A general probabilistic model for group independent component analysis and its estimation methods

Biometrics

Volumn 67, Issue 4, 2011, Pages 1532-1542

  Guo, Ying ^a  

^a EMORY UNIVERSITY   (United States)

Author keywords

EM algorithm; Functional magnetic resonance imaging (fMRI); Group inferences; Independent component analysis; Maximum likelihood estimation; Multisubject imaging data; Variational approximation

Indexed keywords

APPROXIMATION ALGORITHMS; INDEPENDENT COMPONENT ANALYSIS; LINEAR REGRESSION; MAGNETIC RESONANCE IMAGING; MAXIMUM PRINCIPLE;

EXPECTATIONS MAXIMIZATION ALGORITHMS; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GROUP INFERENCE; IMAGING DATA; INDEPENDENT COMPONENTS ANALYSIS; MAXIMUM-LIKELIHOOD ESTIMATION; MULTISUBJECT IMAGING DATA; RESONANCE IMAGING DATA; VARIATIONAL APPROXIMATION;

MAXIMUM LIKELIHOOD ESTIMATION;

ACCURACY ASSESSMENT; ALGORITHM; COMPUTER SIMULATION; DATA SET; DESIGN; ESTIMATION METHOD; NUMERICAL MODEL; SIGNAL; SPATIOTEMPORAL ANALYSIS; UNCERTAINTY ANALYSIS;

EID: 83655163974     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2011.01601.x     Document Type: Article

References (14)

1. Beckmann, C. F. and Smith, S. M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Transactions on Medical Imaging 23, 137-152.
2. Beckmann, C. F. and Smith, S. M. (2005). Tensorial extensions of independent component analysis for multisubject FMRI analysis. NeuroImage 25, 294-311.
3. Bullmore, E., Brammer, M., Williams, S., Rabe-Hesketh, S., Janot, N., David, A., Mellers, J., Howard, R., and Sham, P. (1996). Statistical methods of estimation and inference for functional MR image analysis. Magnetic Resonance Medicine 35, 261-277.
4. Calhoun, V., Adali, T., Hansen, L. K., Larsen, J., and Pekar, J. (2003). ICA of functional MRI data: An overview. 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), 281-288. Nara, Japan.
5. Calhoun, V. D., Adali, T., Pearlson, G. D., and Pekar, J. J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping 14, 140-151.
6. Csató, L., Fokoué, E., Opper, M., Schottky, B., and Winther, O. (2000). Efficient approaches to Gaussian process classification. In Advances in Neural Information Processing Systems 12, S. A. Solla, T. K. Leen, and K.-R. Müller (eds). Cambridge, Massachusetts: MIT Press.
7. Eckart, C. and Young, G. (1936). The approximation of one matrix by another of lower rank. Psychometrika 1, 211-218.
8. Guo, Y. and Pagnoni, G. (2008). A unified framework for group independent component analysis for multi-subject fMRI data. NeuroImage 42, 1078-1093.
9. Hyvärinen, A. (1998). Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood. Neurocomputing 22, 49-67.
10. Hyvärinen, A., Karhunen, J., and Oja, E. (2001). Independent Component Analysis. New York: Wiley.
11. McKeown, M. J., Makeig, S., Brown, G. G., Jung, T. P., Kindermann, S. S., Bell, A. J., and Sejnowski, T. J. (1998). Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping 6, 160-188.
12. Moulines, È., Cardoso, J.-F., and Gassiat, E. (1997). Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models. Proceedings of the ICASSP, 3617-3620. Munich, Germany.
13. Schmithorst, V. and Holland, S. (2002). Multiple networks recruited during a story processing task found using group inferences across subjects from independent component analysis. Proceedings of the 10th Annual Meeting of ISMRM, 754. Honolulu, Hawaii.
14. Svensén, M., Kruggel, F., and Benali, H. (2002). ICA of fMRI group study data. NeuroImage 16, 551-563.