Bayesian spatiotemporal model of fMRI data using transfer functions

NeuroImage

Volumn 52, Issue 3, 2010, Pages 995-1004

  Quirós, Alicia ^a   Diez, Raquel Montes ^a   Wilson, Simon P ^b  

^a UNIVERSIDAD REY JUAN CARLOS   (Spain)

^b TRINITY COLLEGE DUBLIN   (Ireland)

Author keywords

Bayesian analysis; FMRI; HRF estimation; MCMC; Spatiotemporal model; Transfer functions

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; CONTROLLED STUDY; EVOKED RESPONSE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HEMODYNAMICS; IMAGE ANALYSIS; NEUROIMAGING; NORMAL DISTRIBUTION; PRIORITY JOURNAL; PROBABILITY; SIGNAL NOISE RATIO; SIMULATION; STATISTICAL MODEL; BIOLOGICAL MODEL; BRAIN; BRAIN CIRCULATION; BRAIN MAPPING; COMPUTER ASSISTED DIAGNOSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES; VASCULARIZATION;

ALGORITHMS; BAYES THEOREM; BRAIN; BRAIN MAPPING; CEREBROVASCULAR CIRCULATION; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL;

EID: 77954386091     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2009.12.085     Document Type: Article

References (26)

1. Bowman F.D. Spatiotemporal models for region of interest analysis of functional neuroimaging data. J. Am. Stat. Assoc. 2007, 102(478):442-453.
2. Box G., Jenkins G. Time Series Analysis, Forecasting and Control 1976, Holden-Day, San Francisco. second edn.
3. Boynton G.M., Engel S.A., Glover G.H., Heeger D.J. Linear systems analysis of functional magnetic resonance imaging in human V1. J. Neurosci. 1996, 16(13):4207-4221.
4. Ciuciu P., Poline J.-B., Marrelec G., Idier J., Pallier C., Benali H. Unsupervised robust nonparametric estimation of the hemodynamic response function for any fMRI experiment. IEEE Trans. Med. Imaging 2003, 22(10):1235-1251.
5. Flandin G., Penny W.D. Bayesian fMRI data analysis with sparse spatial basis function priors. NeuroImage 2007, 34:1108-1125.
6. Friston K.J., Penny W. Posterior probability maps and SPMs. NeuroImage 2003, 19:1240-1249.
7. Friston K.J., Jezzard P., Turner R. Analysis of functional MRI time-series. Hum. Brain Mapp. 1994, 1(2):153-171.
8. Friston K.J., Holmes A.P., Poline J.-B., Gransby P.J., Williams S.C.R., Frackowiak R.S.J., Turner R. Analysis of fMRI time-series revisited. NeuroImage 1995, 2(1):45-53.
9. Friston K.J., Fletcher P., Joshephs O., Holmes A., Rugg M., Turner R. Event-related fmri: Characterizing differential responses. NeuroImage 1998, 7:30-40.
10. Geweke J. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. Bayesian Statistics 4 1992, Clarendon Press, Oxford, UK. J.M. Bernardo, J.O. Berger, A.P. Dawid, A.F.M. Smith (Eds.).
11. Gossl C., Auer D.P., Fahrmeir L. Bayesian spatiotemporal inference in functional magnetic resonance imaging. Biometrics 2001, 57(2):554-562.
12. Goutte C., Nielsen F.A., Hansen L.K. Modeling the haemodynamic response in fmri using smooth fir filters. IEEE Trans. Med. Imaging 2000, 19(12):1188-1201.
13. Katanoda K., Matsuda Y., Sugishita M. A spatio-temporal regression model for the analysis of functional MRI data. NeuroImage 2002, 17(3):1415-1428.
14. Kornak, J., 2000. Bayesian spatial inference from haemodynamic response parameters in functional magnetic resonance imaging. PhD. Thesis, University of Nottingham.
15. Lange N., Zeger S.L. Non-linear fourier time series analysis for human brain mapping by functional magnetic resonance imaging. Applied Statistics 1997, 46(1):1-29.
16. Makni S., Idier J., Vincent T., Thirion B., Dehaene-Lambertz G., Ciuciu P. A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI. NeuroImage 2008, 41:941-969.
17. Nielsen, F. A., Hansen, L. K., Toft, P., Goutte, C., Lange, N., Strother, S. C., Mørch, N., Svarer, C., Savoy, R., Rosen, B., Rostrup, E. and Born, P., 1997. Comparison of two convolution models for fmri time series, in NeuroImage, edited by Friberg, L., Gjedde, A., Holm, S., Lassen, N. A. and Novak, M., vol. 5, p. S473, San Diego: Academic Press, From the Third International Conference on Functional Mapping of the Human Brain.
18. Ogawa S., Lee T.M., Kay A.R., 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. 1990, 64:803-812.
19. Penny W., Kiebel S., Friston K. Variational Bayesian inference for fMRI time series. NeuroImage 2003, 19:727-741.
20. Penny W.D., Trujilllo-Barreto N.J., Friston K.J. Bayesian fMRI time series analysis with spatial priors. NeuroImage 2005, 24(2):350-362.
21. Quirós A., Montes R., Hernández J.A. A fully Bayesian two-stage model for detecting brain activity in fMRI. Lect. Notes Comput. Sci. 2006, 4345:334-345.
22. Quirós A., Montes Diez R., Gamerman D. Bayesian spatiotemporal model of fMRI data. NeuroImage 2010, 49:442-456.
23. Rue H., Held L. Gaussian Markov Random Fields: Theory and Applications. Monographs on Statistics and Applied Probability 2005, 104. Chapman & Hall/CRC, London.
24. Wang B., Titterington D.M. Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values. Proc. 20th Conf. Uncertainty in Artificial Intell 2004, 577-584. AUAI Press. M. Chickering, J. Halperin (Eds.).
25. Woolrich M.W., Jenkinson M., Brady J.M., Smith S.M. Fully bayesian spatio-temporal modeling of fMRI data. IEEE Trans. Med. Imaging 2004, 23(2):213-231.
26. Woolrich M.W., Behrens T.E.J., Smith S.M. Constrained linear basis sets for HRF modelling using variational Bayes. NeuroImage 2004, 21(4):1748-1761.