Empirical Markov Chain Monte Carlo Bayesian analysis of fMRI data

NeuroImage

Volumn 42, Issue 1, 2008, Pages 99-111

  de Pasquale, F ^a   Del Gratta, C ^a   Romani, G L ^a  

^a UNIVERSITY OF CHIETI   (Italy)

Author keywords

Activation inference; fMRI; GLM; MCMC Bayesian approach; Variational Bayes

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; MONTE CARLO METHOD; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PROBABILITY; TASK PERFORMANCE;

ALGORITHMS; BAYES THEOREM; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; EVOKED POTENTIALS; HUMANS; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; MONTE CARLO METHOD;

EID: 45849107954     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2008.04.235     Document Type: Article

References (58)

1. Adler R.J. On excursion sets, tube formulae, and maxima of random fields, (special invited paper). Ann. Appl. Probab. 10 (2000) 1-74
2. Ahmed N., Natarajan T., and Rao K.R. Discrete cosine transform. Trans. Comput. 23 1 (1974) 90-93
3. Ardekani B.A., and Kanno I. Statistical methods for detecting activated regions in functional MRI of the brain. Magn. Reson. Imaging 16 10 (1998) 1217-1225
4. Aykroyd R.G., and Zimeras S. Inhomogeneous prior models for image reconstruction. J. Am. Stat. Soc. 94 447 (1999) 934-946
5. Bernardo J.M., Bajarri M.J., Berger J.O., David A.P., Heckermann D., Smith A.F.M., and West M. Bayesian Statistics (2007), Oxford University Press, Oxford
6. Box G.E.P., Jenkins G.M., and Reinsel G.C. Forecasting and Control (1994), Prentice Hall, London
7. Brooks S., and Gelman A. General methods for monitoring convergence of iterative simulations. Comput. Graph. Stat. 7 (1998) 434-455
8. Bullmore E., Brammer M., Williams S.C.R., Rabe-Hesketh S., Janot N., David A., Howard J.M.R., and Sham P. Statistical methods of estimation and inference for functional MR image analysis. Magn. Reson. Med. 35 (1996) 261-277
9. Bullmore E., Long C., Suckling J., Fadili J., Calvert G., Zelaya F., Carpenter T.A., and Brammer M. Colored noise and computational inference in neurophysiological (fMRI) time series analysis. HBM 12 (2001) 61-78
10. Burnham K.P., and Anderson D.R. Multimodal inference: understanding AIC and BIC in model selection. Soc. Meth. Res. 33 2 (2004) 261-304
11. Ciuciu P., Poline J.B., Marrelec G., Idier J., Pallier C., and Benali H. Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment. IEEE Trans. Med. Imag. 22 10 (2003) 1235-1251
12. de Pasquale F., Barone P., Sebastiani G., and Stander J. Bayesian analysis of dynamic magnetic resonance breast images. Appl. Stat. 53 3 (2004) 475-493
13. Descombes X., Kruggel F., and Cramon D.Y.V. Spatio-temporal fMRI analysis using Markov random fields. IEEE Trans. Med. Imag. 17 (1998) 1028-1039
14. Faisan S., Thoraval L., Armspach J.P., Metz-Luz M.-N., and Heitz F. Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models. IEEE Trans. Med. Imag. 24 2 (2005) 263-276
15. Ferretti A., Babiloni C., Gratta C.D., Caulo M., Tartaro A., Bonomo L., Rossini P., and Romani G. Functional topography of the secondary somatosensory cortex for nonpainful and painful stimuli: an fMRI study. Neuroimage 20 (2003) 1625-1638
16. Flandin G., and Penny W. Bayesian fMRI data analysis with sparse spatial basis function priors. Neuroimage 34 (2007) 1108-1125
17. Frackowiak R.S., Friston K.J., Frith C.D., Dolan R., Ashburner J., Price C., Penny W., and Zeki S. Human Brain Function (2004), Academic press
18. Frank L.R., Buxton R.B., and Wong E.C. Probabilistic analysis of functional magnetic resonance imaging data. MRM 39 (1998) 132-148
19. Friman O., Borga M., Lundberg P., and Knutsson H. Detection and detrending in fMRI data analysis. Neuroimage 22 2 (2004) 645-655
20. Friston K.J. Statistical parametric maps in functional imaging: a general linear model approach. Hum. Brain Mapp 2 (1994) 189-210
21. Friston K.J., Fletcher P., Josephs O., Holmes A.P., Rugg M.D., and Turner R. Event related fMRI: characterising differential responses. Neuroimage 7 (1998) 30-40
22. Friston K.J., Penny W., Phillips C., Kiebel S., Hinton G., and Ashburner J. Classical and Bayesian inference in neuroimaging: theory. Neuroimage 16 (2002) 465-483
23. Friston K.J., Worsley K., Frackowiak R.S.J., Mazziotta J.C., and Evans A.C. Assessing the significance of focal activations using their spatial extent. Hum. Brain Mapp. 1 (1994) 214-220
24. Geman S., and McClure D. Statistical methods for tomographic image reconstruction. Bull. Int. Stat. Inst. LII 4 (1987) 5-21
25. Gelman A., and Rubin D. Inference from iterative simulation using multiple sequence (with discussion). Stat. Sci. 7 (1992) 457-511
26. Gelman A., Roberts G., and Gilks W. Efficient metropolis jumping rules. OUP 5 (1996)
27. Gilks W.R., Richardson S., and Spiegelhalter D.J. Markov Chain Monte Carlo in practice (1996), Chapman and Hall, London
28. Gore V.K., and Jerrum M.R. The Swendsen-Wang process does not always mix rapidly. J. Stat. Phys. 97 1-2 (1999) 67-86
29. Gossl C., Fahrmeir L., and Auer P. Bayesian Modeling of the hemodynamic response function in BOLD fMRI. Neuroimage 14 (2001) 140-148
30. Green P.J. Bayesian reconstructions from emission tomography data using a modified EM algorithm. IEEE Trans. Med. Imag. 9 (1990) 84-93
31. Hartvig N., and Jensen J. Spatial mixture modeling of fMRI data. Hum. Brain Mapp. 11 4 (2000) 233-248
32. Henson R.N.A., Shallice T., Gorno-Tempini M.L., and Dolan R.J. Face repetition effects in implicit and explicit memory tests as measured by fMRI. Cereb. Cortex 12 (2001) 178-186
33. Hlushchuck Y., and Hari R. Transient suppression of ipsilateral primary somatosensory cortex during tactile finger stimulation. J. Neurosci. 26 21 (2006) 5819-5824
34. Jeffreys H.J. Theory of Probability. Carendon Press (1961) Oxford
35. Kass R.E., and Raftery A.E. Bayes factors. J. Am. Stat. Ass 90 430 (1995) 773-795
36. Kershaw J., Ardekani B.A., and Kanno I. Application of Bayesian inference to fMRI data analysis. IEEE Trans. Med. Imag. 18 12 (1999) 1138-1153
37. Luo H., and Puthusserypady S. A sparse Bayesian method for determination of flexible design matrix for fMRI data analysis. IEEE Trans. Circuit Syst. 52 12 (2005) 2699-2705
38. MacKay D.J.C. Information Theory, Inference, and Learning Algorithms (2003), Cambridge University Press. http://www.inference.phy.cam.ac.uk/mackay/itila/ Available from http://www.inference.phy.cam.ac.uk/mackay/itila/
39. Makni S., Ciuciu P., Idier J., and Poline J. Joint detection estimation of brain activity in functional MRI: a multichannel deconvolution solution. IEEE Trans. Signal Process 53 9 (2005) 3488-3502
40. Marinari E., and Parisi G. Simulated Tempering: a new Monte Carlo scheme. Europhys. Lett. 19 (1992) 451-458
41. Neumann J., and Lohmann G. Bayesian second-level analysis of functional magnetic resonance images. NeuroImage 20 (2003) 1346-1355
42. Nummenmaa A., Auranen T., Hamalainen M.S., Jaaskelainen I.P., Lampinen J., Sams M., and Vehtari A. Hierarchical Bayesian estimates of distributed MEG sources: theoretical aspects and comparison of variational and MCMC methods. NeuroImage 35 (2007) 669-685
43. Penny W.D., Trujillo-Barreto N.J., and Friston K.J. Bayesian fMRI time series analysis with spatial priors. NeuroImage 24 (2005) 350-362
44. Penny W.D., Flandin G., and Trujillo-Barreto N. Bayesian comparison of spatially regularised general linear models. Hum. Brain Mapp. 28 4 (2007) 275-293
45. Perona P., and Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. patt. Anal. and Mach. Intell. 12 7 (1990) 629-639
46. Poline J.B., Worsley K.J., Evans A.C., and Friston K.J. Combining spatial extent and peak intensity to test for activations in functional imaging. NeuroImage 5 (1997) 83-96
47. Smith M., and Fahrmeir L. Spatial Bayesian variable selection with application to functional magnetic resonance imaging. JASA 15 (2007) 417-431
48. Smith M., Putz B., Auer D., and Fahrmeir L. Assessing brain activity through spatial Bayesian variable selection. Neuroimage 20 2 (2003) 802-815
49. Smith M., and Smith D. Estimation of binary Markov random fields using Markov chain Monte Carlo. J. Comput. Graph. Stat. 15 1 (2006) 207-227
50. Swendsen R.H., and Wang J.S. Nonuniversal critical dynamics in Monte Carlo simulations. Phys. Rev. Lett. 58 (1987) 86-88
51. Weickert J. Anisotropic Diffusion in Image Processing (1998), Teubner Verlag
52. Weir I.S., and Green P.J. Modelling data from single photon emission computed tomography. Stat. Images 2 (1994)
53. Winkler G. Image Analysis, Random Fields and Dynamic Monte Carlo Methods (1995), Springer Verlag, New York
54. Woolrich M.W., Jenkinson M., Brady M., and Smith S. Fully Bayesian spatio-temporal modeling of fMRI data. IEEE Trans. Med. Imag. 23 2 (2004) 213-231
55. Worsley K.J. Detecting activation in fMRI data. Stat. Methods Med. Res. 12 (2003) 401-418
56. Worsley K.J. Spatial smoothing of autocorrelations to control the degrees of freedom in fMRI analysis. NeuroImage 26 (2005) 635-641
57. Worsley K.J., and Taylor J.E. Detecting fMRI activation allowing for unknown latency of the hemodynamic response. NeuroImage 29 (2006) 649-654
58. Worsley K.J., Liao C., Aston J.A.D., Petre V., Duncan G.H., Morales F., and Evans A.C. A general statistical analysis for fMRI data. NeuroImage 15 (2002) 1-15