Sparse variable principal component analysis with application to FMRI

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings

Volumn , Issue , 2007, Pages 460-463

  Ulfarsson, Magnus O ^a   Solo, Victor ^b  

^a UNIVERSITY OF MICHIGAN   (United States)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Biomedical image processing; Functional magnetic resonance imaging; Statistics

Indexed keywords

BRAIN; COMPUTATIONAL METHODS; DATA REDUCTION; INDEPENDENT COMPONENT ANALYSIS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

BIOMEDICAL IMAGE PROCESSING; FUNCTIONAL MAGNETIC RESONANCE IMAGING;

MAGNETIC RESONANCE IMAGING;

EID: 36348976941     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2007.356888     Document Type: Conference Paper

References (21)

1. S. Ogawa, D.W. Tank, R. Menon, J.M. Ellerman, S.G. Kim, and H. Merkle, "Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging," Proc. Natl. Acad. Sci. USA, vol. 89, pp. 5951-5955, 1992.
2. R.B. Buxton, Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques, Cambridge University Press, Cambridge, England, 2002.
3. G.M. Boynton, S.A. Engel, G.G. Glover, and D.J. Heeger, "Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1," J. Neurosci., vol. 16, no. 13, pp. 4207-4221, 1996.
4. K.J. Friston, P. Jezzard, and R. Turner, "Analysis of functional MRI series," Human Brain Mapping, pp. 153-171, 1994.
5. Cohen M.S., "Parametric analysis of fMRI data using linear system methods." NeuroImage, vol. 6, pp. 93-103, 1997.
6. V. Solo, C. Long, E.N. Brown, E. Aminoff, M. Bar, and S. Sana, "Fmri signal modeling using laguerre polynomials," in Proc. ICIP, 2004, pp. 2431-2434.
7. V. Solo, "Automatic stopping criterion for anisotropic diffusion," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'01), Salt Lake City, Utah, USA, May 2001, vol. 6, pp. 3441-3444.
8. E. Bullmore, S.C. Brammer, S. Rabe-Hasketh, N. Janot, D.A. Meilers, R.J. Howard, and P. Sham, "Statistical methods of estimation and inference for functional mr image analysis," Magn. Reson. Med., vol. 35, no. 2, pp. 261-277, 1996.
9. K. J. Friston, C. D. Frith, P. F. Liddle, and R. S. Frackowiak, "Functional Connectivity: The principal component analysis of large data sets," J. Cereb. Blood Flow Metab., vol. 13, pp. 5-14, 1993.
10. M.J. McKeown, S. Makeig, Brown G.G., T.P. Jung, Bell A.J. Kinderman, S.S., and T.J. Sejnowski, "Analysis of fMRI data by blind separation into independent spatial component," Human Brain Mapping, vol. 6, pp. 160-188, 1998.
11. S.J. Peltier, T.A. Polk, and D.C. Noll, "Detecting low-frequency functional connectivity in fmri using self-organizing map (som) algorithm," Human Brain Mapping, vol. 20, no. 4, pp. 220-226, 2003.
12. A.R. Mcintosh, F.L. Bookstein, J.V. Haxby, and C.L. Grady, "Spatial pattern analysis of functional brain images using partial least squares," Neuroimage, vol. 3, pp. 143-157, 1996.
13. O. Friman, J. Cedefamn, P. Lundberg, Magnus Borga, and Hans Knutsson, "Detection of neural activity in functional mri using canonical correlation analysis," Magn. Res. Med., vol. 45, pp. 323-330, 2001.
14. I Johnstone and A.Y. Lu, "Sparse principal component analysis," Tech. Rep., Statistics department, Stanford University, 2004.
15. H. Zou, T. Hastie, and R. Tibshirani, "Sparse principal component analysis," J. Comput. Graphical Stat., vol. 15, no. 2, pp. 265-286, 2006.
16. I.T. Jolliffe and M. Uddin, "A modified principal component technique based on the lasso," J. Comput. Graphical Stat., vol. 12, no. 3, pp. 531-547, 2003.
17. M.O. Ulfarsson and V. Solo, "Sparse noisy PCA via geodesic steepest descent," Tech. Rep., University of Michigan, 2007.
18. D.N. Lawley, "A modified method of estimation in factor analysis and some large sample results," in Uppsala symposium on psychological factor analysis, Uppsala, Sweden, 1953, pp. 35-42.
19. D.P. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont, MA, second edition, 1999.
20. D.K. Luenberger, "The gradient projection method along geodesic," Management Sci., Theory Series, vol. 18, no. 11, pp. 620-631, 1972.
21. A. Edelman, T. A. Aries, and S.T. Smith, "The geometry of algorithms with orthogonality constraints," SIAM J. Matrix Anal. Appl., vol. 20, no. 2, pp. 303-353, 1998.