F. Ferrarelli, Breakdown in cortical effective connectivity during midazolam-induced loss of consciousness, Proceedings of the National Academy of Sciences, vol.107, issue.6, pp.2681-2686, 2010.
DOI : 10.1073/pnas.0913008107

G. Tononi, The Information Integration Theory of Consciousness, BMC neuroscience, vol.4, issue.15, 2004.
DOI : 10.1002/9780470751466.ch23

J. Burns, An evolutionary theory of schizophrenia: Cortical connectivity, metarepresentation, and the social brain, Behavioral and Brain Sciences, vol.27, issue.06, p.831, 2004.
DOI : 10.1017/S0140525X04000196

. Fig, Performance of the different probabilistic models under leave-one-out validation Inside the main figure is the Box-and-Whisker diagram over all subjects. 4. K. Konrad and S. Eickhoff: Is the adhd brain wired differently? a review on structural and functional connectivity in attention deficit hyperactivity disorder, Human Brain Mapping, vol.3, issue.316, pp.904-916, 2010.

R. Müller, The study of autism as a distributed disorder, Mental Retardation and Developmental Disabilities Research Reviews, vol.25, issue.1, pp.85-95, 2007.
DOI : 10.1002/mrdd.20141

L. Pollonini, Information Communication Networks in Severe Traumatic Brain Injury, Brain Topography, vol.2, issue.7872, pp.221-226, 2010.
DOI : 10.1007/s10548-010-0139-9

A. Morcom and P. Fletcher, Does the brain have a baseline? Why we should be resisting a rest, NeuroImage, vol.37, issue.4, pp.1073-82, 2007.
DOI : 10.1016/j.neuroimage.2006.09.013

M. Greicius, Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network, Cerebral Cortex, vol.19, issue.1, 2008.
DOI : 10.1093/cercor/bhn059

J. Damoiseaux and M. Greicius, Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity, Brain Structure and Function, vol.68, issue.1, pp.525-533, 2009.
DOI : 10.1007/s00429-009-0208-6

C. Honey, Network structure of cerebral cortex shapes functional connectivity on multiple time scales, Proceedings of the National Academy of Sciences, vol.104, issue.24, p.10240, 2007.
DOI : 10.1073/pnas.0701519104

C. Honey, Predicting human resting-state functional connectivity from structural connectivity, Proceedings of the National Academy of Sciences, vol.106, issue.6, pp.2035-2040, 2009.
DOI : 10.1073/pnas.0811168106

M. P. Van-den-heuvel, Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain, Human Brain Mapping, vol.100, issue.Part 3, pp.3127-3168, 2009.
DOI : 10.1002/hbm.20737

A. Venkataraman, Joint Generative Model for fMRI/DWI and Its Application to Population Studies, a) Original fMRI (b) Original DTI (c, pp.191-200, 2010.
DOI : 10.1007/978-3-642-15705-9_24

. Fig, Qualitative results for one leave-one-out subject when its structural connectivity matrix is used as a predictor. a) Sampled covariance matrix of the original fMRI signal, b) Structural connectivity matrix used as the input to the trained model. c) Sparsity pattern over all other subjects, d) Prediction based on Cor (LW) Black circles point out to relatively large errors in prediction for several connections, e) Prediction based on the Chol cov, Prediction based on the SP MAR (L), g) Prediction based on the SP MAR (R)

G. Varoquaux, Brain covariance selection: better individual functional connectivity models using population prior, Advances in Neural Information Processing Systems, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00512451

F. Deligianni, Inference of functional connectivity from structural brain connectivity, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1113-1116, 2010.
DOI : 10.1109/ISBI.2010.5490188

S. Lauritzen, Graphical models, 1996.

S. Smith, Network modelling methods for FMRI, NeuroImage, vol.54, issue.2, 2010.
DOI : 10.1016/j.neuroimage.2010.08.063

P. Fransson and G. Marrelec, The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis, NeuroImage, vol.42, issue.3, pp.1178-1184, 2008.
DOI : 10.1016/j.neuroimage.2008.05.059

Y. Lecun, Energy-based models, Predicting structured data, pp.191-245, 2007.

W. Förstner and B. Moonen, A Metric for Covariance Matrices, Qua vadis geodesia, pp.113-128, 1999.
DOI : 10.1007/978-3-662-05296-9_31

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

C. Lenglet, Statistics on the Manifold of Multivariate Normal Distributions: Theory and Application to Diffusion Tensor MRI Processing, a) SP MAR (L) (b) SP MAR (R, pp.423-444, 2006.
DOI : 10.1007/s10851-006-6897-z

. Fig, Identifying structural connections associated with the default mode network . With yellow is represented the lateral parietal cortex, green areas represent the posterior cingulate gyrus (PCC ), blue and light blue represent the medial prefrontal and orbito-frontal areas

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B, vol.58, issue.1, pp.267-288, 1996.

D. Donoho, For most large underdetermined systems of linear equations the minimal ???1-norm solution is also the sparsest solution, Communications on Pure and Applied Mathematics, vol.50, issue.6, pp.797-829, 2006.
DOI : 10.1002/cpa.20132

B. Efron, Least angle regression, Annals of statistics, vol.32, issue.2, pp.407-499, 2004.

P. Aljabar, Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy, NeuroImage, vol.46, issue.3, pp.726-764, 2009.
DOI : 10.1016/j.neuroimage.2009.02.018

D. Rueckert, Non-rigid registration using free-form deformations, IEEE Transactions on Medical Imaging, vol.18, pp.712-721, 1999.
DOI : 10.1007/978-0-387-09749-7_15

K. Friston, Statistical parametric mapping: the analysis of functional brain images, 2007.

S. Smith, Advances in functional and structural MR image analysis and implementation as FSL, NeuroImage, vol.23, pp.208-219, 2004.
DOI : 10.1016/j.neuroimage.2004.07.051

T. Behrens, Characterization and propagation of uncertainty in diffusion-weighted MR imaging, Magnetic Resonance in Medicine, vol.36, issue.5, pp.1077-1088, 2003.
DOI : 10.1002/mrm.10609

Y. Iturria-medina and E. Canales-rodriguez, Characterizing brain anatomical connections using diffusion weighted MRI and graph theory, NeuroImage, vol.36, issue.3, pp.645-660, 2007.
DOI : 10.1016/j.neuroimage.2007.02.012

E. Robinson, Identifying population differences in whole-brain structural networks: A machine learning approach, NeuroImage, vol.50, issue.3, pp.910-919, 2010.
DOI : 10.1016/j.neuroimage.2010.01.019

B. Mädler, S. Drabycz, and S. Kolind, Is diffusion anisotropy an accurate monitor of myelination?, Magnetic Resonance Imaging, vol.26, issue.7, pp.874-888, 2008.
DOI : 10.1016/j.mri.2008.01.047

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