ICA, kernel methods and nonnegativity: New paradigms for dynamical component analysis of fMRI data

Engineering Applications of Artificial Intelligence

Volumn 22, Issue 4-5, 2009, Pages 497-504

  Gruber, Peter ^a,b   Meyer Bäse, Anke ^a   Foo, Simon ^a   Theis, Fabian J ^b  

^a FLORIDA STATE UNIVERSITY   (United States)

^b INSTITUTE OF EPIDEMIOLOGY   (Germany)

Author keywords

Genetic Algorithm; Kernel Method; Nonnegative matrix factorization; PCA; ROC; Sparseness

Indexed keywords

KERNEL METHOD; NONNEGATIVE MATRIX FACTORIZATION; PCA; ROC; SPARSENESS;

APARTMENT HOUSES; BLIND SOURCE SEPARATION; DATA VISUALIZATION; FACTORIZATION; GENETIC ALGORITHMS; HEMODYNAMICS; LIGHT MEASUREMENT; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MATRIX ALGEBRA; MEDICAL APPLICATIONS; MEDICAL IMAGING; NONLINEAR ANALYSIS; NUCLEAR MAGNETIC RESONANCE; NUMBER THEORY; PHYSIOLOGICAL MODELS; SPEECH ANALYSIS; TIME SERIES ANALYSIS;

INDEPENDENT COMPONENT ANALYSIS;

EID: 67349209953     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2008.11.010     Document Type: Article

References (28)

1. Arfanakis K., Cordes D., Haughton V., Moritz C., Quigley M., and Meyerand M. Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets. Magn. Reson. Imaging 18 (2000) 921-930
2. Bandettini P., Wong E., Hinks R., Tikofski R., and Hyde J. Time course EPI of human brain function during task activation. Magn. Reson. Med. 25 (1992) 390-397
3. Boxerman J., Bandettini P., Kwong K., and Baker J. The intravascular contribution to fMRI signal change: Monte Carlo modeling and diffusion-weighted studies in vivo. Magn. Reson. Med. 34 (1995) 4-10
4. Chipperfield, A., Fleming, P., Pohlheim, H., Fonseca, C., 1994. Genetic algorithm toolbox user's guide. ACSE Research Report 512, Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK.
5. Frahm J., Merboldt K., and Hanicke W. Functional MRI of human brain activation at high spatial resolution. Magn. Reson. Med. 29 (1992) 139-144
6. Gruber, P., Kohler, C., Theis, F.J., 2007. A toolbox for model-free analysis of fMRI data. In: Proceedings of International Symposium on Independent Component Analysis and Blind Signal Separation (ICA), London, GB. Lecture Notes in Computer Science. Springer, Berlin.
7. Hoyer P.O. Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5 (2004) 1457-1469
8. Hyvärinen A., and Oja E. Independent component analysis: algorithms and applications. Neural Networks 13 (2000) 411-430
9. Hyvärinen A., Hoyer P.O., and Inki M. Topographic independent component analysis. Neural Comput. 13 7 (2001) 1525-1558
10. Hyvärinen, A., Karhunen, J., Oja, E. Independent Component Analysis, Wiley, New York.
11. Karvanen, J., Eriksson, J., Koivunen, V., Pearson system based method for blind separation. In: Proceedings of Second International Workshop on Independent Component Analysis and Blind Signal Separation, pp. 585-590.
12. Keck I.R., Theis F.J., Gruber P., Lang E.W., Specht K., and Puntonet C.G. 3D spatial analysis of fMRI data-a comparison of ICA and GLM analysis on a word perception task. Proceedings of International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary (2004) 2495-2500 iSBN: 0-7803-8360-5
13. Kwong K. Functional magnetic resonance imaging with echo planar imaging. Magn. Reson. Q. 11 (1992) 1-20
14. Kwong K., Belliveau J., and Chesler D. Dynamic magnetic resonance imaging of human brain activity during primary sensor stimulation. Proc. Natl. Acad. Sci. 89 (1992) 5675-5679
15. Lee D.D., and Seung H.S. Learning the parts of objects by non-negative matrix factorization. Nature 40 (1999) 788-791
16. McKeown M., Jung T., Makeig S., Brown G., Jung T., Kindermann S., Bell A., and Sejnowski T. Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapping 6 (1998) 160-188
17. Meyer-Bäse A., Lange O., Wismüller A., and Ritter H. Model-free functional MRI analysis using topographic independent component analysis. Int. J. Neural Syst. 14 4 (2004) 217-228
18. Meyer-Bäse, A., Gruber, P., Theis, F.J., Wismuüller, A., Ritter, H., 2005. Application of unsupervised clustering methods to biomedical image analysis. In: Proceedings of Workshop on Self-Organizing Maps (WSOM), Paris, France, pp. 621-628.
19. Ogawa S., Tank D., and Menon R. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. 89 (1992) 5951-5955
20. Ogawa S., Lee T., and Barrere B. The sensitivity of magnetic resonance image signals of a rat brain to changes in the cerebral venous blood oxygenation activation. Magn. Reson. Med. 29 (1993) 205-210
21. Schölkopf B., Smola A., and Müller K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10 (1998) 1299-1319
22. Stadlthanner, K., Theis, F.J., Puntonet, C.G., Lang, E.W., 2005. Extended sparse nonnegative matrix factorization. In: Proceedings of International Work-Conference on Artificial Neural Networks (IWANN), Lecture Notes in Computer Science, vol. 3512, Springer, Berlin, Germany, Barcelona, Spain, pp. 249-256.
23. Theis F.J. Uniqueness of complex and multidimensional independent component analysis. Signal Process. 84 5 (2004) 951-956
24. Theis, F.J., Gruber, P., Keck, I.R., Meyer-Bäse, A., Lang, E.W., 2005a. Spatiotemporal blind source separation using double-sided approximate joint diagonalization. In: Proceedings of European Signal Processing Conference (EUSIPCO), Antalya, Turkey.
25. Theis, F.J., Stadlthanner, K., Tanaka, T., 2005b. First results on uniqueness of sparse non-negative matrix factorization. In: Proceedings of European Signal Processing Conference (EUSIPCO), Antalya, Turkey.
26. Wismüller A., Lange O., Dersch D., Leinsinger G., Hahn K., Pütz B., and Auer D. Cluster analysis of biomedical image time-series. Int. J. Comput. Vision 46 (2002) 102-128
27. Woods R., Cherry S., and Mazziotta J. Rapid automated algorithm for aligning and reslicing pet images. J. Comput. Assisted Tomography 16 (1992) 620-633
28. Ziehe, A., Mueller, K.-R., 1998. TDSEP-an efficient algorithm for blind separation using time structure. In: Niklasson, L., Bodén, M., Ziemke, T., (Eds.), Proceedings of ICANN'98. Springer, Berlin, Skövde, Sweden, pp. 675-680.