A. Abi-dargham and G. Horga, The search for imaging biomarkers in 520 psychiatric disorders, Nature Medicine, vol.22, 1248.

A. Abraham, M. Milham, A. D. Martino, R. C. Craddock, D. Samaras et al., Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example, NeuroImage, vol.147, 2016.
DOI : 10.1016/j.neuroimage.2016.10.045

URL : https://hal.archives-ouvertes.fr/hal-01398867

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

A. Argyriou, T. Evgeniou, and M. Pontil, Convex multi-task feature learning, Machine Learning, vol.8, issue.7, pp.243-272, 2008.
DOI : 10.1137/0905052

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

B. B. Avants, P. A. Cook, L. Ungar, J. C. Gee, and M. Grossman, Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis, NeuroImage, vol.50, issue.3, pp.1004-1016, 2010.
DOI : 10.1016/j.neuroimage.2010.01.041

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

G. E. Box and D. R. Cox, An analysis of transformations, Journal of the Royal Statistical Society. Series B (Methodological), pp.211-252, 1964.

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

D. Bzdok, M. Eickenberg, O. Grisel, B. Thirion, and G. Varoquaux, Semi- 540 supervised factored logistic regression for high-dimensional neuroimaging data, pp.2015-3348, 2015.

V. D. Calhoun, J. Sui, K. Kiehl, J. Turner, E. Allen et al., Exploring the Psychosis Functional Connectome: Aberrant Intrinsic Networks in Schizophrenia and Bipolar Disorder, Frontiers in Psychiatry, vol.2, 2012.
DOI : 10.3389/fpsyt.2011.00075

R. Caruana, Multitask Learning, Machine Learning, vol.28, pp.41-75, 1997.
DOI : 10.1007/978-1-4615-5529-2_5

F. X. Castellanos, A. D. Martino, R. C. Craddock, A. D. Mehta, and M. P. Milham, Clinical applications of the functional connectome, NeuroImage, vol.80, 2013.
DOI : 10.1016/j.neuroimage.2013.04.083

E. Castro, V. Gómez-verdejo, M. Martínez-ramón, K. A. Kiehl, and V. D. Calhoun, A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: Application to schizophrenia, NeuroImage, vol.87, pp.1-17, 2014.
DOI : 10.1016/j.neuroimage.2013.10.065

R. C. Craddock, P. E. Holtzheimer, X. P. Hu, and H. S. Mayberg, Disease state prediction from resting state functional connectivity, Disease state prediction from resting state functional connectivity, pp.1619-1628, 2009.
DOI : 10.1007/978-1-4757-2440-0

A. M. Dale, B. Fischl, and M. I. Sereno, Cortical Surface-Based Analysis, NeuroImage, vol.9, issue.2, pp.179-194, 1999.
DOI : 10.1006/nimg.1998.0395

F. Deligianni, M. Centeno, D. W. Carmichael, and J. D. Clayden, Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands, Frontiers in Neuroscience, vol.6, issue.339, 2014.
DOI : 10.1038/nrneurol.2009.198

URL : http://doi.org/10.3389/fnins.2014.00258

D. Martino, A. Yan, C. G. Li, Q. Denio, E. Castellanos et al., The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism, Molecular Psychiatry, vol.3, issue.6, p.659, 2014.
DOI : 10.1007/s12021-012-9151-4

A. T. Drysdale and L. Grosenick, Resting-state connectivity biomark- 565 ers define neurophysiological subtypes of depression, Nature Medicine, 2016.

J. Dubois and R. Adolphs, Building a Science of Individual Differences from fMRI, Trends in Cognitive Sciences, vol.20, issue.6, pp.425-443, 2016.
DOI : 10.1016/j.tics.2016.03.014

P. Elliott and T. C. Peakman, The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine, International Journal of Epidemiology, vol.37, issue.2, pp.234-244, 2008.
DOI : 10.1093/ije/dym276

K. A. Ellis, A. I. Bush, D. Darby, D. D. Fazio, J. Foster et al., The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease, International Psychogeriatrics, vol.46, issue.04, p.672, 2009.
DOI : 10.1016/S0006-3223(02)01348-3

E. L. Floch and V. Guillemot, 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. Gönen, M. Kandemir, and S. Kaski, Multitask learning using regular, p.585, 2011.

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

H. Hotelling, RELATIONS BETWEEN TWO SETS OF VARIATES, Biometrika, vol.28, issue.3-4, pp.321-377, 1936.
DOI : 10.1093/biomet/28.3-4.321

S. E. Hyman, Can neuroscience be integrated into the DSM-V?, Nature Reviews Neuroscience, vol.148, issue.9, 2007.
DOI : 10.1176/ajp.148.4.421

T. Insel, B. Cuthbert, M. Garvey, R. Heinssen, D. S. Pine et al., Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders, American Journal of Psychiatry, vol.167, issue.7, pp.748-751, 2010.
DOI : 10.1176/appi.ajp.2010.09091379

T. R. Insel and B. N. Cuthbert, Brain disorders? Precisely, Science, vol.7, issue.1, pp.499-500, 2015.
DOI : 10.1038/npp.2011.225

A. J. Izenman, Reduced-rank regression for the multivariate linear model, Journal of Multivariate Analysis, vol.5, issue.2, pp.248-264, 1975.
DOI : 10.1016/0047-259X(75)90042-1

URL : http://doi.org/10.1016/0047-259x(75)90042-1

D. Borowski, B. Britson, P. J. Whitwell, J. Ward, and C. , The alzheimer's disease neuroimaging initiative (adni): Mri methods, Journal of Magnetic Resonance Imaging, vol.27, pp.685-691, 2008.

R. Keefe, The Brief Assessment of Cognition in Schizophrenia: reliability, sensitivity, and comparison with a standard neurocognitive battery, Schizophrenia Research, vol.68, issue.2-3, p.610, 2004.
DOI : 10.1016/j.schres.2003.09.011

M. Kowalski, Sparse regression using mixed norms, Applied and Computational Harmonic Analysis, vol.27, issue.3, pp.303-324, 2009.
DOI : 10.1016/j.acha.2009.05.006

URL : https://hal.archives-ouvertes.fr/hal-00202904

A. Krishnan, L. J. Williams, A. R. Mcintosh, and H. Abdi, Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review, NeuroImage, vol.56, issue.2, p.615, 2011.
DOI : 10.1016/j.neuroimage.2010.07.034

C. Lai, The merits and problems of Neuropsychiatric Inventory as an assessment tool in people with dementia and other neurological disorders, Clinical Interventions in Aging, 1051.
DOI : 10.2147/CIA.S63504

J. M. Lampe, L. Rahim, M. Abraham, A. Craddock, R. C. Riedel-heller et al., Predicting brain-age from multimodal imaging data captures cognitive impairment, NeuroImage, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01403005

M. Loeffler and C. Engel, The life-adult-study: objectives and design 625, 2015.

A. F. Marquand, M. Brammer, S. C. Williams, and O. M. Doyle, Bayesian multi-task learning for decoding multi-subject neuroimaging data, NeuroImage, vol.92, pp.298-311, 2014.
DOI : 10.1016/j.neuroimage.2014.02.008

URL : http://doi.org/10.1016/j.neuroimage.2014.02.008

K. L. Miller and F. Alfaro-almagro, 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

J. M. Monteiro, A. Rao, J. Shawe-taylor, and J. Mourão-miranda, A multiple hold-out framework for Sparse Partial Least Squares, Journal of Neuroscience Methods, vol.271, p.635, 2016.
DOI : 10.1016/j.jneumeth.2016.06.011

URL : http://doi.org/10.1016/j.jneumeth.2016.06.011

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikitlearn: Machine learning in Python, Journal of Machine Learning Research, vol.640, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

R. Petersen, P. Aisen, L. Beckett, M. Donohue, A. Gamst et al., Alzheimer's Disease Neuroimaging Initiative (ADNI): Clinical characterization, Neurology, vol.74, issue.3, pp.201-209, 2010.
DOI : 10.1212/WNL.0b013e3181cb3e25

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2809036

M. Rahim, B. Thirion, C. Comtat, and G. Varoquaux, Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction, IEEE Journal of Selected Topics in Signal Processing, vol.10, issue.7, pp.1204-1213, 2016.
DOI : 10.1109/JSTSP.2016.2600400

A. Savio and M. Graña, Local activity features for computer aided diagnosis of schizophrenia on resting-state fMRI, Neurocomputing, vol.164, pp.154-161, 2015.
DOI : 10.1016/j.neucom.2015.01.079

J. Schrouff, J. Mourão-miranda, C. Phillips, and J. Parvizi, Decoding intracranial EEG data with multiple kernel learning method, Journal of Neuroscience Methods, vol.261, pp.19-28, 2016.
DOI : 10.1016/j.jneumeth.2015.11.028

URL : http://doi.org/10.1016/j.jneumeth.2015.11.028

M. Segal and Y. Xiao, Multivariate random forests, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.93, issue.1, pp.80-87, 2011.
DOI : 10.2307/2669615

L. M. Shaw, H. Vanderstichele, M. Knapik-czajka, and M. Figurski, 660 Qualification of the analytical and clinical performance of CSF biomarker analyses in ADNI, Acta Neuropathologica, vol.121, 2011.

S. M. Smith, T. E. Nichols, D. Vidaurre, A. M. Winkler, T. E. Behrens et al., A positive-negative mode of population covariation links brain connectivity, demographics and behavior, Nature Neuroscience, vol.55, issue.11, pp.1565-1567, 2015.
DOI : 10.1016/j.neuroimage.2005.12.057

J. Sui, T. Adali, Q. Yu, J. Chen, and V. D. Calhoun, A review of multivariate methods for multimodal fusion of brain imaging data, Journal of Neuroscience Methods, vol.204, issue.1, pp.68-81, 2012.
DOI : 10.1016/j.jneumeth.2011.10.031

P. M. Thompson, J. L. Stein, S. E. Medland, D. P. Hibar, A. A. Vasquez et al., The enigma consortium: large-scale collaborative analyses of neuroimaging and genetic data, pp.153-182, 2014.

D. C. Van-essen and . Smith, The WU-Minn Human Connectome Project: An overview, NeuroImage, vol.80, pp.62-79, 2013.
DOI : 10.1016/j.neuroimage.2013.05.041

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, NeuroImage, vol.145, 2016.
DOI : 10.1016/j.neuroimage.2016.10.038

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

M. Vounou, T. E. Nichols, G. Montana, and A. D. Initiative, Discovering genetic associations with high-dimensional neuroimaging pheno- 680 types: a sparse reduced-rank regression approach, Neuroimage, vol.53, 2010.
DOI : 10.1016/j.neuroimage.2010.07.002

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5253177

H. Wang, F. Nie, H. Huang, S. Risacher, C. Ding et al., Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance, International Conference on Computer Vision, Institute of Electrical and Electronics En- 685 gineers, p.2011, 2011.

H. Wang, F. Nie, H. Huang, J. Yan, S. Kim et al., High-order multi-task feature learning to identify longitudinal phenotypic markers for alzheimer's disease progression prediction, pp.1277-1285, 2012.

L. Wang, Y. Zang, Y. He, M. Liang, X. Zhang et al., Changes in hippocampal connectivity in the early stages of Alzheimer's disease: Evidence from resting state fMRI, Changes in hippocampal connectivity in the early stages of alzheimer´salzheimer´s disease: Evidence from resting state fMRI, pp.496-504, 2006.
DOI : 10.1016/j.neuroimage.2005.12.033

C. W. Woo, L. J. Chang, M. A. Lindquist, and T. D. Wager, 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

L. Yuan, Y. Wang, P. M. Thompson, V. A. Narayan, and J. Ye, Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data, NeuroImage, vol.61, issue.3, pp.622-632, 2012.
DOI : 10.1016/j.neuroimage.2012.03.059

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, 2006.
DOI : 10.1198/016214502753479356

D. Zhang and D. Shen, Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease, NeuroImage, vol.59, issue.2, pp.895-907, 2012.
DOI : 10.1016/j.neuroimage.2011.09.069