Reversible jump Markov chain Monte Carlo signal detection in functional neuroemaging analysis

2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano

Volumn 1, Issue , 2004, Pages 868-871

  Lukic, Ana S ^a   Wernick, Miles N ^a   Galatsanos, Nikolas P ^b   Yang, Yongyi ^a   Strother, Stephen C ^c  

^a Illinois Institute of Technology   (United States)

^b UNIVERSITY OF IOANNINA   (Greece)

^c UNIVERSITY OF MINNESOTA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVATION FUNCTIONS; ACTIVATION PATTERNS; BRAIN ACTIVATION; MARKOV CHAIN MONTE CARLO SIGNAL DETECTION;

ACOUSTIC NOISE; ACTIVATION ANALYSIS; COMPUTERIZED TOMOGRAPHY; DRUG PRODUCTS; MAGNETIC RESONANCE IMAGING; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS;

MEDICAL IMAGING;

EID: 17144414952     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. Green, P. J., "Reversible Jump Markov chain Monte Carlo Computation and Bayesian Model Determination," Biometrika, vol. 82, pp. 711-32, 1995.
2. Hartvig, N. V. and Jensen, J. L., "Spatial Mixture Modeling of fMRI Data," Human Brain Mapping, vol. 11, pp. 233-248, 2000.
3. LaConte, S., Anderson, J., Muley, S., Ashe, J., Fruitiger, S., Rehm, K., Hansen, L. K., Yacoub, E., Hu, X., Rottenberg, D. A., and Strother, S. C., "The Evaluation of Preprocessing Choices in Single-Subject BOLD-fMRI Using NPAIRS Performance Metrics," Neuroimage, vol. 18, pp. 10-27, 2003.
4. Lukic, A. S., Wernick, M. N., and Strother, S. C., "An evaluation of methods for detection of brain activations from PET or fMRI images," Artificial Intelligence in Medicine, vol. 25, pp. 69-88, 2002.
5. Petersson, K. M., Nichols, T. E., Poline, J.-B., and Holmes, A. P., "Statistical Limitations in Functional Neuroimaging. I. Non-Inferential Methods and Statistical Models," Phil. Trans. Royal Soc. - Series B: Biological Sciences, pp. 1239-1260, 1999.
6. Robert, C. and Casella, G., Monte Carlo Statistical Methods: Springer Verlag, 1999.
7. Robert, C. P., Ryden, T., and Titterington, D. M., "Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method," Journal of Royal Statistical Society, vol. 62, pp. 57-75, 2000.
8. Stawinski, G., Doucet, A., and Duvaut, P., "Reversible Jump Markov Chain Monte Carlo for Bayesian Deconvolution of Point Sources," Proceedings of SPIE, Bayesian Inference for Inverse Problems, vol. 3459, pp. 179-190, 1998.
9. Strother, S. C., Anderson, J. R., Hansen, L. K., Kjems, U., Kustra, R., Siditis, J., Fruitiger, S., Muley, S., LaConte, S., and Rottenberg, D. A., "The Quantitative Evaluation of Functional Neuroimaging Experiments: The NPAIRS Data Analyis Framework," Neuroimage, vol. 15, pp. 747-771, 2002.