M. Albughdadi, L. Chaari, F. Forbes, J. Tourneret, and P. Ciuciu, Model selection for hemodynamic brain parcellation in fMRI, Proc. EUSIPCO. Lisbon, Portugal, pp.31-35, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01107475

H. Attias, A variational Bayesian framework for graphical models, Proc. Advances in Neural Information Processing Systems 12, 2000.

S. Badillo, G. Varoquaux, and P. Ciuciu, Hemodynamic Estimation Based on Consensus Clustering, 2013 International Workshop on Pattern Recognition in Neuroimaging, pp.211-215, 2013.
DOI : 10.1109/PRNI.2013.61
URL : https://hal.archives-ouvertes.fr/hal-00854621

S. Badillo, T. Vincent, and P. Ciuciu, Group-level impacts of within- and between-subject hemodynamic variability in fMRI, NeuroImage, vol.82, pp.433-448, 2013.
DOI : 10.1016/j.neuroimage.2013.05.100
URL : https://hal.archives-ouvertes.fr/hal-00854481

C. Bakhous, Modèles d'encodage parcimonieux de l'activité cérébrale mesurée par irm fonctionnelle, 2013.

M. J. Beal, Variational algorithms for approximate bayesian inference, 2003.

J. Besag, Spatial interaction and the statistical analysis of lattice systems, Journal of the Royal Statistical Society. Series B (Methodological), pp.192-236, 1974.

D. M. Blei and M. I. Jordan, Variational inference for Dirichlet process mixtures, Bayesian Analysis, vol.1, issue.1, pp.121-143, 2006.
DOI : 10.1214/06-BA104

G. M. Boynton, S. A. Engel, G. H. Glover, and D. J. Heeger, Linear systems analysis of functional magnetic resonance imaging in human v1, The Journal of Neuroscience, vol.16, issue.13, pp.4207-4221, 1996.

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

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

L. Chaari, S. Badillo, T. Vincent, G. Dehaene-lambertz, F. Forbes et al., Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework, 2015.
DOI : 10.1007/978-3-642-33454-2_23
URL : https://hal.archives-ouvertes.fr/hal-00859388

L. Chaari, P. Ciuciu, S. Mériaux, and J. Pesquet, Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI, MAGMA)DOI: 10.1007, pp.10334-10348, 2014.
DOI : 10.1007/s10334-014-0436-5
URL : https://hal.archives-ouvertes.fr/hal-01084324

L. Chaari, F. Forbes, T. Vincent, and P. Ciuciu, Hemodynamic-informed parcellation of fMRI data in a variational joint detection estimation framework, Medical Image Computing and Computer- Assisted Intervention, pp.180-188, 2012.

L. Chaari, F. Forbes, T. Vincent, M. Dojat, and P. Ciuciu, Variational Solution to the Joint Detection Estimation of Brain Activity in fMRI, Medical Image Computing and Computer-Assisted Intervention, pp.260-268, 2011.
DOI : 10.1109/TMI.2006.880682
URL : https://hal.archives-ouvertes.fr/inserm-00635384

L. Chaari, T. Vincent, F. Forbes, M. Dojat, and P. Ciuciu, Fast Joint Detection-Estimation of Evoked Brain Activity in Event-Related fMRI Using a Variational Approach, IEEE Transactions on Medical Imaging, vol.32, issue.5, pp.821-837, 2013.
DOI : 10.1109/TMI.2012.2225636
URL : https://hal.archives-ouvertes.fr/inserm-00753873

S. P. Chatzis and G. Tsechpenakis, The Infinite Hidden Markov Random Field Model, IEEE Transactions on Neural Networks, vol.21, issue.6, pp.1004-1014, 2010.
DOI : 10.1109/TNN.2010.2046910

D. M. Chickering and D. Heckerman, Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables, Machine Learning, vol.29, issue.2/3, pp.181-212, 1997.
DOI : 10.1023/A:1007469629108

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. Poline, G. Marrelec, J. Idier, C. Pallier 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

T. Deneux and O. Faugeras, Using nonlinear models in fMRI data analysis: Model selection and activation detection, NeuroImage, vol.32, issue.4, pp.1669-1689, 2006.
DOI : 10.1016/j.neuroimage.2006.03.006

S. Eickhoff, D. Bzdok, A. Laird, C. Roski, S. Caspers et al., Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation, NeuroImage, vol.57, issue.3, pp.938-949, 2011.
DOI : 10.1016/j.neuroimage.2011.05.021

M. D. Escobar and M. West, Bayesian Density Estimation and Inference Using Mixtures, Journal of the American Statistical Association, vol.55, issue.430, pp.577-588, 1995.
DOI : 10.1080/01621459.1987.10478458

T. S. Ferguson, A Bayesian analysis of some nonparametric problems. The Annals of Statistics, pp.209-230, 1973.

G. Flandin, F. Kherif, X. Pennec, D. Rivì-ere, N. Ayache et al., A new representation of fMRI data using anatomo-functional constraints, Proc. Neuroimage (HBM'02, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00615928

A. Fouque, P. Ciuciu, and L. Risser, Multivariate Spatial Gaussian Mixture Modeling for statistical clustering of hemodynamic parameters in functional MRI, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.445-448, 2009.
DOI : 10.1109/ICASSP.2009.4959616

A. Frau-pascual, T. Vincent, F. Forbes, and P. Ciuciu, Hemodynamically informed parcellation of cerebral FMRI data, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2079-2083, 2014.
DOI : 10.1109/ICASSP.2014.6853965
URL : https://hal.archives-ouvertes.fr/hal-01100186

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

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

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

P. Golland, Y. Golland, and R. Malach, Detection of Spatial Activation Patterns as Unsupervised Segmentation of fMRI Data, Medical Image Computing and Computer-Assisted Intervention, pp.110-118, 2007.
DOI : 10.1007/978-3-540-75757-3_14

D. Handwerker, J. Gonzalez-castillo, M. Esposito, and P. Bandettini, The continuing challenge of understanding and modeling hemodynamic variation in fMRI, NeuroImage, vol.62, issue.2, pp.1017-1023, 2012.
DOI : 10.1016/j.neuroimage.2012.02.015

D. A. Handwerker, J. Ollinger, and M. Esposito, Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses 21, pp.1639-1651, 2004.

R. N. Henson, M. D. Rugg, and K. J. Friston, The choice of basis functions in event-related fMRI, NeuroImage, vol.13, issue.6, p.149, 2001.
DOI : 10.1016/S1053-8119(01)91492-2

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.1138-1153, 1999.
DOI : 10.1109/42.819324

D. Lashkari, R. Sridharan, E. Vul, P. 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

D. Lashkari, E. Vul, N. Kanwisher, and P. Golland, Discovering structure in the space of fMRI selectivity profiles, NeuroImage, vol.50, issue.3, pp.1085-1098, 2010.
DOI : 10.1016/j.neuroimage.2009.12.106

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

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

S. Ogawa, T. Lee, A. R. Kay, and D. W. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation., Proc. Natl. Acad. Sci, pp.9868-72, 1990.
DOI : 10.1073/pnas.87.24.9868

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

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

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, G. Varoquaux, E. Dohmatob, and J. Poline, Which fMRI clustering gives good brain parcellations? Frontiers in neuroscience 8, 2014.

T. Vincent, S. Badillo, L. Risser, L. Chaari, C. Bakhous et al., Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF, Frontiers in Neuroscience, vol.61, issue.67, 2014.
DOI : 10.1002/mrm.21902
URL : https://hal.archives-ouvertes.fr/hal-01084249

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

M. W. Woolrich, B. D. Ripley, M. Brady, and S. M. 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