A. Abraham, F. Pedregosa, M. Eickenberg, P. Gervais, A. Mueller et al., Machine learning for neuroimaging with scikit-learn.Front, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01093971

M. Chupin, A. Hammers, R. Liu, O. Colliot, J. Burdett et al., Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: Method and validation, NeuroImage, vol.46, issue.3, pp.749-761, 2009.
DOI : 10.1016/j.neuroimage.2009.02.013

URL : https://hal.archives-ouvertes.fr/hal-00805390

Y. Cointepas, D. Geffroy, N. Souedet, I. Denghien, D. Rivière et al., The BrainVISA project: a shared software development infrastructure for biomedical imaging research, 2010.

O. Colliot, G. Chetelat, M. Chupin, B. Desgranges, B. Magnin et al., Discrimination between Alzheimer Disease, Mild Cognitive Impairment, and Normal Aging by Using Automated Segmentation of the Hippocampus, Radiology, vol.248, issue.1, pp.194-201, 2008.
DOI : 10.1148/radiol.2481070876

URL : https://hal.archives-ouvertes.fr/inserm-00383265

A. Ferguson, J. Nielson, . Cragin, . Mh, A. Bandrowski et al., Big data from small data: datasharing in the 'long tail' of neuroscience, ME Nature neuroscience, issue.11, pp.17-1442, 2014.

B. Fischl, FreeSurfer, NeuroImage, vol.62, issue.2, pp.774-781, 2012.
DOI : 10.1016/j.neuroimage.2012.01.021

G. Frisoni, A. Redolfi, D. Manset, . Rousseau, . Mé et al., Virtual imaging laboratories for marker discovery in neurodegenerative diseases, Nature Reviews Neurology, vol.5, issue.8, pp.429-438, 2011.
DOI : 10.1038/nrneurol.2011.99

P. Guevara, D. Duclap, C. Poupon, L. Marrakchi-kacem, P. Fillard et al., Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas, NeuroImage, vol.61, issue.4, pp.1083-1099, 2012.
DOI : 10.1016/j.neuroimage.2012.02.071

URL : https://hal.archives-ouvertes.fr/hal-00700800

J. Mangin, E. Jouvent, and A. Cachia, In-vivo measurement of cortical morphology: means and meanings, Current Opinion in Neurology, vol.23, pp.359-367, 2010.
DOI : 10.1097/WCO.0b013e32833a0afc

J. Mangin, D. Riviere, A. Cachia, E. Duchesnay, Y. Cointepas et al., Object-Based Morphometry of the Cerebral Cortex, IEEE Transactions on Medical Imaging, vol.23, issue.8, pp.968-982, 2004.
DOI : 10.1109/TMI.2004.831204

J. Martini, . Habert, . Mo, N. Yeni, A. Giron et al., Large-scale validation of a computer-aided quantification for 123I-FP-CIT images, Society of Nuclear Medicine Annual Meeting Abstracts, p.2037, 2014.

W. D. Penny, K. J. Friston, J. T. Ashburner, S. J. Kiebel, and . Nichols, Statistical Parametric Mapping: The Analysis of Functional Brain Images: The Analysis of Functional Brain Images, 2011.

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

J. Poline, J. Breeze, S. Ghosh, K. Gorgolewski, Y. Halchenko et al., Data sharing in neuroimaging research, Frontiers in Neuroinformatics, vol.6, 2012.
DOI : 10.3389/fninf.2012.00009

T. Samaille, L. Fillon, R. Cuingnet, E. Jouvent, H. Chabriat et al., Contrast-Based Fully Automatic Segmentation of White Matter Hyperintensities: Method and Validation, PLoS ONE, vol.41, issue.1, p.48953, 2013.
DOI : 10.1371/journal.pone.0048953.s005

URL : https://hal.archives-ouvertes.fr/hal-00789657

T. Sejnowski, . Churchland, . Ps, and J. Movshon, Putting big data to good use in neuroscience, Nature Neuroscience, vol.17, issue.11, pp.1440-1441, 2014.
DOI : 10.1126/science.3055294

J. Van-horn and A. Toga, Human neuroimaging as a ???Big Data??? science, Brain Imaging and Behavior, vol.49, issue.2, pp.323-331, 2014.
DOI : 10.1007/s11682-013-9255-y

J. Wallis, R. E. Borgman, and C. , If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology, PLoS ONE, vol.90, issue.7, 2013.
DOI : 10.1371/journal.pone.0067332.t005