A bayesian time-course model for functional magnetic resonance imaging data

Journal of the American Statistical Association

Volumn 95, Issue 451, 2000, Pages 691-703

  Genovese, Christopher R ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Bayesian inference; Functional neuroimaging; Hierarchical models; Magnetic resonance

Indexed keywords

EID: 2242458721     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.2000.10474253     Document Type: Article

References (35)

1. Aguirre, G., Zarahn, E., and D’Esposito, M. (1998), “A Critique of the Use of the Kolmogorov-Smirnov (KS) Statistic for the Analysis of BOLD fMRI Data,” Magnetic Resonance in Medicine, 39, 500-505.
2. Baddeley, A. (1986), Working Memory, New York: Oxford University Press.
3. Bandettini, P. A., Jesmanowicz, A., Wong, E. C., and Hyde, J. (1993), “Processing Strategies for Time-Course Data Sets in Functional MRI of the Human Brain,” Magnetic Resonance in Medicine, 30, 161-173.
4. Cohen, J., Forman, S., Braver, T., Casey, B., Servan-Schreiber, D., and Noll, D. (1994), “Activation of Prefrontal Cortex in a Nonspatial Working Memory Task With Functional MRI,” Human Brain Mapping, 1, 293-304.
5. Cowles, M. K., and Carlin, B. P. (1996), “Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review,” Journal of the American Statistical Association, 91, 883-904.
6. de Boor, C. (1978), A Practical Guide to Splines, New York: SpringerVerlag.
7. DiCiccio, T. J., Kass, R. E., Raftery, A., and Wasserman, L. (1997), “Computing Bayes Factors by Combining Simulation and Asymptotic Approximations,” Journal of the American Statistical Association, 92, 903-915.
8. Eddy, W. F., Fitzgerald, M., Genovese, C. R., Mockus, A., and Noll, D. (1996), “Functional Image Analysis Software—Computational Olio,” in Proceedings in Computational Statistics, Vol. 12, ed. A. Prat, Heidelberg: Physica-Verlag, pp. 39-4-9.
9. Eddy, W. F., Fitzgerald, M., and Noll, D. C. (1996), “Improved Image Registration Using Fourier Interpolation,” Magnetic Resonance in Medicine, 36, 923-931.
10. Forman, S., Cohen, J. C., Fitzgerald, M., Eddy, W., Mintun, M., and Noll, D. C. (1995), “Improved Assessment of Significant Change in Functional Magnetic Resonance fMRI: Use of a Cluster Size Threshold,” Magnetic Resonance in Medicine, 33, 636-647.
11. Friston, K. J., Frith, C. D., and Frackowiak, R. S. J. (1994), “Time-Dependent Changes in Effective Connectivity Measured with PET,” Human Brain Mapping, 1, 69-79.
12. Friston, K. J., Jezzard, P., and Turner, R. (1994), “Analysis of Functional MRI Time-Series,” Human Brain Mapping, 1, 153-171.
13. Geman, S., and Geman, D. (1984), “Stochastic Relaxation, Gibbs Distributions, and Bayesian Restoration of Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.
14. Geman, S., and McClure, D. E. (1987), “Statistical Methods for Tomographic Image Reconstruction,” in Proceedings of the 46th Session of the ISI, Bulletin of the ISI, 52.
15. Genovese, C. R., Noll, D. C., and Eddy, W. F. (1997), “Estimating Test-Retest Reliability in fMRI I,” Magnetic Resonance in Medicine, 38,497-507.
16. Green, P. J. (1995), “Reversible Jump MCMC Computation and Bayesian Model Determination,” Biometrika, 82, 711-732.
17. Hastie, T., and Tibshirani, R. (1990), Generalized Additive Models, New York: Chapman and Hall.
18. Holmes, A. P., Blair, R. C., Watson, J. D., and Ford, I. (1996), “Non-Parametric Analysis of Statistic Images From Functional Mapping Experiments,” Journal of Cerebral Blood Flow and Metabolism, 16, 7-22.
19. Johnson, V. E., Bowsher, J., Jaszczak, R., and Turkington, T. (1995), “Analysis and Reconstruction of Medical Images Using Prior Information,” in Case Studies in Bayesian Statistics, Vol. 2, eds. C. Gatsonis, J. S. Hodges, R. E. Kass, and N. D. Singpurwalla, New York: Springer-Verlag, pp. 149-218.
20. Johnson, V. E., Wong, W. H., Hu, X., and Chen, C. T. (1991), “Aspects of Image Restoration Using Gibbs Priors: Boundary Modeling, Treatment of Blurring, and Selection of Hyperparameters IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 412-235.
21. Just, M. A., and Carpenter, P. A. (1992), “A Capacity Theory of Comprehension: Individual Differences in Working Memory,” Psychological Review, 99, 122-149.
22. Just, M., Carpenter, P., Keller, T. A., Eddy, W. F., and Thulborn, K. R. (1996), “Brain Activation Modulated by Sentence Comprehension,” Science, 274, 114.
23. Lange, N., and Zeger, S. (1997), “Non-Linear Fourier Time Series Analysis for Human Brain Mapping by Functional Magnetic Resonance Imaging” (with discussion), Journal of the Royal Statistical Society, Ser. C, 46, 129.
24. Ogawa, S., Tank, D., Menon, D., Ellermann, J., Kim, S., Merkle, H., and Ugurbil, K. (1992), “Intrinsic Signal Changes Accompanying Sensory Stimulation: Functional Brain Mapping Using MRI,” Proceedings of the National Academy of Sciences, 89, 5951-5955.
25. Poline, J. B., and Mazoyer, B. (1994), “Cluster Analysis in Individual Functional Brain Images: Some New Techniques to Enhance the Sensitivity of Activation Detection Methods,” Human Brain Mapping, 2, 103-111.
26. Roberts, G. O., Gelman, A., and Gilks, W. R. (1997), “Weak Convergence and Optimal Scaling of Random Walk Metropolis Algorithms,” Annals of Applied Probability, 7, 110-120.
27. Schwarz, G. (1978), “Estimating the Dimension of a Model,” The Annals of Statistics, 6, 461-164.
28. Smith, A. F. M., and Roberts, G. O. (1993), “Bayesian Computation via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods,” Journal of the Royal Statistical Society, Ser. B, 55, 3-23.
29. Talairach, J., and Tounoux, P. (1988), Coplanar Stereotaxic Atlas of the Human Brain. Three-Dimensional Proportional System: An Approach to Cerebral Imaging: Thieme, New York: Medical Publishers, Inc.
30. Thulborn, K., Waterton, J., Matthews, P., and Radda, G. (1982), “Oxygenation Dependence of the Transverse Relaxation Time of Water Protons in Whole Blood at High Field,” Biochemical Biophysics Acta, 714, 265-270.
31. Tierney, L. (1994), “Markov Chains for Exploring Posterior Distributions,” The Annals of Statistics, 22, 1701-1727.
32. Weaver, J. B., Saykin, A. Y., Burr, R. B., Riordan, H., and Maerlender, A. (1994), “Principal Component Analysis of Functional MRI of Memory,” in Proceedings of the Society for Magnetic Resonance, Second Annual Meeting, p. 808.
33. Weisskoif, R. M., Baker, J., Belliveau, J., Davis, T. L., Kwong, K. K., Cohen, M. S., and Rosen, B. R. (1993), “Power Spectrum Analysis of Functionally Weighted MR Data: What’s in the Noise?,” in Proceedings of the Society for Magnetic Resonance in Medicine, Twelfth Annual Meeting, p. 7.
34. Worsley, K. J. (1995), “Estimating the Number of Peaks in a Random Field Using the Hadwiger Characteristic of Excursion Sets, With Applications to Medical Images,” The Annals of Statistics, 23, 640-669.
35. Worsley, K. J., and Friston, K. (1995), “Analysis of fMRI Time Series Revisited—Again,” Neuroimage, 2, 173-181.