Bayesian spatiotemporal model of fMRI data

NeuroImage

Volumn 49, Issue 1, 2010, Pages 442-456

  Quirós, Alicia ^a   Diez, Raquel Montes ^a   Gamerman, Dani ^b  

^a UNIVERSIDAD REY JUAN CARLOS   (Spain)

^b FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

Activation inference; Bayesian analysis; fMRI; GMRF; MCMC; Spatiotemporal model

Indexed keywords

ACCURACY; ARTICLE; BAYES THEOREM; COMPUTER SIMULATION; CONTROLLED STUDY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HEMODYNAMICS; NEUROIMAGING; NORMAL DISTRIBUTION; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING;

ALGORITHMS; BAYES THEOREM; COMPUTER SIMULATION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MARKOV CHAINS; MODELS, STATISTICAL; NORMAL DISTRIBUTION;

EID: 70349971910     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2009.07.047     Document Type: Article

References (41)

1. Beckmann C.F., and Smith S.M. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imag. 23 2 (2004) 137-152
2. Besag J.E. Spatial interaction and the statistical analysis of lattice systems. J. R. Stat. Soc., Series B (Methodological) 36 2 (1974) 192-236
3. Bowman F.D. Spatiotemporal models for region of interest analysis of functional neuroimaging data. J. Am. Stat. Assoc. 102 478 (2007) 442-453
4. Bowman F.D., Caffo B.S., Bassett S.S., and Kilts C. A Bayesian hierarchical framework for spatial modeling of fMRI data. NeuroImage 39 1 (2008) 146-156
5. Boynton G.M., Engel S.A., Glover G.H., and Heeger D.J. Linear systems analysis of functional magnetic resonance imaging in human V1. J. Neurosci. 16 13 (1996) 4207-4221
6. Calhoun V.D., Adali T., McGinty V.B., Pekar J.J., Watson T.D., and Pearlson G.D. fMRI activation in a visual-perception task: network of areas detected using the general linear model and independent components analysis. NeuroImage 14 5 (2001) 1080-1088
7. de Pasquale F., Del Gratta C., and Romani G.L. Empirical Markov Chain Monte Carlo Bayesian analysis of fMRI data. NeuroImage 42 1 (2008) 99-111
8. Esposito F., Seifritz E., Formisano E., Morrone R., Scarabino T., Tedeschi G., Cirillo S., Goebel R., and Di Salle F. Real-time independent component analysis of fMRI time-series. NeuroImage 20 4 (2003) 2209-2224
9. Flandin G., and Penny W.D. Bayesian fMRI data analysis with sparse spatial basis function priors. NeuroImage 34 (2007) 1108-1125
10. Frackowiak R.S.J., Friston K.J., Frith C.D., Dolan R.J., Price C.J., Zeki S., Ashburner J.T., and Penny W.D. Human Brain Function (2004), Academic Press
11. Friston K.J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2 (1994) 56-78
12. Friston K.J., and Penny W. Posterior probability maps and SPMs. NeuroImage 19 (2003) 1240-1249
13. Friston K.J., Jezzard P., and Turner R. Analysis of functional MRI time-series. Hum. Brain Mapp. 1 2 (1994) 153-171
14. Friston K.J., Holmes A.P., Poline J.-B., Gransby P.J., Williams S.C.R., Frackowiak R.S.J., and Turner R. Analysis of fMRI time-series revisited. NeuroImage 2 1 (1995) 45-53
15. Friston K.J., Penny W., Phillips C., Kiebel S., Hinton G., and Ashburner J. Classical and Bayesian inference in neuroimaging: theory. NeuroImage 16 2 (2002) 465-483
16. Friston K.J., Ashburner J., Kiebel S., Nichols T., and Penny W. Statistical Parametric Mapping: The Analysis of Functional Brain Images (2006), Elsevier, London
17. Gamerman D., and Lopes H.F. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference (2006), CRC Press, London
18. Gelman A., and Rubin D.B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7 (1992) 457-472
19. Geweke J. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In: Bernardo J.M., Berger J.O., Dawid A.P., and Smith A. (Eds). Bayesian Statistics 4 (1992), Clarendon Press, Oxford, UK
20. Gilks W.R., Richardson S., and Spiegelhalter D.J. Markov Chain Monte Carlo in Practice (1996), Chapman & Hall/CRC
21. Gossl C., Auer D.P., and Fahrmeir L. A Bayesian approach for spatial connectivity in fMRI. Poster No. 483. Biometrics 57 (1999) 554-562
22. Gossl C., Auer D.P., and Fahrmeir L. Bayesian spatiotemporal inference in functional magnetic resonance imaging. Biometrics 57 2 (2001) 554-562
23. Goutte C., Toft P., Rostrup E., Nielsen F.A., and Hansen L.K. On clustering fMRI time series. NeuroImage 9 3 (1999) 298-310
24. Harrison L.M., Penny W., Ashburner J., Trujillo-Barreto N., and Friston K.J. Diffusion-based spatial priors for imaging. NeuroImage 38 4 (2007) 677-695
25. Hartvig N.V., and Jensen J.L. Spatial mixture modelling of fMRI data. Hum. Brain Mapp. 11 4 (2000) 233-248
26. Katanoda K., Matsuda Y., and Sugishita M. A spatio-temporal regression model for the analysis of functional MRI data. NeuroImage 17 3 (2002) 1415-1428
27. Kornak, J., 2000. Bayesian spatial inference from haemodynamic response parameters in functional magnetic resonance imaging. PhD thesis, University of Nottingham.
28. Lange N., and Zeger S.L. Non-linear Fourier time series analysis for human brain mapping by functional magnetic resonance imaging. Appl. Stat. 46 1 (1997) 1-29
29. Lazar N.A. The Statistics Analysis of Functional MRI data (2008), Springer, New York, Statistics for Biology and Health
30. Mardia K.V., Kent J.M., and Bibby J.M. Multivariate Analysis (1979), Academic Press, London
31. McKeown M.J., Ping Jung T., Makeig S., Brown G., Kindernann S., Won Lee T., and Sejnowski T.J. Independent component analysis of functional MRI: what is signal and what is noise?. Curr. Opin. Neurobiol. 13 5 (2003) 620-629
32. Ogawa S., Lee T.M., Kay A.R., and Tank D.W. Functional brain mapping by blood oxygenation level - dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. Biophys. J. 64 (1990) 803-812
33. Penny W., Kiebel S., and Friston K. Variational Bayesian inference for fMRI time series. NeuroImage 19 (2003) 727-741
34. Penny W.D., Trujilllo-Barreto N.J., and Friston K.J. Bayesian fMRI time series analysis with spatial priors. NeuroImage 24 2 (2005) 350-362
35. Porill J., Stone J.V., Mayhew J.E.W., Berwick J., and Coffey P. Spatio-temporal data analysis using weak linear models. Spatial Temporal Modelling and its Applications (1999) 17-20
36. Quirós A., Montes R., and Hernández J.A. A fully Bayesian two-stage model for detecting brain activity in fMRI. Lect. Notes Comput. Sci. 4345 (2006) 334-345
37. Rue H., and Held L. Gaussian Markov Random Fields: Theory and Applications. Monographs on Statistics and Applied Probability, 104 (2005), Chapman & Hall/CRC, London
38. Sjöstrand K., Lund T.E., Madsen K.H., and Larsen R. Sparse PCA, A New Method for Unsupervised Analyses of fMRI Data. Proc. International Society of Magnetic Resonance In Medicine (2006), ISMRM2006, Seattle, Washington, USA
39. Winkler G. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. A Mathematical Introduction (2003), Springer, cop
40. Woolrich M.W., Jenkinson M., Brady J.M., and Smith S.M. Fully bayesian spatio-temporal modeling of fMRI data. IEEE Trans. Med. Imaging 23 2 (2004) 213-231
41. Woolrich M.W., Behrens T.E.J., and Smith S.M. Constrained linear basis sets for HRF modelling using variational Bayes. NeuroImage 21 4 (2004) 1748-1761