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

A. Vanhaudenhuyse, Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients, Brain, vol.133, issue.1, p.161, 2010.
DOI : 10.1093/brain/awp313

D. Fair, Development of distinct control networks through segregation and integration, Proceedings of the National Academy of Sciences, vol.104, issue.33, p.13507, 2007.
DOI : 10.1073/pnas.0705843104

L. Uddin and V. Menon, The anterior insula in autism: Under-connected and under-examined, Neuroscience & Biobehavioral Reviews, vol.33, issue.8, pp.1198-1203, 2009.
DOI : 10.1016/j.neubiorev.2009.06.002

J. Sato, An fMRI normative database for connectivity networks using one-class support vector machines, Human Brain Mapping, vol.36, issue.4, p.1068, 2008.
DOI : 10.1002/hbm.20569

M. Rocca, Altered functional and structural connectivities in patients with MS: A 3-T study, Neurology, vol.69, issue.23, p.2136, 2007.
DOI : 10.1212/01.wnl.0000295504.92020.ca

C. Lenglet, Statistics on the Manifold of Multivariate Normal Distributions: Theory and Application to Diffusion Tensor MRI Processing, Journal of Mathematical Imaging and Vision, vol.12, issue.1, pp.423-444, 2006.
DOI : 10.1007/s10851-006-6897-z

X. Pennec, P. Fillard, and N. , A Riemannian Framework for Tensor Computing, International Journal of Computer Vision, vol.6, issue.2, pp.41-66, 2006.
DOI : 10.1007/s11263-005-3222-z

URL : https://hal.archives-ouvertes.fr/inria-00070743

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

P. Fletcher and S. Joshi, Riemannian geometry for the statistical analysis of diffusion tensor data, Signal Processing, vol.87, issue.2, pp.250-262, 2007.
DOI : 10.1016/j.sigpro.2005.12.018