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

Volumn 65, Issue 3, 2011, Pages 325-338

  Risser, Laurent ^a,b,c,d   Vincent, Thomas ^a,b   Forbes, Florence ^e   Idier, Jérôme ^c   Ciuciu, Philippe ^a,b  

^a DIF   (France)

^b IFR 49   (France)

^c INSTITUT DE RECHERCHE EN COMMUNICATIONS ET CYBERNÉTIQUE DE NANTES   (France)

^d IMPERIAL COLLEGE LONDON   (United Kingdom)

^e INRIA RHÔNE ALPES   (France)

Author keywords

Bayesian inference; Detection estimation; fMRI; Markov random field; MCMC; Partition function; Potts fields

Indexed keywords

BAYESIAN INFERENCE; DETECTION-ESTIMATION; FMRI; MARKOV RANDOM FIELDS; MCMC; PARTITION FUNCTION; POTTS FIELDS;

APPROXIMATION THEORY; BAYESIAN NETWORKS; ESTIMATION; EXTRAPOLATION; INFERENCE ENGINES; MAGNETIC RESONANCE IMAGING; SIGNAL PROCESSING; THREE DIMENSIONAL;

BRAIN;

EID: 81855196126     PISSN: 19398018     EISSN: 19398115     Source Type: Journal    
DOI: 10.1007/s11265-010-0505-6     Document Type: Article

References (21)

1. Risser, L., Vincent, T., Ciuciu, P., & Idier, J. (2009). Robust extrapolation scheme for fast estimation of 3D Ising field partition functions. Application to within subject fMRI data analysis. In 12th Proc. MICCAI, LNCS (Vol. 5761, pp. 975-983). London, UK.
2. T Vincent L Risser P Ciuciu 2010 Spatially adaptive mixture modeling for analysis of fmri time series IEEE Transactions on Medical Imaging 29 4 1059 1074 10.1109/TMI.2010.2042064
3. B Thirion G Flandin P Pinel A Roche P Ciuciu J-B Poline 2006 Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets Human Brain Mapping 27 8 678 693 10.1002/hbm.20210 (Pubitemid 44182456)
4. Hammersley, J. M., & Clifford, P. (1990). Markov random fields in statistics. In G. R. Grimmett & D. J. A. Welsh (Eds.), Disorder in physical systems: A volume in honour of John M. Hammersley (pp. 19-32). Oxford University Press.
5. XL Meng WH Wong 1996 Simulating ratios of normalizing constants via a simple identity: A theoretical exploration Statistica Sinica 6 831 860 1422406 0857.62017
6. A Gelman X-L Meng 1998 Simulating normalizing constants: From importance sampling to bridge sampling to path sampling Statistical Science 13 163 185 1647507 0966.65004 10.1214/ss/1028905934 (Pubitemid 128466008)
7. M Jerrum A Sinclair 1993 Polynomial-time approximation algorithms for the Ising model SIAM Journal on Computing 22 1087 1116 1237164 0782.05076 10.1137/0222066
8. Trillon, A., Idier, J., & Peureux, P. (2008). Unsupervised Bayesian 3D reconstruction for non-destructive evaluation using gammagraphy. In EUSIPCO 16th European Signal Processing Conference. Lausanne, Switzerland.
9. F Forbes N Peyrard 2003 Hidden Markov random field model selection criteria based on mean field-like approximations IEEE Transactions on Pattern Analysis and Machine Intelligence 25 9 1089 1101 10.1109/TPAMI.2003.1227985
10. RH Swendsen JS Wang 1987 Nonuniversal critical dynamics in Monte Carlo simulations Physical Review Letters 58 86 88 10.1103/PhysRevLett.58.86
11. Risser, L., Idier, J., Ciuciu, P., & Vincent, T. (2009). Fast bilinear extrapolation of 3D ISING field partition function. Application to fMRI image analysis. In Proc. IEEE ICIP. Cairo, Egypt.
12. L Onsager 1944 A two-dimensional model with an order-disorder transition Physical Review 65 3&4 117 149 10315 0060.46001 10.1103/PhysRev.65.117
13. DM Higdon JE Bowsher VE Johnson TG Turkington DR Gilland RJ Jaszczak 1997 Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data IEEE Transactions on Medical Imaging 16 5 516 526 10.1109/42.640741 (Pubitemid 127767630)
14. Vincent, T., Ciuciu, P., & Idier, J. (2007). Spatial mixture modelling for the joint detection-estimation of brain activity in fMRI. In 32th Proc. IEEE ICASSP (Vol. I, pp. 325-328). Honolulu, Hawaii.
15. S Makni J Idier T Vincent B Thirion G Dehaene-Lambertz P Ciuciu 2008 A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI NeuroImage 41 3 941 969 10.1016/j.neuroimage.2008.02.017
16. P Ciuciu J-B Poline G Marrelec J Idier Ch Pallier H Benali 2003 Unsupervised robust non-parametric estimation of the hemodynamic response function for any fMRI experiment IEEE Transactions on Medical Imaging 22 10 1235 1251 10.1109/TMI.2003.817759
17. DM Higdon 1998 Auxiliary variable methods for Markov chain Monte Carlo with applications Journal of the American Statistical Association 93 442 585 595 0953.62103 10.2307/2670110
18. PJ Green S Richardson 2002 Hidden Markov models and desease mapping Journal of the American Statistical Association 97 460 1 16 1951259 10.1198/016214502388618870 (Pubitemid 36136565)
19. M Smith B Pütz D Auer L Fahrmeir 2003 Assessing brain activity through spatial Bayesian variable selection NeuroImage 20 802 815 10.1016/S1053-8119(03)00360-4 (Pubitemid 37268105)
20. D Smith M Smith 2006 Estimation of binary Markov random fields using Markov Chain Monte Carlo Journal of Computational and Graphical Statistics 15 1 207 227 2252462 10.1198/106186006X97817 (Pubitemid 43514418)
21. Chandler, D. (1987). Introduction to modern statistical mechanics (1st Ed). USA: Oxford University Press.