V. Fritsch, G. Varoquaux, B. Thyreau, J. B. Poline, and B. Thirion, Detecting outliers in high-dimensional neuroimaging datasets with robust covariance estimators, Medical Image Analysis, vol.16, issue.7, 2012.
DOI : 10.1016/j.media.2012.05.002

URL : https://hal.archives-ouvertes.fr/hal-00701225

S. Gerber, T. Tasdizen, S. Joshi, and R. Whitaker, On the Manifold Structure of the Space of Brain Images, Med Image Comput Comput Assist Interv, vol.12, p.305, 2009.
DOI : 10.1007/978-3-642-04268-3_38

I. Kalatzis, N. Piliouras, E. Ventouras, C. Papageorgiou, A. Rabavilas et al., Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals, Computer Methods and Programs in Biomedicine, vol.75, issue.1, pp.11-22, 2004.
DOI : 10.1016/j.cmpb.2003.09.003

O. Ledoit and M. Wolf, A well-conditioned estimator for large-dimensional covariance matrices, Journal of Multivariate Analysis, vol.88, issue.2, pp.365-411, 2004.
DOI : 10.1016/S0047-259X(03)00096-4

J. Mourao-miranda, D. R. Hardoon, T. Hahn, A. F. Marquand, S. C. Williams et al., Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine, NeuroImage, vol.58, issue.3, pp.793-804, 2011.
DOI : 10.1016/j.neuroimage.2011.06.042

M. Perrot, D. Rivì-ere, A. Tucholka, and J. F. Mangin, Joint Bayesian Cortical Sulci Recognition and Spatial Normalization, Inf Process Med Imaging, vol.12, issue.5, pp.176-187, 2009.
DOI : 10.1016/j.media.2008.06.005

URL : https://hal.archives-ouvertes.fr/hal-00776670

N. L. Roux and F. Bach, Local component analysis. ArXiv e-prints, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00617965

B. Schölkopf, J. C. Platt, J. C. Shawe-taylor, A. J. Smola, and R. C. Williamson, Estimating the Support of a High-Dimensional Distribution, Neural Computation, vol.6, issue.1, pp.1443-1471, 2001.
DOI : 10.1214/aos/1069362732

S. S. Shapiro and M. B. Wilk, An analysis of variance test for normality (complete samples), Biometrika, vol.52, issue.3-4, pp.591-611, 1965.
DOI : 10.1093/biomet/52.3-4.591

B. Thirion, P. Pinel, S. Mériaux, A. Roche, S. Dehaene et al., Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses, NeuroImage, vol.35, issue.1, pp.105-120, 2007.
DOI : 10.1016/j.neuroimage.2006.11.054

URL : https://hal.archives-ouvertes.fr/cea-00371089

J. Wang, V. Saligrama, and D. A. Castañón, Structural similarity and distance in learning ArXiv e-prints, 2011.

M. Zweig and G. Campbell, Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine, Clin Chem, vol.39, issue.4, pp.561-577, 1993.