C. Bakhous, F. Forbes, T. Vincent, and P. Ciuciu, Sélection de variables dans un cadre bayésien de traitement de données d'IRM fonctionnelle, Journées de Statistique de la Société Française de Statistique (SFdS), 2012.

P. Ciuciu, Adaptive experimental condition selection in event-related fMRI, 9th Proc. IEEE ISBI, pp.1755-1758, 2012.
URL : https://hal.archives-ouvertes.fr/cea-00710489

C. Bakhous, F. Forbes, T. Vincent, M. Dojat, and P. Ciuciu, Variational variable selection to assess experimental condition relevance in event-related fMRI, 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.1500-1503, 0223.
DOI : 10.1109/ISBI.2013.6556821

URL : https://hal.archives-ouvertes.fr/hal-00859391

]. G. Bibliographie, E. Aguirre, M. D. Zarahn, and . Esposito, The variability of human BOLD hemodynamic responses, Neuroimage, vol.8, issue.4, pp.360-369, 1998.

]. J. Ashburner, K. J. Friston, and W. Penny, Human brain function, pp.43-44, 2003.

]. S. Badillo, Etude de la variabilité hémodynamique chez l'enfant et l'adulte sains en IRMf, p.160, 2013.

]. S. Badillo, T. Vincent, and P. Ciuciu, Group-level impacts of withinand between-subject hemodynamic variability in fMRI, p.134, 2013.

C. Bakhous, F. Forbes, T. Vincent, L. Chaari, M. Dojat et al., Sélection de variables dans un cadre bayésien de traitement de données d'IRM fonctionnelle, Journées de Statistique de la Société Française de Statistique (SFdS), 2012.

]. C. Bakhous, F. Forbes, T. Vincent, L. Chaari, M. Dojat et al., Adaptive experimental condition selection in event-related fMRI, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.1755-1758, 2012.
DOI : 10.1109/ISBI.2012.6235920

URL : https://hal.archives-ouvertes.fr/cea-00710489

]. C. Bakhous-2013a, F. Bakhous, F. Forbes, T. Enikeeva, M. Vincent et al., Analyse parcimonieuse des données d'IRM fonctionnelle dans un cadre bayésien variationnel, Journées de Statistique de la Société Française de Statistique (SFdS), 2013.

]. C. Bakhous, F. Forbes, T. Vincent, M. Dojat, and P. Ciuciu, Variational variable selection to assess experimental condition relevance in event-related fMRI, 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.1500-1503, 2013.
DOI : 10.1109/ISBI.2013.6556821

URL : https://hal.archives-ouvertes.fr/hal-00859391

]. P. Bandettini, A. Jesmanowicz, E. C. Wong, and J. S. Hyde, Processing strategies for time-course data sets in functional mri of the human brain, Magnetic Resonance in Medicine, vol.2, issue.2, pp.161-173, 1993.
DOI : 10.1002/mrm.1910300204

P. R. Bannister, J. M. Brady, and M. Jenkinson, TIGER ??? A New Model for Spatio-temporal Realignment of FMRI Data, Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, pp.292-303, 2004.
DOI : 10.1007/978-3-540-27816-0_25

]. Y. Benjamini and Y. Hochberg, Controlling the false discovery rate : a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society, vol.57, issue.1, pp.289-300, 1995.

]. Y. Benjamini and D. Yekutieli, The control of the false discovery rate in multiple testing under dependency, The Annals of Statistics, vol.29, issue.4, pp.1165-1188, 2001.

]. 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, 0144.
DOI : 10.1002/mrm.1910340409

]. G. 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.

]. R. Buckner, J. Goodman, M. Burock, M. Rotte, W. Koutstaal et al., Functional-Anatomic Correlates of Object Priming in Humans Revealed by Rapid Presentation Event-Related fMRI, Neuron, vol.20, issue.2, pp.285-296, 1998.
DOI : 10.1016/S0896-6273(00)80456-0

]. 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

]. R. Buxton, E. C. Wong, . L. Frank, P. Friston, A. Fletcher et al., Dynamics of blood flow and oxygenation changes during brain activation: The balloon model, Magnetic Resonance in Medicine, vol.77, issue.6, pp.855-864, 1998.
DOI : 10.1002/mrm.1910390602

]. S. Geman and D. Geman, Stochastic Relaxation, Gibbs Distributions , and the Bayesian Restoration of Images, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.6, issue.6, pp.721-741, 0220.

]. G. 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. 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, 1952.
DOI : 10.1109/42.897811

]. D. 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

]. W. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109, 0215.
DOI : 10.1093/biomet/57.1.97

]. Y. Ji, Data-driven fMRI data analysis based on parcellation, p.61, 2010.

]. O. Joseph and R. N. Henson, Event-related functional magnetic resonance imaging: modelling, inference and optimization, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.354, issue.1387, pp.1215-1228, 1999.
DOI : 10.1098/rstb.1999.0475

]. A. Knops-2009, B. Knops, E. M. Thirion, V. Hubbard, S. Michel et al., Recruitment of an Area Involved in Eye Movements During Mental Arithmetic, Science, vol.324, issue.5934, pp.1583-1588, 2009.
DOI : 10.1126/science.1171599

]. M. 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.187-98, 2009.
DOI : 10.1016/j.neuroimage.2008.10.065

]. H. Luo and S. Puthusserypady, A Sparse Bayesian Method for Determination of Flexible Design Matrix for fMRI Data Analysis and systems?I : regular papers, IEEE Trans. on Circuits, vol.52, issue.66, pp.2699-2706, 2005.

]. 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, Détection-estimation conjointe de l'activité cérébrale en IRMf, p.70, 1954.

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

]. J. Mandeville, J. J. Marota, C. Ayata, C. Zaharchuck, M. A. Moskowitz et al., Evidence of a cerebrovascular post-arteriole Windkessel with delayed compliance, J. Cereb. Blood Flow Metab, vol.19, pp.679-689, 1999.

]. G. Marrelec, H. Benali, P. Ciuciu, and J. Poline, Bayesian estimation of the hemodynamic response function in Functional MRI MaxEnt Workshops, Bayesian Inference and Maximum Entropy Methods, p.52, 2001.

. Poline, Robust Bayesian estimation of the hemodynamic response function in event-related BOLD MRI using basic physiological information, Hum. Brain Mapp, vol.19, issue.1, pp.1-17, 1952.

]. N. Metropolis-1949, S. Metropolis, and . Ulam, The Monte Carlo Method, Journal of the American Statistical Association, vol.44, issue.247, pp.335-341, 1949.
DOI : 10.1080/01621459.1949.10483310

]. N. Metropolis-1953, A. W. Metropolis, M. N. Rosenbluth, A. H. Rosenbluth, E. Teller et al., Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, vol.21, issue.6, pp.1087-1092, 0214.
DOI : 10.1063/1.1699114

]. F. 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. Ogawa, T. Lee, A. Kay, and D. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation., Proc. Natl. Acad, 1990.
DOI : 10.1073/pnas.87.24.9868

S. Ogawa, D. W. Tank, R. Menon, J. M. Ellermann, S. Kim et al., Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging., Proc. Natl. Acad. Sci. USA, pp.5951-5955, 1992.
DOI : 10.1073/pnas.89.13.5951

]. R. Ohara and M. J. Sillanpaa, A review of Bayesian variable selection methods: what, how and which, Bayesian Analysis, vol.4, issue.1, pp.85-118, 2009.
DOI : 10.1214/09-BA403SUPP

]. V. Oikonomou, K. Blekas, and L. Astrakas, A Sparse and Spatially Constrained Generative Regression Model for fMRI Data Analysis, IEEE Transactions on Biomedical Engineering, vol.59, issue.1, pp.58-67, 2012.
DOI : 10.1109/TBME.2010.2104321

]. T. Park and D. A. Van-dyk, Partially Collapsed Gibbs Samplers: Illustrations and Applications, Journal of Computational and Graphical Statistics, vol.18, issue.2, pp.283-305, 2009.
DOI : 10.1198/jcgs.2009.08108

]. W. 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

]. W. 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

]. M. Pereyra, N. Dobigeon, H. Batatia, and J. Tourneret, Estimating the Granularity Coefficient of a Potts-Markov Random Field Within a Markov Chain Monte Carlo Algorithm, IEEE Transactions on Image Processing, vol.22, issue.6, pp.2385-2397, 2013.
DOI : 10.1109/TIP.2013.2249076

]. M. Perrot, Reconnaissance Automatique des Sillons Corticaux, p.61, 2009.
URL : https://hal.archives-ouvertes.fr/tel-00457072

]. 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, 0126.
DOI : 10.1186/1471-2202-8-91

URL : https://hal.archives-ouvertes.fr/hal-00784462

]. K. Pruessmann, M. Weiger, M. W. Scheidegger, and P. Boesiger, SENSE: Sensitivity encoding for fast MRI, Magnetic Resonance in Medicine, vol.30, issue.5, pp.952-962, 0126.
DOI : 10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S

]. P. Purdon and R. M. Weisskoff, Effect of temporal autocorrelation due to physiological noise and stimulus paradigm on voxel-level false-positive rates in fMRI, Human Brain Mapping, vol.5, issue.4, pp.239-249, 1998.
DOI : 10.1002/(SICI)1097-0193(1998)6:4<239::AID-HBM4>3.0.CO;2-4

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. Rajapakse, F. Kruggel, J. M. Maisog, and D. Y. 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-#

]. B. Rosen, R. L. Buckner, and A. M. Dale, Event-related functional MRI: Past, present, and future, Proc. Natl. Acad. Sci. USA, pp.773-780, 1998.
DOI : 10.1073/pnas.95.3.773

]. D. Smith and L. Fahrmeir, Spatial Bayesian Variable Selection With Application to Functional Magnetic Resonance Imaging, Journal of the American Statistical Association, vol.102, issue.478, pp.417-431, 2007.
DOI : 10.1198/016214506000001031

]. J. Talairach and P. Tournoux, Co?Planar stereotaxic atlas of the human brain. 3-dimensional proportional system : An approach to cerebral imaging, p.33, 1988.

]. B. Thirion, G. Flandin, P. Pinel, and J. Poline, Spatial relaxation increases the sensitivity of random effects analyses : the benefit of intersubject parcellation, Proc. 11th HBMCD-RomNeuroimage vol, pp.12-16, 2005.

]. B. Thirion, S. Dodel, and J. Poline, Detection of signal synchronizations in resting-state fMRI datasets, NeuroImage, vol.29, issue.1, pp.321-327, 2006.
DOI : 10.1016/j.neuroimage.2005.06.054

]. 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.

]. M. Tipping, Sparse Bayesian learning and the relevance vector machine, J. Mach. Learn. Res, vol.1, pp.211-244, 2001.

]. A. Tucholka, B. Thirion, M. Perrot, P. Pinel, J. Mangin et al., Probabilistic Anatomo-Functional Parcellation of the Cortex: How Many Regions?, 11thProc. MICCAI, p.61, 2008.
DOI : 10.1007/978-3-540-85990-1_48

URL : https://hal.archives-ouvertes.fr/inria-00502805

]. Turner, How Much Cortex Can a Vein Drain? Downstream Dilution of Activation-Related Cerebral Blood Oxygenation Changes, NeuroImage, vol.16, issue.4, pp.1062-1067, 2002.
DOI : 10.1006/nimg.2002.1082

]. G. Varoquaux and R. C. Craddock, Learning and comparing functional connectomes across subjects, NeuroImage, vol.80, pp.405-415, 2013.
DOI : 10.1016/j.neuroimage.2013.04.007

URL : https://hal.archives-ouvertes.fr/hal-00812911

]. 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, 1951.
DOI : 10.1109/ICASSP.2007.366682

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

]. T. Vincent, Modèles hémodynamiques spatiaux adaptatifs pour l'imagerie cérébrale fonctionnelle, pp.61-71, 2010.

]. 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, 1951.
DOI : 10.1109/TMI.2010.2042064

URL : https://hal.archives-ouvertes.fr/cea-00470594

S. Smith, Spatio-temporal noise model selection in fMRI, Proc. 8th HBM, p.45, 2002.

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, 1944.
DOI : 10.1109/TMI.2004.836545

]. K. Worsley, C. H. Liao, J. Aston, V. Petre, G. H. 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