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

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

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

L. Chaari, F. Forbes, T. Vincent, and P. Ciuciu, Adaptive hemodynamic-informed parcellation of fMRI data in a variational joint detection estimation framework, 15th Proc. MICCAIPart III), pp.180-188, 2012.

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

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

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

G. Flandin, F. Kherif, X. Pennec, D. Rivire, N. Ayache et al., A new representation of fMRI data using anatomo-functional constraints, Proc. 8th HBM, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00615928

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

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

]. S. Badillo, G. Varoquaux, and P. Ciuciu, Hemodynamic Estimation Based on Consensus Clustering, 2013 International Workshop on Pattern Recognition in Neuroimaging, 2013.
DOI : 10.1109/PRNI.2013.61

URL : https://hal.archives-ouvertes.fr/hal-00854621

K. Friston, P. Fletcher, O. Joseph, A. Holmes, M. Rugg et al., Event-Related fMRI: Characterizing Differential Responses, NeuroImage, vol.7, issue.1, pp.30-40, 1998.
DOI : 10.1006/nimg.1997.0306

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

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

D. Sepandar, D. Kamvar, C. D. Klein, and . Manning, Interpreting and extending classical agglomerative clustering algorithms using a model-based approach, ICML, 2002.

J. H. Ward, Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, vol.58, issue.301, pp.236-244, 1963.
DOI : 10.1007/BF02289263

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905