Assessing brain activity through spatial Bayesian variable selection

NeuroImage

Volumn 20, Issue 2, 2003, Pages 802-815

  Smith, Michael ^a   Pütz, Benno ^b   Auer, Dorothee ^b   Fahrmeir, Ludwig ^c  

^a UNIVERSITY OF SYDNEY   (Australia)

^b MAX PLANCK INSTITUTE OF PSYCHIATRY   (Germany)

^c UNIVERSITY OF MUNICH   (Germany)

Author keywords

Activation probabilities; Anatomical prior information; Bayesian spatial modeling; Edge preservation; fMRI experiments; Ising prior; Markov chain Monte Carlo sampling

Indexed keywords

ARTICLE; BAYES THEOREM; BRAIN CORTEX; DATA ANALYSIS; ELECTROENCEPHALOGRAM; GRAY MATTER; MODEL; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; SIMULATION; TECHNIQUE;

EID: 0142011000     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1053-8119(03)00360-4     Document Type: Article

References (23)

1. Everitt B.S., Bullmore E.T. Mixture model mapping of the brain activation in functional magnetic resonance images. Hum. Brain Mapp. 7:1999;1-14.
2. Fahrmeir, L., Gössl, C., Hennerfeind, A., 2002. Spatial smoothing with robust priors in functional MRI, in: Schwaiger, M., Opitz, O., (Eds.), Exploratory Data Analysis in Empirical Research, Springer-Verlag, Heidelberg, pp. 50-57.
3. Friston K.J., Glaser D.E., Henson R.N.A., Kiebel S., Phillips C., Ashburner J. Classical and Bayesian inference in neuroimaging applications . NeuroImage. 16:2002;484-512.
4. Friston K.J., Holmes A.P., Poline J.-B., Grasby P.J., Williams S.C.R., Frackowiak R.S.J., Turner R. Analysis of fMRI time-series revisited. NeuroImage. 2:1995;45-53.
5. Friston K.J., Penny W., Phillips C., Kiebel S., Hinton G., Ashburner J. Classical and Bayesian inference in neuroimaging theory . NeuroImage. 16:2002;465-483.
6. Gamerman D. Efficient sampling from the posterior distribution in generalized linear models. Stat. Comput. 7:1997;57-68.
7. Genovese C.R. A Bayesian time-course model for functional magnetic resonance imaging data (with discussion). J. Am. Stat. Assoc. 95:2000;691-719.
8. George E., McCulloch R. Approaches for Bayesian variable selection. Stat. Sinica. 7:1997;339-373.
9. Gössl C., Auer D.P., Fahrmeir L. Dynamic models in fMRI. Magn. Reson. Med. 43:2000;72-81.
10. Gössl C., Auer D.P., Fahrmeir L. Bayesian spatio-temporal inference in functional magnetic resonance Imaging. Biometrics June. 57:2001;554-562.
11. Gössl C., Fahrmeir L., Auer D.P. Bayesian modeling of the hemodynamic response function in BOLD fMRI. NeuroImage July. 14:(1 Part 1):2001;140-148.
12. Green P.J., Richardson S. Hidden Markov models and disease mapping. J. Am. Stat. Assoc. 97:2002;1055-1070.
13. Hartvig N.V., Jensen J.L. Spatial mixture modeling of fMRI data. Hum. Brain Mapp. 11:2000;233-248.
14. Higdon D. Auxiliary variable methods for Markov chain Monte Carlo with applications. J. Am. Stat. Assoc. 93:1998;585-595.
15. Huettel S.A. The effects of aging upon the hemodynamic response measured by functional MRI. NeuroImage January. 13:2001;161-175.
16. Kiebel S., Friston K.J. Anatomically informed basis functions in multi-subject studies. Hum. Brain Mapp. 16:2002;36-46.
17. Kiebel S., Goebel R., Friston K.J. Anatomically informed basis functions. NeuroImage. 11:2000;656-667.
18. Kohn R., Smith M., Chan D. Nonparametric regression using linear combinations of basis functions. Stat. Comput. 11:2001;313-322.
19. Raftery A. Hypothesis testing and model selection. Gilks W., Richardson S., Spiegelhalter D. Markov Chain Monte Carlo in Practice. 1996;165-187 Chapman and Hall, London.
20. Smith M., Kohn R. Nonparametric regression via Bayesian variable selection. J. Econometr. 75:1996;317-344.
21. Smith M., Wong C., Kohn R. Additive nonparametric regression with autocorrelated errors. J. R. Stat. Soc. Ser. B. 60:1998;311-331.
22. Swendsen R., Wang J. Non-universal critical dynamics in Monte Carlo simulations. Phys. Rev. Lett. 58:1987;86-88.
23. Tanner M. Tools for Statistical Inference, third ed. 1996; Springer-Verlag, New York.