J. Dubois and R. Adolphs, Building a Science of Individual Differences from fMRI, Trends in cognitive sciences, pp.425-443, 2016.
DOI : 10.1016/j.tics.2016.03.014

C. Woo, Building better biomarkers: brain models in translational neuroimaging, Nature Neuroscience, vol.5, issue.3, pp.365-377, 2017.
DOI : 10.1016/j.neuron.2011.08.026

G. Varoquaux, Assessing and tuning brain decoders: crossvalidation , caveats, and guidelines, NeuroImage, vol.145, 2017.
DOI : 10.1016/j.neuroimage.2016.10.038

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

S. M. Smith, A positive-negative mode of population covariation links brain connectivity, demographics and behavior, Nature Neuroscience, vol.55, issue.11, 2015.
DOI : 10.1016/j.neuroimage.2005.12.057

K. L. Miller, Multimodal population brain imaging in the UK Biobank prospective epidemiological study, Nature Neuroscience, vol.57, issue.11, 2016.
DOI : 10.1016/j.neuroimage.2010.01.069

E. L. Floch, Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares, NeuroImage, vol.63, issue.1, 2012.
DOI : 10.1016/j.neuroimage.2012.06.061

URL : https://hal.archives-ouvertes.fr/hal-00750902

M. Vounou, Discovering genetic assoc. with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach, NeuroImage, 2010.

J. Sui, A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia, NeuroImage, vol.51, issue.1, 2010.
DOI : 10.1016/j.neuroimage.2010.01.069

J. M. Monteiro, A multiple hold-out framework for sparse partial least squares Reduced-rank regression for the multivariate linear model, J. Neurosci M. J. of multivariate analysis, 1975.

H. Hotelling, Relations between two sets of variates, Biometrika, vol.284, issue.3, pp.321-377, 1936.

A. Krishnan, Pls methods for neuroimaging: A tutorial and review, NeuroImage, 2011.
DOI : 10.1016/j.neuroimage.2010.07.034

L. Breiman and J. H. Friedman, Predicting Multivariate Responses in Multiple Linear Regression, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.59, issue.1, 1997.
DOI : 10.1111/1467-9868.00054

R. A. Poldrack, T. Nichols, and J. Mumford, Handbook of Functional MRI Data Analysis, 2009.
DOI : 10.1017/CBO9780511895029

A. Abraham, F. Pedregosa, M. Eickenberg, P. Gervais, A. Mueller et al., Machine learning for neuroimaging with scikit-learn, Frontiers in Neuroinformatics, vol.8, p.14, 2014.
DOI : 10.3389/fninf.2014.00014

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

Y. Behzadi, K. Restom, J. Liau, and T. T. Liu, A component based noise correction method (CompCor) for BOLD and perfusion based fMRI, NeuroImage, vol.37, issue.1, pp.90-101, 2007.
DOI : 10.1016/j.neuroimage.2007.04.042

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2214855/pdf

P. Bellec, Multi-level bootstrap analysis of stable clusters in resting-state fMRI, NeuroImage, 2010.

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, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905