J. M. Harlow, Passage of an Iron Rod Through the Head, The Journal of Neuropsychiatry and Clinical Neurosciences, vol.11, issue.2, pp.389-393
DOI : 10.1176/jnp.11.2.281

K. Brodmann, Vergleichende Lokalisationslehre der Großhirnrinde, 1909.

W. Penfield and P. Perot, THE BRAIN???S RECORD OF AUDITORY AND VISUAL EXPERIENCE, Brain, vol.86, issue.4, pp.595-696, 1963.
DOI : 10.1093/brain/86.4.595

W. G. Clark, J. Del-giudice, and G. K. Aghajanian, Principles of psychopharmacology: a textbook for physicians, medical students, and behavioral scientists, 1970.

M. M. Mesulam, Tetramethyl benzidine for horseradish peroxidase neurohistochemistry: a non-carcinogenic blue reaction product with superior sensitivity for visualizing neural afferents and efferents., Journal of Histochemistry & Cytochemistry, vol.26, issue.2, pp.106-123, 1978.
DOI : 10.1177/26.2.24068

C. Köbbert, Current concepts in neuroanatomical tracing, Progress in Neurobiology, vol.62, issue.4, pp.327-351, 2000.
DOI : 10.1016/S0301-0082(00)00019-8

M. Mesulam, The evolving landscape of human cortical connectivity: Facts and inferences, NeuroImage, vol.62, issue.4, pp.2182-2191, 2012.
DOI : 10.1016/j.neuroimage.2011.12.033

R. Frackowiak and H. Markram, The future of human cerebral cartography: a novel approach, Philosophical Transactions of the Royal Society of London B: Biological Sciences, p.20140171, 1668.
DOI : 10.1126/science.11642769

E. R. Kandel, Neuroscience thinks big (and collaboratively), Nature Reviews Neuroscience, vol.14, issue.9, pp.659-664, 2013.
DOI : 10.1126/science.1236939

K. Amunts, BigBrain: An Ultrahigh-Resolution 3D Human Brain Model, Science, vol.340, issue.6139, pp.1472-1477, 2013.
DOI : 10.1126/science.1235381

D. C. Van-essen, The Human Connectome Project: A data acquisition perspective, NeuroImage, vol.62, issue.4, pp.2222-2253, 2012.
DOI : 10.1016/j.neuroimage.2012.02.018

B. Efron, T. Hastie-poldrack, R. A. , and K. J. Gorgolewski, Computer-Age Statistical Inference. 2016. 13 Making big data open: data sharing in neuroimaging, Nat Neurosci, issue.11, pp.17-1510, 2014.

S. A. House-of-common, The big data dilemma, UK: Committee on Applied and Theoretical Statistics, 2016.

M. I. Jordan, Frontiers in Massive Data Analysis, 2013.

J. Manyika, Big data: The next frontier for innovation, competition, and productivity, 2011.

O. Randlett, Whole-brain activity mapping onto a zebrafish brain atlas, Nature Methods, vol.126, issue.11, pp.1039-1046, 2015.
DOI : 10.1002/hbm.1024

J. W. Lichtman, H. Pfister, and N. Shavit, The big data challenges of connectomics, Nature Neuroscience, vol.27, issue.11, pp.1448-1454, 2014.
DOI : 10.1016/j.conb.2008.08.010

Z. Ghahramani, Probabilistic machine learning and artificial intelligence, Nature, vol.80, issue.7553, pp.521-452, 2015.
DOI : 10.1109/5.18626

C. E. Rasmussen, Gaussian Processes in Machine Learning, 2006.
DOI : 10.1162/089976602317250933

URL : http://hdl.handle.net/11858/00-001M-0000-0013-F365-A

K. Sharp, Explaining Missing Heritability Using Gaussian Process Regression. bioRxiv, p.40576, 2016.
DOI : 10.1101/040576

T. Hofmann, B. Schölkopf, and A. J. Smola, Kernel methods in machine learning. The annals of statistics, pp.1171-1220, 2008.

F. Jäkel, B. Schölkopf, and F. A. Wichmann, Does cognitive science need kernels? Trends in cognitive sciences, pp.381-388, 2009.

A. Halevy, P. Norvig, and F. Pereira, The Unreasonable Effectiveness of Data, IEEE Intelligent Systems, vol.24, issue.2, pp.8-12, 2009.
DOI : 10.1109/MIS.2009.36

C. Eliasmith, A Large-Scale Model of the Functioning Brain, Science, vol.338, issue.6111, pp.1202-1205, 2012.
DOI : 10.1126/science.1225266

M. I. Jordan and T. M. Mitchell, Machine learning: Trends, perspectives, and prospects, Science, vol.349, issue.6245, pp.255-260, 2015.
DOI : 10.1126/science.aaa8415

G. L. Walton and W. E. Paul, CONTRIBUTION TO THE STUDY OF THE CORTICAL SENSORY AREAS, Brain, vol.24, issue.3, pp.430-452, 1901.
DOI : 10.1093/brain/24.3.430

B. T. Yeo, Functional Specialization and Flexibility in Human Association Cortex, Cerebral Cortex, vol.25, issue.10, pp.3654-72, 2015.
DOI : 10.1093/cercor/bhu217

M. A. Bertolero, B. T. Yeo, and M. D. Esposito, The modular and integrative functional architecture of the human brain, Proceedings of the National Academy of Sciences, vol.112, issue.49, pp.112-6798
DOI : 10.1073/pnas.1510619112

K. H. Brodersen, Generative Embedding for Model-Based Classification of fMRI Data, PLoS Computational Biology, vol.49, issue.6, p.1002079, 2011.
DOI : 10.1371/journal.pcbi.1002079.s004

P. Claes, Modeling 3D Facial Shape from DNA, PLoS Genetics, vol.46, issue.3, pp.1004224-1004257, 2014.
DOI : 10.1371/journal.pgen.1004224.s049

U. Güçlü and M. A. Van-gerven, Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream, Journal of Neuroscience, vol.35, issue.27, pp.35-10005, 2015.
DOI : 10.1523/JNEUROSCI.5023-14.2015

B. Thirion, Inverse retinotopy: Inferring the visual content of images from brain activation patterns, NeuroImage, vol.33, issue.4, pp.1104-1120, 2006.
DOI : 10.1016/j.neuroimage.2006.06.062

URL : https://hal.archives-ouvertes.fr/hal-00349668

S. Nishimoto, Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies, Current Biology, vol.21, issue.19, pp.1641-1647, 2011.
DOI : 10.1016/j.cub.2011.08.031

A. G. Huth, Natural speech reveals the semantic maps that tile human cerebral cortex, Nature, vol.9, issue.7600, pp.453-458, 2016.
DOI : 10.1038/nature17637

J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, vol.61, pp.85-117, 2015.
DOI : 10.1016/j.neunet.2014.09.003

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.9, issue.7553, pp.521-436, 2015.
DOI : 10.1007/s10994-013-5335-x

P. Baldi and K. Hornik, Neural networks and principal component analysis: Learning from examples without local minima, Neural Networks, vol.2, issue.1, pp.53-58, 1989.
DOI : 10.1016/0893-6080(89)90014-2

Q. V. Le, ICA with reconstruction cost for efficient overcomplete feature learning, Advances in Neural Information Processing Systems, 2011.

D. Bzdok, Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data, Advances in Neural Information Processing Systems, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01211248

K. E. Stephan, Translational Perspectives for Computational Neuroimaging, Neuron, vol.87, issue.4, pp.716-732, 2015.
DOI : 10.1016/j.neuron.2015.07.008

S. E. Hyman, Can neuroscience be integrated into the DSM-V?, Nature Reviews Neuroscience, vol.148, issue.9, pp.725-757, 2007.
DOI : 10.1016/j.neuron.2005.09.025

B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum, Human-level concept learning through probabilistic program induction, Science, vol.350, issue.6266, pp.350-1332, 2015.
DOI : 10.1126/science.aab3050

D. Bzdok, Formal Models of the Network Co-occurrence Underlying Mental Operations, PLOS Computational Biology, vol.27, issue.8
DOI : 10.1371/journal.pcbi.1004994.s012

URL : https://hal.archives-ouvertes.fr/hal-01338307

G. J. Yanga, Altered global brain signal in schizophrenia, Proceedings of the National Academy of Sciences, vol.111, issue.20, pp.7438-7443
DOI : 10.1073/pnas.1405289111

B. Efron, Modern science and the Bayesian-frequentist controversy, 2005.

D. Freedman, Some issues in the foundation of statistics, Foundations of Science, vol.90, issue.no. 4, pp.19-39, 1995.
DOI : 10.1007/BF00208723

K. J. Friston, Classical and Bayesian Inference in Neuroimaging: Applications, NeuroImage, vol.16, issue.2, pp.484-512, 2002.
DOI : 10.1006/nimg.2002.1091

K. E. Stephan, Bayesian model selection for group studies, NeuroImage, vol.46, issue.4, pp.1004-1021, 2009.
DOI : 10.1016/j.neuroimage.2009.03.025

W. Penny, S. Kiebel, and K. Friston, Variational Bayesian inference for fMRI time series, NeuroImage, vol.19, issue.3, pp.727-768, 2003.
DOI : 10.1016/S1053-8119(03)00071-5

K. H. Brodersen, Variational Bayesian mixed-effects inference for classification studies, NeuroImage, vol.76, issue.76, pp.345-61, 2013.
DOI : 10.1016/j.neuroimage.2013.03.008

W. J. Ma, Bayesian inference with probabilistic population codes, Nature Neuroscience, vol.9, issue.11, pp.1432-1438, 2006.
DOI : 10.1038/nn1691

M. Goodkind, Identification of a Common Neurobiological Substrate for Mental Illness, JAMA Psychiatry, vol.72, issue.4, 2015.
DOI : 10.1001/jamapsychiatry.2014.2206

P. Orbanz and Y. W. Teh, Bayesian nonparametric models, in Encyclopedia of Machine Learning, pp.81-89, 2011.

L. F. Barrett, The Future of Psychology: Connecting Mind to Brain, Perspectives on Psychological Science, vol.316, issue.4, pp.326-365, 2009.
DOI : 10.1037/0003-066X.56.9.717

J. B. Tenenbaum, How to Grow a Mind: Statistics, Structure, and Abstraction, Science, vol.331, issue.6022, pp.331-1279, 2011.
DOI : 10.1126/science.1192788

K. E. Stephan, Charting the landscape of priority problems in psychiatry, part 2: pathogenesis and aetiology. The Lancet Psychiatry, 2015.

R. A. Poldrack, Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping, PLoS Computational Biology, vol.8, issue.10, p.1002707, 2012.
DOI : 10.1371/journal.pcbi.1002707.s002

M. A. Bertolero, B. T. Yeo, and M. D. Esposito, The modular and integrative functional architecture of the human brain, Proceedings of the National Academy of Sciences, vol.112, issue.49
DOI : 10.1073/pnas.1510619112

X. Zhang, Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer's disease

C. Kemp, Learning systems of concepts with an infinite relational model. in Aaai, 2006.

Z. Ghahramani and T. L. Griffiths, Infinite latent feature models and the Indian buffet process. in Advances in neural information processing systems, 2005.

Y. W. Teh, Sharing Clusters Among Related Groups: Hierarchical Dirichlet Processes Advances in neural information processing systems, 2005.

K. P. Murphy, Machine learning: a probabilistic perspective, 2012.

P. K. Gopalan and D. M. Blei, Efficient discovery of overlapping communities in massive networks, Proceedings of the National Academy of Sciences, vol.110, issue.36, pp.110-14534
DOI : 10.1073/pnas.1221839110

B. Efron, Controversies in the Foundations of Statistics, The American Mathematical Monthly, vol.85, issue.4, pp.231-246, 1978.
DOI : 10.2307/2321163

D. Bzdok, Classical Statistics and Statistical Learning in Imaging Neuroscience. arXiv preprint, 2016.

T. Yarkoni and J. Westfall, Choosing prediction over explanation in psychology: Lessons from machine learning, 2016.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2001.

A. Lo, Why significant variables aren???t automatically good predictors, Proceedings of the National Academy of Sciences, vol.112, issue.45, pp.13892-13899
DOI : 10.1073/pnas.1518285112

T. T. Wu, Genome-wide association analysis by lasso penalized logistic regression, Bioinformatics, vol.25, issue.6, pp.714-721, 2009.
DOI : 10.1093/bioinformatics/btp041

P. Domingos, A few useful things to know about machine learning, Communications of the ACM, vol.55, issue.10, pp.55-78
DOI : 10.1145/2347736.2347755

K. J. Friston, Bayesian decoding of brain images, NeuroImage, vol.39, issue.1, pp.181-205, 2008.
DOI : 10.1016/j.neuroimage.2007.08.013

S. Haufe, 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. Taylor and R. J. Tibshirani, Statistical learning and selective inference, Proceedings of the National Academy of Sciences, vol.112, issue.25, pp.7629-7663
DOI : 10.1073/pnas.1507583112

Y. W. Teh and M. I. Jordan, Hierarchical Bayesian nonparametric models with applications, Bayesian nonparametrics, issue.1, 2010.
DOI : 10.1017/CBO9780511802478.006

S. B. Eickhoff, Connectivity-based parcellation: Critique and implications, Human Brain Mapping, vol.5, issue.5, 2015.
DOI : 10.1002/hbm.22933

URL : https://hal.archives-ouvertes.fr/hal-01184563

E. P. Wigner, The Unreasonable Effectiveness of Mathematics in the Natural Sciences, Communications on Pure and Applied Mathematics, vol.13, pp.1-14, 1960.
DOI : 10.1142/9789814503488_0018

G. James, An introduction to statistical learning The predictive capacity of personal genome sequencing, Science translational medicine, vol.112, issue.81 4133, pp.133-58, 2012.

H. Akil, Neuroscience Training for the 21st Century, Neuron, vol.90, issue.5, pp.917-926, 2006.
DOI : 10.1016/j.neuron.2016.05.030

C. M. Bishop and J. Lasserre, Generative or discriminative? getting the best of both worlds. Bayesian statistics, pp.3-24, 2007.

A. Y. Ng and M. I. Jordan, On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes Advances in neural information processing systems, p.841, 2002.

G. E. Hinton, The "wake-sleep" algorithm for unsupervised neural networks, Science, vol.268, issue.5214, pp.1158-1161, 1995.
DOI : 10.1126/science.7761831

B. Efron, Bootstrap methods: another look at the jackknife. The annals of Statistics, pp.1-26, 1979.

M. I. Jordan, A message from the President: The era of big data, ISBA Bulletin, vol.18, issue.2, pp.1-3, 2011.

Y. Yang, M. J. Wainwright, and M. I. Jordan, On the computational complexity of highdimensional Bayesian variable selection, 2015.

M. I. Jordan, An Introduction to Variational Methods for Graphical Models, Machine learning, pp.183-233, 1999.
DOI : 10.1007/978-94-011-5014-9_5

D. J. Mackay, Information theory, inference and learning algorithms-supervised learning with deep generative models, Advances in Neural Information Processing Systems, 2003.

R. A. Fisher and W. A. Mackenzie, Studies in crop variation. II. The manurial response of different potato varieties, The Journal of Agricultural Science, vol.13, issue.03, pp.13-311, 1923.
DOI : 10.1017/S0021859600003592

R. L. Wasserstein and N. A. Lazar, The ASA's statement on p-values: context, process, and purpose. The American Statistician, pp.0-00

S. N. Goodman, Toward evidence-based medical statistics. 1: The P value fallacy. Annals of internal medicine, pp.995-1004, 1999.

J. Berkson, Some Difficulties of Interpretation Encountered in the Application of the Chi-Square Test, Journal of the American Statistical Association, vol.33, issue.203, pp.526-536, 1938.
DOI : 10.1080/01621459.1938.10502329

L. Breiman and P. Spector, Submodel selection and evaluation in regression. The X-random case. International statistical review/revue internationale de Statistique, pp.291-319, 1992.

Y. S. Abu-mostafa, M. Magdon-ismail, and H. T. Lin, Learning from data, California: AMLBook. 99. Vapnik, V.N., Statistical Learning Theory, 1989.

C. Dwork, The reusable holdout: Preserving validity in adaptive data analysis, Science, vol.349, issue.6248, pp.636-638, 2015.
DOI : 10.1126/science.aaa9375