F. Bunea, Y. She, H. Ombao, A. Gongvatana, K. Devlin et al., Penalized least squares regression methods and applications to neuroimaging, NeuroImage, vol.55, issue.4, pp.1519-1527, 2011.
DOI : 10.1016/j.neuroimage.2010.12.028

C. Chu, S. K. Kim, Y. Lin, Y. Yu, G. R. Bradski et al., Map-reduce for machine learning on multicore, NIPS, pp.281-288, 2006.

J. Dean and S. Ghemawat, MapReduce, Communications of the ACM, vol.51, issue.1, pp.107-113, 2008.
DOI : 10.1145/1327452.1327492

V. Fritsch, G. Varoquaux, B. Thyreau, J. Poline, and B. Thirion, Detecting Outlying Subjects in High-Dimensional Neuroimaging Datasets with Regularized Minimum Covariance Determinant, Med Image Comput Comput Assist Interv, vol.41, issue.3, pp.264-271, 2011.
DOI : 10.1016/j.neuroimage.2008.02.042

URL : https://hal.archives-ouvertes.fr/inria-00626857

O. Kohannim, D. P. Hibar, J. L. Stein, N. Jahanshad, C. R. Jack et al., Boosting power to detect genetic associations in imaging using multi-locus, genome-wide scans and ridge regression, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1855-1859, 2011.
DOI : 10.1109/ISBI.2011.5872769

S. Laguitton, D. Rivì-ere, T. Vincent, C. Fischer, D. Geffroy et al., Soma-workflow: a unified and simple interface to parallel computing resources, MICCAI Workshop on High Performance and Distributed Computing for Medical Imaging, 2011.

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

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

S. D. Pollak and D. J. Kistler, Early experience is associated with the development of categorical representations for facial expressions of emotion, Proceedings of the National Academy of Sciences, vol.99, issue.13, pp.999072-9076, 2002.
DOI : 10.1073/pnas.142165999

G. Schumann, E. Loth, T. Banaschewski, A. Barbot, G. Barker et al., The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology, Molecular Psychiatry, vol.47, issue.12, pp.151128-1139, 2010.
DOI : 10.1016/j.brainres.2006.03.029

J. L. Stein, X. Hua, S. Lee, A. J. Ho, A. D. Leow et al., Thompson, and Alzheimer's Disease Neuroimaging Initiative. Voxelwise genome-wide association study (vGWAS), Neuroimage, issue.3, pp.531160-1174, 2010.

G. Varoquaux, Joblib: running python function as pipeline jobs

M. Vounou, T. E. Nichols, and G. , Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach, NeuroImage, vol.53, issue.3, pp.1147-1159, 2010.
DOI : 10.1016/j.neuroimage.2010.07.002