19

URL : https://hal.archives-ouvertes.fr/hal-01147731

E. D. Dohmatob, A. Gramfort, B. Thirion, and G. Varoquaux, Benchmarking solvers for TV-? 1 least-squares and logistic regression in brain imaging, IEEE PRNI, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00991743

L. Dümbgen, R. J. Samworth, and D. Schuhmacher, Stochastic search for semiparametric linear regression models. From Probability to Statistics and Back: High-Dimensional Models and Processes ? A Festschrift in Honor of Jon A. Wellner, pp.78-90, 2013.

K. Duncan, C. Pattamadilok, I. Knierim, and J. Devlin, Consistency and variability in functional localisers, NeuroImage, vol.46, issue.4, pp.1018-1026, 2009.
DOI : 10.1016/j.neuroimage.2009.03.014

URL : http://doi.org/10.1016/j.neuroimage.2009.03.014

K. J. Duncan, C. Pattamadilok, I. Knierim, and J. T. Devlin, Consistency and variability in functional localisers, NeuroImage, vol.46, issue.4, p.1018, 2009.
DOI : 10.1016/j.neuroimage.2009.03.014

URL : http://doi.org/10.1016/j.neuroimage.2009.03.014

B. Efron and R. Tibshirani, Improvements on cross-validation: the .632+ bootstrap method, J. Amer. Statist. Assoc, vol.92, pp.548-560, 1997.

M. Eickenberg, E. Dohmatob, B. Thirion, and G. Varoquaux, Total variation meets sparsity: statistical learning with segmenting penalties, International Conference on Medical Image Computing and Computer- Assisted Intervention, 2015.
DOI : 10.1007/978-3-319-24553-9_84

URL : https://hal.archives-ouvertes.fr/hal-01170619

T. E. Nichols and A. P. Holmes, Nonparametric permutation tests for functional neuroimaging: A primer with examples, Human Brain Mapping, vol.4, issue.1, pp.1-25, 2001.
DOI : 10.1002/hbm.1058

D. Eppstein, M. S. Paterson, and F. Yao, On Nearest-Neighbor Graphs, Discrete & Computational Geometry, vol.4, issue.3, pp.263-282, 1997.
DOI : 10.1007/PL00009293

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.3206

D. V. Essen, K. Ugurbil, E. Auerbach, D. Barch, T. Behrens et al., The Human Connectome Project: A data acquisition perspective, NeuroImage, vol.62, issue.4, pp.2222-2231, 2012.
DOI : 10.1016/j.neuroimage.2012.02.018

M. Ester, H. Peter-kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, ensembles of models in fmri: stable learning in large-scale settings 113, pp.226-231, 1996.

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, pp.1731-1743, 2008.
DOI : 10.1016/j.neuroimage.2007.10.031

P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, vol.59, issue.2, pp.167-181, 2004.
DOI : 10.1023/B:VISI.0000022288.19776.77

M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. C. Essen et al., From The Cover: The human brain is intrinsically organized into dynamic, anticorrelated functional networks, Proceedings of the National Academy of Sciences, pp.9673-9678, 2005.
DOI : 10.1126/science.273.5283.1868

K. J. Friston, A. P. Holmes, K. J. Worsley, J. Poline, C. D. Frith et al., Statistical parametric maps in functional imaging: A general linear approach, Human Brain Mapping, vol.26, issue.4, pp.189-210, 1993.
DOI : 10.1002/hbm.460020402

K. J. Friston, A. P. Holmes, K. J. Worsley, J. Poline, C. D. Frith et al., Statistical parametric maps in functional imaging: A general linear approach, Human Brain Mapping, vol.26, issue.4, pp.189-210, 1994.
DOI : 10.1002/hbm.460020402

B. Gaonkar and C. Davatzikos, Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification, NeuroImage, vol.78, pp.270-283, 2013.
DOI : 10.1016/j.neuroimage.2013.03.066

C. R. Genovese, N. A. Lazar, and T. Nichols, Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate, NeuroImage, vol.15, issue.4, pp.870-878, 2002.
DOI : 10.1006/nimg.2001.1037

A. Gittens and M. W. Mahoney, Revisiting the nystrom method for improved large-scale machine learning, International Conference on Machine Learning, pp.567-575, 2013.

M. F. Glasser, T. S. Coalson, E. C. Robinson, C. D. Hacker, J. Harwell et al., A multi-modal parcellation of human cerebral cortex, Nature, vol.17, issue.7615, pp.171-178, 2016.
DOI : 10.1038/nature18933

G. H. Glover, Deconvolution of Impulse Response in Event-Related BOLD fMRI1, NeuroImage, vol.9, issue.4, pp.416-429, 1999.
DOI : 10.1006/nimg.1998.0419

K. J. Gorgolewski, G. Varoquaux, G. Rivera, Y. Schwartz, S. S. Ghosh et al., org: A web-based repository for collecting and sharing unthresholded statistical maps of the human brain, Margulies. Neurovault. Front. Neuroinform, 2015.
URL : https://hal.archives-ouvertes.fr/inserm-01134573

K. Grill-spector and R. Malach, THE HUMAN VISUAL CORTEX, Annual Review of Neuroscience, vol.27, issue.1, pp.649-677, 2004.
DOI : 10.1146/annurev.neuro.27.070203.144220

URL : https://hal.archives-ouvertes.fr/hal-01418475

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

URL : http://doi.org/10.1016/j.neuroimage.2012.12.062

D. A. Handwerker, J. M. Ollinger, and M. D. Esposito, Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses, NeuroImage, vol.21, issue.4, pp.1639-1651, 2004.
DOI : 10.1016/j.neuroimage.2003.11.029

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, pp.37-53, 2009.
DOI : 10.1007/s12021-008-9041-y

T. Hastie, R. Tibshirani, M. B. Eisen, A. Alizadeh, R. Levy et al., 'gene shaving' as a method for identifying distinct sets of genes with similar expression patterns

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2009.

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

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, pp.2425-2430, 2001.
DOI : 10.1126/science.1063736

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

C. Hedge, A. C. Sankaranarayanan, W. Yin, and R. G. Baraniuk, NuMax: a convex approach for learning near-isometric linear embeddings, IEEE Transactions on Signal Processing, vol.63, pp.6109-6121, 2015.

]. R. Henson, Forward inference using functional neuroimaging: dissociations versus associations, Trends in Cognitive Sciences, vol.10, issue.2, pp.64-69, 2006.
DOI : 10.1016/j.tics.2005.12.005

R. Henson, T. Shallice, M. Gorno-tempini, and R. Dolan, Face Repetition Effects in Implicit and Explicit Memory Tests as Measured by fMRI, fmri: stable learning in large-scale settings 115, p.178, 2002.
DOI : 10.1093/cercor/12.2.178

URL : http://cercor.oxfordjournals.org/cgi/content/short/12/2/178

F. G. Hillary and J. Deluca, functional neuroimaging in clinical populations, 2007.

A. Hoyos-idrobo, Y. Schwartz, G. Varoquaux, and B. Thirion, Improving Sparse Recovery on Structured Images with Bagged Clustering, 2015 International Workshop on Pattern Recognition in NeuroImaging, 2015.
DOI : 10.1109/PRNI.2015.30

URL : https://hal.archives-ouvertes.fr/hal-01174335

A. Hoyos-idrobo, G. Varoquaux, J. Kahn, and B. Thirion, Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signals, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01366651

L. Jacob, P. Neuvial, and S. Dudoit, More power via graph-structured tests for differential expression of gene networks, The Annals of Applied Statistics, vol.6, issue.2, pp.561-600, 2012.
DOI : 10.1214/11-AOAS528SUPPC

URL : https://hal.archives-ouvertes.fr/hal-00521097

H. Jégou, M. Douze, and C. Schmid, Improving Bag-of-Features for Large Scale Image Search, International Journal of Computer Vision, vol.42, issue.3, pp.316-336, 2010.
DOI : 10.1007/s11263-009-0285-2

W. Johnson and J. Lindenstrauss, Extensions of Lipschitz mappings into a Hilbert space, Conference in modern analysis and probability, pp.189-206, 1984.
DOI : 10.1090/conm/026/737400

H. G. Katzgraber, Random numbers in scientic computing: An introduction. CoRR, abs/1005, 2010.

E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra, Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases, Knowledge and Information Systems, vol.3, issue.3, p.263, 2001.
DOI : 10.1007/PL00011669

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.4993

A. Knops, B. Thirion, E. M. Hubbard, V. Michel, and S. Dehaene, Recruitment of an Area Involved in Eye Movements During Mental Arithmetic, Science, vol.42, issue.5, p.1583, 2009.
DOI : 10.1016/S0010-9452(08)70416-7

URL : https://hal.archives-ouvertes.fr/hal-00504101

L. I. Kuncheva and J. J. Rodríguez, Classifier ensembles for fMRI data analysis: an experiment, Magnetic Resonance Imaging, vol.28, issue.4, pp.583-593, 2010.
DOI : 10.1016/j.mri.2009.12.021

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.357

L. I. Kuncheva, J. J. Rodriguez, C. O. Plumpton, D. E. Linden, and S. J. Johnston, Random Subspace Ensembles for fMRI Classification, IEEE Transactions on Medical Imaging, vol.29, issue.2, pp.531-542, 2010.
DOI : 10.1109/TMI.2009.2037756

L. and L. Folgoc, Apprentissage statistique pour la personnalisation de modèles cardiaques à partir de données d'imagerie

L. , L. Magoarou, and R. Gribonval, Flexible multi-layer sparse approximations of matrices and applications. arXiv preprint, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01167948

S. Lemm, B. Blankertz, T. Dickhaus, and K. Müller, Introduction to machine learning for brain imaging, NeuroImage, vol.56, issue.2, pp.387-399, 2011.
DOI : 10.1016/j.neuroimage.2010.11.004

G. Leung and A. R. Barron, Information theory and mixing leastsquares regressions, IEEE Transactions on information theory, vol.52, 2006.
DOI : 10.1109/tit.2006.878172

E. Liberty, Simple and deterministic matrix sketching, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '13, pp.581-588, 2013.
DOI : 10.1145/2487575.2487623

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.348.6826

C. Lim and B. Yu, Estimation Stability With Cross-Validation (ESCV), Journal of Computational and Graphical Statistics, vol.8, issue.2
DOI : 10.1214/009053605000000255

URL : http://arxiv.org/abs/1303.3128

J. Lin, E. Keogh, L. Wei, and S. Lonardi, Experiencing SAX: a novel symbolic representation of time series, Data Mining and Knowledge Discovery, vol.5, issue.2, pp.107-144, 2007.
DOI : 10.1007/s10618-007-0064-z

M. A. Lindquist, The Statistical Analysis of fMRI Data, Statistical Science, vol.23, issue.4, pp.439-464, 2008.
DOI : 10.1214/09-STS282

N. K. Logothetis, J. Pauls, M. Augath, T. Trinath, and A. Oeltermann, Neurophysiological investigation of the basis of the fMRI signal, Nature, vol.412, issue.6843, pp.150-157, 2001.
DOI : 10.1038/35084005

H. Lombaert, A. Criminisi, and N. Ayache, Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation, MICCAI, pp.547-555, 2015.
DOI : 10.1007/978-3-319-19992-4_37

URL : https://hal.archives-ouvertes.fr/hal-01203568

M. E. Lopes, L. J. Jacob, and M. J. Wainwright, A more powerful twosample test in high dimensions using random projection, Advances in Neural Information Processing Systems, 2011.

Y. Lu, P. S. Dhillon, D. P. Foster, and L. H. Ungar, Faster ridge regression via the subsampled randomized hadamard transform, Advances in neural information processing systems, pp.369-377, 2013.

A. Lucchi, K. Smith, G. Achanta, P. Knott, and . Fua, Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features, IEEE Transactions on Medical Imaging, vol.31, issue.2, pp.474-486, 2012.
DOI : 10.1109/TMI.2011.2171705

M. W. Mahoney, Randomized algorithms for matrices and data. Found, Trends Mach. Learn, vol.3, pp.123-224, 2011.

M. W. Mahoney and P. Drineas, CUR matrix decompositions for improved data analysis, Proceedings of the National Academy of Sciences, pp.697-702, 2009.
DOI : 10.1073/pnas.0500191102

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2630100

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, ensembles of models in fmri: stable learning in large-scale settings 117, p.1498, 2007.
DOI : 10.1109/42.906424

J. Margeta, A. Criminisi, R. C. Lozoya, D. Lee, and N. Ayache, Finetuned convolutional neural nets for cardiac MRI acquisition plane recognition, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp.1-11, 2015.
DOI : 10.1080/21681163.2015.1061448

URL : https://hal.archives-ouvertes.fr/hal-01162880

A. Mechelli, C. J. Price, K. J. Friston, and J. Ashburner, Voxel-Based Morphometry of the Human Brain: Methods and Applications, Current Medical Imaging Reviews, vol.1, issue.2, 2005.
DOI : 10.2174/1573405054038726

N. Meinshausen and P. Bühlmann, Stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.7, issue.4, pp.417-473, 2010.
DOI : 10.1111/j.1467-9868.2010.00740.x

A. Mensch, J. Marial, B. Thirion, and G. Varoquaux, Dictionary learning for massive matrix factorization, International conference on machine learning, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01308934

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, pp.1328-1340, 2011.
DOI : 10.1109/TMI.2011.2113378

M. Misaki, Y. Kim, P. A. Bandettini, and N. Kriegeskorte, Comparison of multivariate classifiers and response normalizations for pattern-information fMRI, NeuroImage, vol.53, issue.1, pp.103-118, 2010.
DOI : 10.1016/j.neuroimage.2010.05.051

T. M. Mitchell, R. Hutchinson, R. S. Niculescu, F. Pereira, X. Wang et al., Learning to Decode Cognitive States from Brain Images, Machine Learning, p.145, 2004.
DOI : 10.1023/B:MACH.0000035475.85309.1b

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

H. Mohr, U. Wolfensteller, S. Frimmel, and H. Ruge, Sparse regularization techniques provide novel insights into outcome integration processes, NeuroImage, vol.104, 2015.
DOI : 10.1016/j.neuroimage.2014.10.025

URL : http://doi.org/10.1016/j.neuroimage.2014.10.025

J. M. Moran, E. Jolly, and J. P. Mitchell, Social-Cognitive Deficits in Normal Aging, Journal of Neuroscience, vol.32, issue.16, p.5553, 2012.
DOI : 10.1523/JNEUROSCI.5511-11.2012

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3341664

J. Mourão-miranda, A. L. Bokde, C. Born, H. Hampel, and M. Stetter, Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data, NeuroImage, vol.28, issue.4, pp.980-995, 2005.
DOI : 10.1016/j.neuroimage.2005.06.070

J. Mourão-miranda, A. L. Bokde, C. Born, H. Hampel, and M. Stetter, Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data, NeuroImage, vol.28, issue.4, 2005.
DOI : 10.1016/j.neuroimage.2005.06.070

D. Müllner, Modern hierarchical, agglomerative clustering algorithms. ArXiv e-prints, 2011.

T. Naselaris, K. N. Kay, S. Nishimoto, and J. L. Gallant, Encoding and decoding in fMRI, NeuroImage, vol.56, issue.2, pp.400-410, 2011.
DOI : 10.1016/j.neuroimage.2010.07.073

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3037423

A. Nemirovski, Topics in non-parametric statistics. Lectures on Probability Theory and Statistics: Ecole d'Ete de Probabilites de Saint- Flour XXVIII-1998, p.85, 2000.

T. Nichols and S. Hayasa, Controlling the familywise error rate in functional neuroimaging: a comparative review, Statistical Methods in Medical Research, vol.12, issue.5, pp.419-446, 2003.
DOI : 10.1191/0962280203sm341ra

T. E. Nichols, S. Das, S. B. Eickho, A. C. Evans, T. Glatard et al., Best practices in data analysis and sharing in neuroimaging using MRI, BioRxiv, p.54262, 2016.

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

S. E. O-'bryant, G. Xiao, R. Barber, J. Reisch, R. Doody et al., A serum protein-based algorithm for the detection of Alzheimer's disease, Archives of neurology, vol.67, 2010.

S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation., Proceedings of the National Academy of Sciences, pp.9868-9872, 1990.
DOI : 10.1073/pnas.87.24.9868

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC55275

S. Ogawa, D. Tank, R. Menon, J. Ellermann, S. Kim et al., Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging., Proceedings of the National Academy of Sciences, pp.5951-5955, 1992.
DOI : 10.1073/pnas.89.13.5951

A. J. O-'toole, F. Jiang, H. Abdi, N. Pénard, J. P. Dunlop et al., Theoretical, statistical, and practical perspectives on pattern-based classication approaches to the analysis of functional neuroimaging data, J Cogn Neurosci, vol.19, 2007.

R. Overbeek, N. Larsen, G. D. Pusch, M. D. Souza, E. S. Jr et al., WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction, Nucleic Acids Research, vol.28, issue.1, pp.123-125, 2000.
DOI : 10.1093/nar/28.1.123

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC102471

D. J. Pearce, An improved algorithm for nding the strongly connected components of a directed graph, 2005.

F. Pedregosa, Feature extraction and supervised learning on fMRI : from practice to theory, ensembles of models in fmri: stable learning in large-scale settings 119, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01100921

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

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

M. Penrose, Single Linkage Clustering and Continuum Percolation, Journal of Multivariate Analysis, vol.53, issue.1, pp.94-109, 1995.
DOI : 10.1006/jmva.1995.1026

URL : http://doi.org/10.1006/jmva.1995.1026

F. Pereira, T. Mitchell, and M. Botvinick, Machine learning classifiers and fMRI: A tutorial overview, NeuroImage, vol.45, issue.1, pp.199-209, 2009.
DOI : 10.1016/j.neuroimage.2008.11.007

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2892746

R. Poldrack, Can cognitive processes be inferred from neuroimaging data? Trends in cognitive sciences, pp.59-63, 2006.

R. A. Poldrack, Inferring Mental States from Neuroimaging Data: From Reverse Inference to Large-Scale Decoding, Neuron, vol.72, issue.5, pp.692-697, 2011.
DOI : 10.1016/j.neuron.2011.11.001

URL : http://doi.org/10.1016/j.neuron.2011.11.001

R. A. Poldrack and K. J. Gorgolewski, Making big data open: data sharing in neuroimaging, Nature Neuroscience, vol.6, issue.11, pp.1510-1517, 2014.
DOI : 10.1016/j.neuroimage.2008.04.186

R. A. Poldrack and K. J. Gorgolewski, OpenfMRI: Open sharing of task fMRI data, NeuroImage, vol.144, 2015.
DOI : 10.1016/j.neuroimage.2015.05.073

R. A. Poldrack and T. Yarkoni, From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure, Annual Review of Psychology, vol.67, issue.1
DOI : 10.1146/annurev-psych-122414-033729

R. A. Poldrack, J. A. Mumford, and T. E. Nichols, Handbook of functional MRI data analysis, 2011.
DOI : 10.1017/CBO9780511895029

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

J. Praestgaard and J. A. Wellner, Exchangeably weighted bootstraps of the general empirical process. The Annals of Probability, pp.2053-2086, 1993.

M. Rahim, B. Thirion, A. Abraham, M. Eickenberg, E. Dohmatob et al., Integrating Multimodal Priors in Predictive Models for the Functional Characterization of Alzheimer???s Disease, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.207-214, 2015.
DOI : 10.1016/j.neuroimage.2011.01.008

A. Rahimi and B. Recht, Random features for large-scale kernel machines, Advances in neural information processing systems, 2007.

A. Rinaldo, S. Bacanu, B. Devlin, V. Sonpar, L. Wasserman et al., Characterization of multilocus linkage disequilibrium, Genetic Epidemiology, vol.14, issue.3, p.193, 2005.
DOI : 10.1002/gepi.20056

A. Rudi, R. Camoriano, and L. Rosasco, Less is more: Nyström computational regularization, Advances in neural information processing systems, pp.1648-1656, 2015.

J. C. Russ, The image processing handbook, 2007.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, vol.1010, issue.1, pp.211-252, 2015.
DOI : 10.1007/s11263-015-0816-y

URL : http://arxiv.org/abs/1409.0575

J. Schrou, C. Kussé, L. Wehenkel, P. Maquet, and C. Phillips, Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes, PLoS ONE, vol.15, issue.3, p.35860, 2012.
DOI : 10.1371/journal.pone.0035860.s001

Y. Schwartz, B. Thirion, and G. Varoquaux, Mapping cognitive ontologies to and from the brain, Advances in neural information processing systems, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00904763

G. E. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

R. D. Shah and R. J. Samworth, Variable selection with error control: another look at stability selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.97, issue.1, pp.55-80, 2013.
DOI : 10.1111/j.1467-9868.2011.01034.x

URL : http://arxiv.org/abs/1105.5578

S. Shalev-shwartz and S. Ben-david, Understanding machine learning: From theory to algorithms, 2014.
DOI : 10.1017/CBO9781107298019

R. Shibata, Bootstrap estimate of kullback-leibler information for model selection, Statistica Sinica, vol.7, pp.375-394, 1997.

D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, The emerging eld of signal processing on graphs, IEEE Signal processing magazine, vol.83, 2013.

D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, ensembles of models in fmri: stable learning in large-scale settings 121, pp.83-98, 2013.
DOI : 10.1109/MSP.2012.2235192

S. M. Smith, C. F. Beckmann, J. Andersson, E. J. Auerbach, J. Bijsterbosch et al., Resting-state fMRI in the Human Connectome Project, NeuroImage, vol.80, pp.144-168, 2013.
DOI : 10.1016/j.neuroimage.2013.05.039

D. Stauer and A. Aharony, Introduction to Percolation Theory, 1971.

S. Teng and F. F. Yao, k-Nearest-Neighbor Clustering and Percolation Theory, Algorithmica, vol.14, issue.2, pp.192-211, 2007.
DOI : 10.1007/s00453-007-9040-7

B. Thirion, G. Varoquaux, E. Dohmatob, and J. Poline, Which fMRI clustering gives good brain parcellations?, Frontiers in Neuroscience, vol.106, issue.2, 2014.
DOI : 10.1152/jn.00338.2011

URL : https://hal.archives-ouvertes.fr/hal-01015172

R. Tibshirani, Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, 1994.
DOI : 10.1111/j.1467-9868.2011.00771.x

A. Tikhonov, On the stability of inverse problems, 1943.

G. Tononi, O. Sporns, and G. M. Edelma, A measure for brain complexity: relating functional segregation and integration in the nervous system., Proceedings of the National Academy of Sciences, pp.5033-5037, 1994.
DOI : 10.1073/pnas.91.11.5033

J. A. Tropp, IMPROVED ANALYSIS OF THE SUBSAMPLED RANDOMIZED HADAMARD TRANSFORM, Advances in Adaptive Data Analysis, vol.54, issue.01n02, 2011.
DOI : 10.1016/j.acha.2007.12.002

S. Van-der-walt, J. L. Schönberger, J. Nunez-iglesias, F. Boulogne, J. D. Warner et al., scikit-image: image processing in Python, PeerJ, vol.13, issue.2, p.453, 2014.
DOI : 10.7717/peerj.453/fig-5

URL : https://hal.archives-ouvertes.fr/hal-01071542

V. N. Vapnik, The Nature of Statistical Learning Theory. Information Science and Statistics, 2000.

V. N. Vapnik and A. Y. Chervonenkis, On the uniform convergence of relative frequencies of events to their probabilities. Theory of probability and its applications, pp.264-280, 1971.

G. Varoquaux and B. Thirion, How machine learning is shaping cognitive neuroimaging, GigaScience, vol.3, issue.1, 2014.
DOI : 10.1186/2047-217X-3-28

URL : https://hal.archives-ouvertes.fr/hal-01094737

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

G. Varoquaux, P. R. Raamana, D. A. Engemann, A. Hoyos-idrobo, Y. Schwartz et al., Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. arxiv preprint arxiv, pp.1606-05201, 2016.
DOI : 10.1016/j.neuroimage.2016.10.038

URL : https://hal.archives-ouvertes.fr/hal-01332785

T. Wager, M. Davidson, B. Hughes, M. Lindquist, and K. Ochsner, Prefrontal-Subcortical Pathways Mediating Successful Emotion Regulation, Neuron, vol.59, issue.6, pp.1037-1050, 2008.
DOI : 10.1016/j.neuron.2008.09.006

URL : http://doi.org/10.1016/j.neuron.2008.09.006

T. D. Wager, M. Lindquist, and L. Kaplan, Meta-analysis of functional neuroimaging data: current and future directions, Social cognitive and aective neuroscience, pp.150-158, 2007.
DOI : 10.1093/scan/nsm015

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, pp.1388-1397, 2013.
DOI : 10.1056/NEJMoa1204471

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3691100

H. Wang and P. A. Yushkevich, Multi-atlas Segmentation without Registration: A Supervoxel-Based Approach, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.535-542, 2013.
DOI : 10.1007/978-3-642-40760-4_67

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3918684

Z. Wang, A. Childress, J. Wang, and J. Detre, Support vector machine learning-based fMRI data group analysis, NeuroImage, vol.36, issue.4, pp.1139-1151, 2007.
DOI : 10.1016/j.neuroimage.2007.03.072

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2717002

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

S. Weichwald, T. Meyer, O. Özdenizci, B. Schölkopf, T. Ball et al., Causal interpretation rules for encoding and decoding models in neuroimaging, NeuroImage, vol.110, pp.48-59, 2015.
DOI : 10.1016/j.neuroimage.2015.01.036

URL : http://arxiv.org/abs/1511.04780

C. Williams and M. Seeger, Using the nyström method to speed up kernel machines, Advances in neural information processing systems, pp.682-688, 2001.

D. Williams, J. Detre, J. Leigh, and A. Koretsky, Magnetic resonance imaging of perfusion using spin inversion of arterial water., Proceedings of the National Academy of Sciences, pp.212-216, 1992.
DOI : 10.1073/pnas.89.1.212

K. Worsley, A. Evans, S. Marrett, and P. Neelin, A Three-Dimensional Statistical Analysis for CBF Activation Studies in Human Brain, Journal of Cerebral Blood Flow & Metabolism, vol.251, issue.6, pp.900-918, 1992.
DOI : 10.1038/jcbfm.1992.127

J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma, Robust Face Recognition via Sparse Representation, ensembles of models in fmri: stable learning in large-scale settings 123, pp.210-227, 2009.
DOI : 10.1109/TPAMI.2008.79

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.152.4658

Z. J. Xiang, H. Xu, and P. J. Ramadge, Learning sparse representations of high dimensional data on large scale dictionaries, Advances in neural information processing systems, pp.900-908, 2011.

B. Yu, Stability, Bernoulli, vol.19, issue.4, pp.1484-1500, 2013.
DOI : 10.3150/13-BEJSP14

URL : https://hal.archives-ouvertes.fr/hal-00851253

Z. Zhou, Ensemble Methods: Foundations and Algorithms, 2012.