Clustered cNMF for fMRI data analysis

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 5746, Issue II, 2005, Pages 631-638

  Wang, Xiaoxiang ^a   Tian, Jie ^a   Yang, Lei ^a   Hu, Jin ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

BOLD; fMRI; K means clustering; Minimum Description Length (MDL); Non negative Matrix Factorization (NMF)

Indexed keywords

BOLD; FMRI; K-MEANS CLUSTERING; MINIMUM DESCRIPTION LENGTH (MDL); NON-NEGATIVE MATRIX FACTORIZATION (NMF);

ALGORITHMS; BRAIN; COMPUTATIONAL METHODS; DATA REDUCTION; IMAGE SEGMENTATION; INFORMATION RETRIEVAL; MATHEMATICAL MODELS; NEURAL NETWORKS;

MAGNETIC RESONANCE IMAGING;

EID: 23844487491     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.596023     Document Type: Conference Paper

References (24)

1. S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, "Brain magnetic resonance imaging with contrast dependent on blood oxygenation", Proc. Natl. Acad. Sciences, 87, pp. 9868-9872, 1990.
2. K. K. Kwong, J. W. Belliveau, D. A. Chesler, I. E. Goldberg, R. M. Weissko, B. P. Poncelet, D. N. Kennedy, B. E. Hoppel, M. S. Cohen, R. Turner, H. Cheng, T. J. Brady, and B. R. Rosen, "Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation", Proc. Natl. Acad. Sciences, 89, pp. 5675-5679, 1992.
3. Ola Friman. Adaptive Analysis of Functional MRI Data. PhD thesis, Linkoping Studies in Science and Technology, 2003.
4. K.J. Friston, J. Ashburner, et al., SPM 97 course notes. Wellcome Department of Cognitive Neurology, University College, London, 1997.
5. K.J. Worsley, C.H. Liao, J. Aston, V. Petre, G.H. Duncan, F. Morales, A.C. Evans, "A general statistical analysis for fMRI data", NeuroImage, 15, pp. 1-15, 2002.
6. J. V. Stone, "Independent component analysis: An introduction", Trends in Cognitive Sciences, 6(2), pp. 59-64, 2002.
7. V. Calhoun, T. Adali, L. K. Hansen, J. Larsen, and J. Pekar, "ICA of functional MRI data: an overview", Proc. IEEE Workshop on ICA, Nara, Japan, April 2003.
8. C. Goutte, P. Toft, E. Rostrup, F. A. Nielsen, and L. K Hansen, "On clustering fMRI time series", Neuroimage, 9, pp. 298-310, 1999.
9. K. H. Chuang, M.J. Chiu, C. C. Lin, and J. H. Chen, "Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy c-means", IEEE Trans. on Medical Imaging, 18, no. 12, pp. 1117-1128, December 1999.
10. M.J. McKeown, S. Makeig et al., "Analysis of fMRI data by blind separation into independant spatial components", Human Brain Mapping, 6, pp. 160-188, 1998.
11. V.D. Calhoun, T. Adali, G.D. Pearlson, J.J. Pekar, "Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms", Human Brain Mapping, 13, pp. 43-53, 2001.
12. J. Stone, J. Porfill, N. Porter, and I. Wilkinson, "Spatiotemporal independent component analysis of event-related fMRI data using skewed probability density functions", Neurolmage, 15, pp. 407-421, 2002.
13. V.D. Calhoun, T. Adali, J.J. Pekar, & G.D. Pearlson, "Latency (in)sensitive ICA: Group Independent Component Analysis of fMRI Data in the Temporal Frequency Domain", NeuroImage, 20, pp. 1661, 2003.
14. C. F. Beckmann, and S. M. Smith, "Probabilistic extensions to independent component analysis for FMRI", NeuroImage, 16. Presented at the 8th International Conference on Functional Mapping of the Human Brain, Sendai, Japan, June 2-6, 2002.
15. C. F. Beckmann, and S. M. Smith Beckmann, "Probabilistic ICA for FMRI - noise and inference", Fourth Int. Symp. on Independent Component Analysis and Blind Signal Separation, 2003.
16. V. D. Calhoun, T. Adali, G. D. Pearlson, P. C. M. van Zijl, and J. J. Pekar, "Independent component analysis of fMRI data in the complex domain", Magnetic Resonance in Medicine, 48, pp. 180-192, 2002.
17. V. D. Calhoun, T. Adali, G. D. Pearlson, and J. J. Pekar, "A infomax method for performing ICA of fMRI data in the complex domain", NeuroImage, 16(2), pp. 349, 2002.
18. V. Calhoun and T. Adali, "Complex ICA for FMRI analysis: performance of several approaches", Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Hong Kong, April 2003.
19. D. D. Lee, H. S. Seung, "Learning the parts of objects by non-negative matrix factorization", Nature, 401, pp. 788-791, 1999.
20. D. D. Lee, H. S. Seung, "Algorithms for non-negative matrix factorization", Advances in Neural Information Processing Systems, 13, pages 556-562, 2000.
21. S. Z. Li, X. W. Hou, and H. J. Zhang, "Learning spatially localized parts-based representation", Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Hawaii, 1, pp.207-212, 2001.
22. X. Wang, J. Tian, X. Li, J. Dai, L. Ai, "Detecting the Brain Activations by Constrained Non-negative Matrix Factorization from Task-related fMRI", Proceedings of SPIE Medical Imaging 2004.
23. MacQueen, J., "Some methods for classification and analysis of multivariate observations". In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (L. M. Le Cam and J. Neyman, Eds.), Univ. California Press, Berkeley, CA, vol. 1, 1967, pp. 281-297.
24. Wax, Mati and Kailath, Thomas, "Detection of Signals by Information Theoretic Criteria". IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol 33, (2), April, 1985.