M. Abramowitz and I. A. Stegun, Handbook of mathematical functions. Dover publications, 1970.

G. K. Aguirre, E. Zarahn, and M. Esposito, The Variability of Human, BOLD Hemodynamic Responses, NeuroImage, vol.8, issue.4, p.574, 1998.
DOI : 10.1006/nimg.1998.0369

A. Andrade, F. Kherif, J. Mangin, K. Worsley, A. Paradis et al., Detection of fMRI activation using Cortical Surface Mapping, Human Brain Mapping, vol.4, issue.2, pp.79-93, 2001.
DOI : 10.1002/1097-0193(200102)12:2<79::AID-HBM1005>3.0.CO;2-I

URL : https://hal.archives-ouvertes.fr/hal-00349824

A. P. Bagshaw, C. Hawco, C. Bénar, E. Kobayashi, Y. Aghakhani et al., Analysis of the EEG???fMRI response to prolonged bursts of interictal epileptiform activity, NeuroImage, vol.24, issue.4, pp.1099-1112, 2005.
DOI : 10.1016/j.neuroimage.2004.10.010

C. Bénar, C. Grova, E. Kobayashi, A. P. Bagshaw, Y. Aghakhani et al., EEG???fMRI of epileptic spikes: Concordance with EEG source localization and intracranial EEG, NeuroImage, vol.30, issue.4, pp.1161-1170, 2006.
DOI : 10.1016/j.neuroimage.2005.11.008

D. Benboudjema and W. Pieczynski, Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.8, pp.1367-1378, 2007.
DOI : 10.1109/TPAMI.2007.1059

URL : https://hal.archives-ouvertes.fr/hal-01347974

R. 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

S. Chib, Marginal Likelihood from the Gibbs Output, Journal of the American Statistical Association, vol.92, issue.432, pp.1313-1321, 1995.
DOI : 10.1080/01621459.1995.10476591

S. Chib and I. Jeliazkov, Marginal Likelihood From the Metropolis???Hastings Output, Journal of the American Statistical Association, vol.96, issue.453, pp.270-281, 2001.
DOI : 10.1198/016214501750332848

P. Ciuciu, J. Idier, and S. Makni, Modeling non-linear and nonstationary effects of the BOLD response using mixture models in fMRI, Proc. 12th HBM CD-Rom, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00408606

P. Ciuciu, J. Idier, A. Roche, and C. Pallier, Outlier detection for robust region-based estimation of the hemodynamic response function in event-related fMRI, 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), pp.392-395, 2004.
DOI : 10.1109/ISBI.2004.1398557

P. Ciuciu, J. Idier, T. Veit, and T. Vincent, Application du rééchantillonnage stochastique de l'´ echelle en détection-estimation de l'activité cérébrale par IRMf, Actes du 21 e colloque GRETSI, pp.373-376, 2007.

P. Ciuciu, J. Poline, G. Marrelec, J. Idier, C. Pallier et al., Unsupervised robust non-parametric estimation of the hemodynamic response function for any fMRI experiment, IEEE Trans. Med. Imag, issue.10, pp.22-1235, 2003.

M. S. Cohen, Parametric Analysis of fMRI Data Using Linear Systems Methods, NeuroImage, vol.6, issue.2, pp.93-103, 1997.
DOI : 10.1006/nimg.1997.0278

G. Dehaene-lambertz, S. Dehaene, J. Anton, A. Campagne, P. Ciuciu et al., Functional segregation of cortical language areas by sentence repetition, Human Brain Mapping, vol.256, issue.5, pp.360-371, 2006.
DOI : 10.1002/hbm.20250

URL : https://hal.archives-ouvertes.fr/hal-00349690

L. Devroye, Non-Uniform Random Variate Generation, 1986.
DOI : 10.1007/978-1-4613-8643-8

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.333.8896

I. Drobnjak, D. Gavaghan, E. Pitt-francis, J. , M. et al., Development of a functional magnetic resonance imaging simulator for modeling realistic rigid-body motion artifacts, Magnetic Resonance in Medicine, vol.50, issue.2, pp.364-380, 2006.
DOI : 10.1002/mrm.20939

B. S. Everitt and E. T. Bullmore, Mixture model mapping of brain activation in functional magnetic resonance images, Human Brain Mapping, vol.7, issue.1, pp.1-14, 1999.
DOI : 10.1002/(SICI)1097-0193(1999)7:1<1::AID-HBM1>3.3.CO;2-8

G. Flandin, F. Kherif, X. Pennec, G. Malandain, N. Ayache et al., Improved detection sensitivity of functional MRI data using a brain parcellation technique, Proc. 5th MICCAI, pp.467-474, 2002.

G. Flandin and W. D. Penny, Bayesian fMRI data analysis with sparse spatial basis function priors, NeuroImage, vol.34, issue.3, pp.1108-1125, 2007.
DOI : 10.1016/j.neuroimage.2006.10.005

K. Friston, R. Thatcher, M. Hallet, T. Zeffiro, E. John et al., Statistical parametric mapping, Functional Neuroimaging : Technical Foundations, pp.79-93, 1994.
DOI : 10.1007/978-1-4615-1079-6_16

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.8.3745

K. Friston, Imaging neuroscience: Principles or maps?, Proc. Natl. Acad. Sci. USA 95, pp.796-802, 1998.
DOI : 10.1073/pnas.95.3.796

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC33800/pdf

A. Gelfand, A. Smith, and T. Lee, Bayesian Analysis of Constrained Parameter and Truncated Data Problems Using Gibbs Sampling, Journal of the American Statistical Association, vol.47, issue.418, pp.533-532, 1992.
DOI : 10.1093/biomet/60.2.319

S. Geman and D. Mcclure, Statistical methods for tomographic image reconstruction, Proceedings of the 46th Session of the ici, pp.5-21, 1987.

C. Genovese, A Bayesian Time-Course Model for Functional Magnetic Resonance Imaging Data, Journal of the American Statistical Association, vol.55, issue.451, pp.691-719, 2000.
DOI : 10.1006/nimg.1995.1023

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

C. Gössl, D. P. Auer, and L. Fahrmeir, Bayesian Spatiotemporal Inference in Functional Magnetic Resonance Imaging, Biometrics, vol.22, issue.2, pp.554-562, 2001.
DOI : 10.1111/j.0006-341X.2001.00554.x

C. Goutte, F. A. Nielsen, and L. K. Hansen, Modeling the haemodynamic response in fMRI using smooth filters, IEEE Trans. Med, 2000.

I. Gradshteyn and I. Ryzhik, Table of Integrals, Series, and Products, Sixth Avenue, 1250.

P. J. Green, Bayesian reconstructions from emission tomography data using a modified EM algorithm, IEEE Transactions on Medical Imaging, vol.9, issue.1, pp.84-93, 1990.
DOI : 10.1109/42.52985

P. J. Green, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995.
DOI : 10.1093/biomet/82.4.711

K. Grill-spector and . Malach, fmri-adaptation: a tool for studying the functional properties of human cortical neurons, Acta Psychol (Amst), vol.107, pp.1-3, 2001.

K. Grill-spector, R. Sayres, and D. Ress, High-resolution imaging reveals highly selective nonface clusters in the fusiform face area, Nature Neuroscience, vol.19, issue.9, pp.1177-1185, 2006.
DOI : 10.1073/pnas.95.5.2609

C. Grova, S. Makni, G. Flandin, P. Ciuciu, J. Gotman et al., Anatomically informed interpolation of fMRI data on the cortical surface, NeuroImage, vol.31, issue.4, pp.1475-1486, 2006.
DOI : 10.1016/j.neuroimage.2006.02.049

D. A. Handwerker, J. M. Ollinger, and M. 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

W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, p.97, 1970.
DOI : 10.1093/biomet/57.1.97

R. Henson, C. Price, M. Rugg, R. Turner, and K. Friston, Detecting Latency Differences in Event-Related BOLD Responses: Application to Words versus Nonwords and Initial versus Repeated Face Presentations, NeuroImage, vol.15, issue.1, pp.83-97, 2002.
DOI : 10.1006/nimg.2001.0940

R. E. Kass and A. E. Raftery, Bayes Factors, Journal of the American Statistical Association, vol.2, issue.430, pp.773-795, 1995.
DOI : 10.1080/01621459.1995.10476572

S. M. Kay, Modern Spectral Estimation, 1988.

F. Kruggel and D. Y. Von-crammon, Modeling the hemodynamic response in single-trial functional MRI experiments, Magnetic Resonance in Medicine, vol.6, issue.4, pp.787-797, 1999.
DOI : 10.1002/(SICI)1522-2594(199910)42:4<787::AID-MRM22>3.0.CO;2-V

N. Lange, Empirical and substantive models, the Bayesian paradigm, and meta-analysis in functional brain imaging, Human Brain Mapping, vol.1, issue.4, pp.259-263, 1997.
DOI : 10.1002/(SICI)1097-0193(1997)5:4<259::AID-HBM10>3.0.CO;2-9

J. Liu, Monte Carlo strategies in scientific computing. Springer series in Statistics, 2001.

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, P. Ciuciu, J. Idier, and J. Poline, Bayesian Joint Detection-Estimation of Brain Activity Using MCMC With a Gamma-Gaussian Mixture Prior Model, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, pp.1093-1096, 2006.
DOI : 10.1109/ICASSP.2006.1661470

URL : https://hal.archives-ouvertes.fr/hal-00408603

J. Mangin, V. Frouin, I. Bloch, J. Régis, and J. López-krahe, From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations, Journal of Mathematical Imaging and Vision, vol.44, issue.6, pp.297-318, 1995.
DOI : 10.1007/BF01250286

G. Marrelec, H. Benali, P. Ciuciu, M. Pélégrini-issac, and J. Poline, Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information, Human Brain Mapping, vol.2, issue.1, pp.1-17, 2003.
DOI : 10.1002/hbm.10100

URL : https://hal.archives-ouvertes.fr/cea-00333748

G. Marrelec, P. Ciuciu, M. Pélégrini-issac, and H. Benali, Estimation of the Hemodynamic Response in Event-Related Functional MRI: Bayesian Networks as a Framework for Efficient Bayesian Modeling and Inference, IEEE Transactions on Medical Imaging, vol.23, issue.8, pp.959-967, 2004.
DOI : 10.1109/TMI.2004.831221

URL : https://hal.archives-ouvertes.fr/cea-00333687

V. Mazet, D. Brie, and J. Idier, Simulation of positive normal variables using several proposal distributions, IEEE workshop on statistical signal processing, 2005.

F. M. Miezin, L. Maccotta, J. M. Ollinger, S. E. Petersen, and R. L. Buckner, Characterizing the Hemodynamic Response: Effects of Presentation Rate, Sampling Procedure, and the Possibility of Ordering Brain Activity Based on Relative Timing, NeuroImage, vol.11, issue.6, pp.735-759, 2000.
DOI : 10.1006/nimg.2000.0568

S. Moussaoui, D. Brie, A. Mohammad-djafari, and C. Carteret, Separation of Non-Negative Mixture of Non-Negative Sources Using a Bayesian Approach and MCMC Sampling, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4133-4145, 2006.
DOI : 10.1109/TSP.2006.880310

URL : https://hal.archives-ouvertes.fr/hal-00121602

L. Naccache and S. Dehaene, Unconscious semantic priming extends to novel unseen stimuli, Cognition, vol.80, issue.3, pp.215-229, 2001.
DOI : 10.1016/S0010-0277(00)00139-6

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.1580

J. Neumann and G. Lohmann, Bayesian second-level analysis of functional magnetic resonance images, NeuroImage, vol.20, issue.2, pp.1346-1355, 2003.
DOI : 10.1016/S1053-8119(03)00443-9

F. A. Nielsen, L. K. Hansen, P. Toft, C. Goutte, N. Lange et al., Comparison of two convolution models for fMRI time series, Neuroimage, vol.5, p.473, 1997.

A. Nieto-castanon, S. Ghosh, J. Tourville, and F. Guenther, Region of interest based analysis of functional imaging data, NeuroImage, vol.19, issue.4, pp.1303-1316, 2003.
DOI : 10.1016/S1053-8119(03)00188-5

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

W. Ou and P. Golland, From Spatial Regularization to Anatomical Priors in fMRI Analysis, 2005.
DOI : 10.1007/11505730_8

W. Penny and K. Friston, Mixtures of general linear models for functional neuroimaging, IEEE Transactions on Medical Imaging, vol.22, issue.4, pp.504-514, 2003.
DOI : 10.1109/TMI.2003.809140

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

W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical Recipes in C -The Art of Scientific Computing, pp.699-706, 1992.

A. E. Raftery, M. A. Newton, J. M. Satagopan, and P. N. Krivitsky, Estimating the integrated likelihood via posterior simulation using the harmonic mean identity, Bayesian statistics 8, pp.1-45, 2007.

J. C. Rajapakse, F. Kruggel, J. M. Maisog, and D. Von-cramon, Modeling hemodynamic response for analysis of functional MRI time-series, Human Brain Mapping, vol.2, issue.4, pp.283-300, 1998.
DOI : 10.1002/(SICI)1097-0193(1998)6:4<283::AID-HBM7>3.0.CO;2-#

S. Richardson and P. J. Green, On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion), Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.59, issue.4, pp.731-792, 1997.
DOI : 10.1111/1467-9868.00095

C. Robert, Simulation of truncated normal variables, Statistics and Computing, vol.82, issue.2, pp.121-125, 1995.
DOI : 10.1007/BF00143942

URL : https://hal.archives-ouvertes.fr/hal-00431310

C. P. Robert, The Bayesian Choice. Second Edition, 2001.
DOI : 10.1007/978-1-4757-4314-2

S. J. Roberts and W. D. Penny, Variational Bayes for generalized autoregressive models, IEEE Transactions on Signal Processing, vol.50, issue.9, pp.2245-2257, 2002.
DOI : 10.1109/TSP.2002.801921

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.380.3217

M. Smith, B. Pütz, D. Auer, and L. Fahrmeir, Assessing brain activity through spatial bayesian variable selection, NeuroImage, vol.20, issue.2, pp.802-815, 2003.
DOI : 10.1016/S1053-8119(03)00360-4

URL : https://epub.ub.uni-muenchen.de/1697/1/paper_316.pdf

M. Svensen, F. Kruggel, and D. Von-crammon, Probabilistic modeling of single-trial fMRI data, IEEE Transactions on Medical Imaging, vol.19, issue.1, pp.19-35, 2000.
DOI : 10.1109/42.832957

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

B. Thirion, P. Pinel, A. Tucholka, A. Roche, P. Ciuciu et al., Structural Analysis of fMRI Data Revisited: Improving the Sensitivity and Reliability of fMRI Group Studies, IEEE Transactions on Medical Imaging, vol.26, issue.9, pp.1256-1269, 2007.
DOI : 10.1109/TMI.2007.903226

URL : https://hal.archives-ouvertes.fr/cea-00333747

B. Thyreau, B. Thirion, G. Flandin, and J. Poline, Anatomofunctional description of the brain: a probabilistic approach, Proc. 31th Proc. IEEE ICASSP, pp.1109-1112, 2006.

V. Hartvig, N. Jensen, and J. , Spatial mixture modeling of fMRI data, Human Brain Mapping, vol.4, issue.4, pp.233-248, 2000.
DOI : 10.1002/1097-0193(200012)11:4<233::AID-HBM10>3.0.CO;2-F

T. Veit and J. Idier, Rééchantillonnage de l'´ echelle dans les algorithmes MCMC pour lesprobì emes inverses bilinéaires, Actes du 21 e colloque GRETSI, pp.1233-1236, 2007.

T. Vincent, P. Ciuciu, and J. Idier, Application and validation of spatial mixture modelling for the joint detection-estimation of brain activity in fMRI, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.5218-5222, 2007.
DOI : 10.1109/IEMBS.2007.4353518

URL : https://hal.archives-ouvertes.fr/cea-00333639

T. Vincent, P. Ciuciu, and J. Idier, Spatial Mixture Modelling for the Joint Detection-Estimation of Brain Activity in fMRI, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, pp.325-328, 2007.
DOI : 10.1109/ICASSP.2007.366682

URL : https://hal.archives-ouvertes.fr/hal-00408628

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

M. Woolrich, T. Behrens, C. Beckmann, and S. Smith, Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data, IEEE Transactions on Medical Imaging, vol.24, issue.1, pp.1-11, 2005.
DOI : 10.1109/TMI.2004.836545

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

M. Woolrich, M. Jenkinson, J. M. Brady, and S. Smith, Constrained linear basis sets for HRF modelling using Variational Bayes, NeuroImage, vol.21, issue.4, pp.1748-1761, 2004.
DOI : 10.1016/j.neuroimage.2003.12.024

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

K. Worsley, C. Liao, J. Aston, V. Petre, G. Duncan et al., A General Statistical Analysis for fMRI Data, NeuroImage, vol.15, issue.1, pp.1-15, 2002.
DOI : 10.1006/nimg.2001.0933