S. Ogawa, T. Lee, A. Kay, and D. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation., Proc. Natl. Acad. Sci. USA, pp.9868-9872, 1990.
DOI : 10.1073/pnas.87.24.9868
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC55275

K. J. Friston, A. P. Holmes, J. Poline, P. J. Grasby, S. C. Williams et al., Analysis of fMRI Time-Series Revisited, NeuroImage, vol.2, issue.1, pp.45-53, 1995.
DOI : 10.1006/nimg.1995.1007

B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde, Functional connectivity in the motor cortex of resting human brain using echo-planar mri, Magnetic Resonance in Medicine, vol.13, issue.4, pp.537-541, 1995.
DOI : 10.1002/mrm.1910340409

M. D. Fox, A. Z. Snyder, J. L. Vincent, M. Corbetta, D. C. Van-essen et al., From The Cover: The human brain is intrinsically organized into dynamic, anticorrelated functional networks, Proc. Natl. Acad. Sci. USA, pp.9673-9678, 2005.
DOI : 10.1073/pnas.0504136102

R. B. Buxton and L. Frank, A Model for the Coupling Between Cerebral Blood Flow and Oxygen Metabolism During Neural Stimulation, Journal of Cerebral Blood Flow & Metabolism, vol.10, issue.5, pp.64-72, 1997.
DOI : 10.1097/00004647-199701000-00009

K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics, NeuroImage, vol.12, issue.4, pp.466-477, 2000.
DOI : 10.1006/nimg.2000.0630

R. B. Buxton, K. Uludag, D. J. Dubowitz, and T. T. Liu, Modeling the hemodynamic response to brain activation, NeuroImage, vol.23, pp.220-233, 2004.
DOI : 10.1016/j.neuroimage.2004.07.013

J. J. Riera, J. Watanabe, I. Kazuki, M. Naoki, E. Aubert et al., A state-space model of the hemodynamic approach: nonlinear filtering of BOLD signals, NeuroImage, vol.21, issue.2, pp.547-567, 2004.
DOI : 10.1016/j.neuroimage.2003.09.052

D. Lashkari, R. Sridharan, E. Vul, P. J. Hsieh, N. Kanwisher et al., Search for patterns of functional specificity in the brain: A nonparametric hierarchical Bayesian model for group fMRI data, NeuroImage, vol.59, issue.2, pp.1348-1368, 2012.
DOI : 10.1016/j.neuroimage.2011.08.031

K. J. Friston, P. Jezzard, and R. Turner, Analysis of functional MRI time-series, Human Brain Mapping, vol.12, issue.2, pp.153-171, 1994.
DOI : 10.1002/hbm.460010207

G. M. Boynton, S. A. Engel, G. H. Glover, and D. J. Heeger, Linear systems analysis of functional magnetic resonance imaging in human V1, J. Neurosci, vol.16, issue.13, pp.4207-4221, 1996.

D. A. Handwerker, J. M. Ollinger, and M. D. Esposito, Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses, NeuroImage, vol.21, issue.4, pp.1639-1651, 2004.
DOI : 10.1016/j.neuroimage.2003.11.029

P. Ciuciu, T. Vincent, A. Fouque, and A. Roche, Improved fMRI group studies based on spatially varying non-parametric BOLD signal modeling, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1263-1266, 2008.
DOI : 10.1109/ISBI.2008.4541233

S. Badillo, T. Vincent, and P. Ciuciu, Impact of the joint detection-estimation approach on random effects group studies in FMRI, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.376-380, 2011.
DOI : 10.1109/ISBI.2011.5872427
URL : https://hal.archives-ouvertes.fr/hal-00854626

M. A. Lindquist, J. M. Loh, L. Y. Atlas, and T. D. Wager, Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling, NeuroImage, vol.45, issue.1, pp.187-198, 2009.
DOI : 10.1016/j.neuroimage.2008.10.065

G. H. Glover, Deconvolution of Impulse Response in Event-Related BOLD fMRI1, NeuroImage, vol.9, issue.4, pp.416-429, 1999.
DOI : 10.1006/nimg.1998.0419

R. Henson, M. Rugg, and K. Friston, The choice of basis functions in event-related fMRI, Neuroimage (HBM'01), p.149, 2001.
DOI : 10.1016/S1053-8119(01)91492-2

C. Goutte, F. A. Nielsen, and L. K. Hansen, Modeling the hemodynamic response in fMRI using smooth FIR filters, IEEE Transactions on Medical Imaging, vol.19, issue.12, pp.1188-1201, 2000.
DOI : 10.1109/42.897811

P. Ciuciu, J. Poline, G. Marrelec, J. Idier, . Ch et al., Unsupervised robust nonparametric estimation of the hemodynamic response function for any fmri experiment, IEEE Transactions on Medical Imaging, vol.22, issue.10, pp.1235-1251, 2003.
DOI : 10.1109/TMI.2003.817759
URL : https://hal.archives-ouvertes.fr/cea-00333694

G. Marrelec, P. Ciuciu, M. Pélégrini-issac, and H. Benali, Estimation of the hemodynamic response function in eventrelated functional MRI: Bayesian networks as a framework for efficient Bayesian modeling and inference, IEEE Trans

J. Kershaw, B. A. Ardekani, and I. Kanno, Application of Bayesian inference to fMRI data analysis, IEEE Transactions on Medical Imaging, vol.18, issue.12, pp.1138-1152, 1999.
DOI : 10.1109/42.819324

S. Makni, P. Ciuciu, J. Idier, and J. Poline, Joint detection-estimation of brain activity in functional MRI: a Multichannel Deconvolution solution, IEEE Transactions on Signal Processing, vol.53, issue.9, pp.3488-3502, 2005.
DOI : 10.1109/TSP.2005.853303

S. Makni, J. Idier, T. Vincent, B. Thirion, G. Dehaene-lambertz et al., A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI, NeuroImage, vol.41, issue.3, pp.941-969, 2008.
DOI : 10.1016/j.neuroimage.2008.02.017
URL : https://hal.archives-ouvertes.fr/cea-00333624

F. De-pasquale, C. D. Gratta, and G. L. Romani, Empirical Markov Chain Monte Carlo Bayesian analysis of fMRI data, NeuroImage, vol.42, issue.1, pp.99-111, 2008.
DOI : 10.1016/j.neuroimage.2008.04.235

T. Vincent, L. Risser, and P. Ciuciu, Spatially Adaptive Mixture Modeling for Analysis of fMRI Time Series, IEEE Transactions on Medical Imaging, vol.29, issue.4, pp.1059-1074, 2010.
DOI : 10.1109/TMI.2010.2042064
URL : https://hal.archives-ouvertes.fr/cea-00470594

B. Thirion, G. Flandin, P. Pinel, A. Roche, P. Ciuciu et al., Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets, Human Brain Mapping, vol.22, issue.8, pp.678-693, 2006.
DOI : 10.1002/hbm.20210

W. D. Penny, S. Kiebel, and K. J. Friston, Variational Bayesian inference for fMRI time series, NeuroImage, vol.19, issue.3, pp.727-741, 2003.
DOI : 10.1016/S1053-8119(03)00071-5
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.209.612

K. J. Friston, J. Mattout, N. Trullijo-barreto, J. Ashburner, and W. Penny, Variational free energy and the Laplace approximation, NeuroImage, vol.34, issue.1, pp.220-234, 2007.
DOI : 10.1016/j.neuroimage.2006.08.035
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2088

C. Makni, S. Beckmann, M. Smith, and . Woolrich, Bayesian deconvolution fMRI data using bilinear dynamical systems, NeuroImage, vol.42, issue.4, pp.1381-1396, 2008.
DOI : 10.1016/j.neuroimage.2008.05.052

M. Woolrich and T. Behrens, Variational bayes inference of spatial mixture models for segmentation, IEEE Transactions on Medical Imaging, vol.25, issue.10, pp.1380-1391, 2006.
DOI : 10.1109/TMI.2006.880682

L. Risser, T. Vincent, F. Forbes, J. Idier, and P. Ciuciu, Min-max Extrapolation Scheme for Fast Estimation of 3D Potts Field Partition Functions. Application to the Joint Detection-Estimation of Brain Activity in fMRI, Journal of Signal Processing Systems, vol.15, issue.1, pp.325-338, 2011.
DOI : 10.1007/s11265-010-0505-6
URL : https://hal.archives-ouvertes.fr/hal-00555919

M. Woolrich, B. Ripley, M. Brady, and S. Smith, Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data, NeuroImage, vol.14, issue.6, pp.1370-1386, 2001.
DOI : 10.1006/nimg.2001.0931

M. Woolrich, M. Jenkinson, J. Brady, and S. Smith, Fully Bayesian Spatio-Temporal Modeling of FMRI Data, IEEE Transactions on Medical Imaging, vol.23, issue.2, pp.213-231, 2004.
DOI : 10.1109/TMI.2003.823065

W. D. Penny, G. Flandin, and N. Trujillo-bareto, Bayesian comparison of spatially regularised general linear models, Human Brain Mapping, vol.5, issue.4, pp.275-293, 2007.
DOI : 10.1002/hbm.20327

W. D. Penny, N. Trujillo-barreto, and K. J. Friston, Bayesian fMRI time series analysis with spatial priors, NeuroImage, vol.24, issue.2, pp.350-362, 2005.
DOI : 10.1016/j.neuroimage.2004.08.034

R. M. Neal and G. E. Hinton, A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants, Learning in Graphical Models, pp.355-368, 1998.
DOI : 10.1007/978-94-011-5014-9_12

M. J. Beal and Z. Ghahramani, The variational Bayesian EM Algorithm for incomplete data: with application to scoring graphical model structures, pp.453-464, 2003.

J. Goldsmith, M. P. Wand, and C. Crainiceanu, Functional regression via variational Bayes, Electronic Journal of Statistics, vol.5, issue.0, pp.572-602, 2011.
DOI : 10.1214/11-EJS619
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3234121

M. J. Beal, Variational Algorithms for Approximate Bayesian Inference, 2003.

A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian data analysis, 2004.

G. Celeux, F. Forbes, and N. Peyrard, EM procedures using mean field-like approximations for Markov model-based image segmentation, Pattern Recognition, vol.36, issue.1, pp.131-144, 2003.
DOI : 10.1016/S0031-3203(02)00027-4
URL : https://hal.archives-ouvertes.fr/inria-00072526

R. Casanova, S. Ryali, J. Serences, L. Yang, R. Kraft et al., The impact of temporal regularization on estimates of the BOLD hemodynamic response function: A comparative analysis, NeuroImage, vol.40, issue.4, pp.1606-1618, 2008.
DOI : 10.1016/j.neuroimage.2008.01.011

P. Pinel, B. Thirion, S. Mériaux, A. Jobert, J. Serres et al., Fast reproducible identification and large-scale databasing of individual functional cognitive networks, BMC Neuroscience, vol.8, issue.1, p.91, 2007.
DOI : 10.1186/1471-2202-8-91
URL : https://hal.archives-ouvertes.fr/hal-00784462

T. T. Liu, L. R. Frank, E. C. Wong, and R. B. Buxton, Detection Power, Estimation Efficiency, and Predictability in Event-Related fMRI, NeuroImage, vol.13, issue.4, pp.759-773, 2001.
DOI : 10.1006/nimg.2000.0728

O. Gruber, P. Indefrey, H. Steinmetz, and A. Kleinschmidt, Dissociating Neural Correlates of Cognitive Components in Mental Calculation, Cerebral Cortex, vol.11, issue.4, pp.350-359, 2001.
DOI : 10.1093/cercor/11.4.350

A. Gelman and D. B. Rubin, Inference from Iterative Simulation Using Multiple Sequences, Statistical Science, vol.7, issue.4, pp.457-472, 1992.
DOI : 10.1214/ss/1177011136

P. Carbonetto and M. Stephens, Scalable Variational Inference for Bayesian Variable Selection in Regression, and Its Accuracy in Genetic Association Studies, Bayesian Analysis, vol.7, issue.1, pp.73-108, 2012.
DOI : 10.1214/12-BA703

A. Nummenmaa, T. Auranen, M. S. Hämäläinen, I. P. Jääskeläinen, J. Lampinen et al., Hierarchical Bayesian estimates of distributed MEG sources: Theoretical aspects and comparison of variational and MCMC methods, NeuroImage, vol.35, issue.2, pp.669-685, 2007.
DOI : 10.1016/j.neuroimage.2006.05.001

P. Ciuciu, S. Sockeel, T. Vincent, and J. Idier, Modelling the neurovascular habituation effect on fMRI time series, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.433-436, 2009.
DOI : 10.1109/ICASSP.2009.4959613

A. Tucholka, B. Thirion, M. Perrot, P. Pinel, J. Mangin et al., Probabilistic Anatomo-Functional Parcellation of the Cortex: How Many Regions?, 11thProc. MICCAI, 2008.
DOI : 10.1007/978-3-540-85990-1_48
URL : https://hal.archives-ouvertes.fr/inria-00502805

T. Vincent, P. Ciuciu, and B. Thirion, Sensitivity analysis of parcellation in the joint detection-estimation of brain activity in fMRI, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.568-571, 2008.
DOI : 10.1109/ISBI.2008.4541059

F. Forbes and N. Peyrard, Hidden markov random field model selection criteria based on mean field-like approximations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.9, pp.1089-1101, 2003.
DOI : 10.1109/TPAMI.2003.1227985