Repeated decompositions reveal the stability of infomax decomposition of fMRI data

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn 7 VOLS, Issue , 2005, Pages 5324-5327

  Duann, Jeng Ren ^a   Jung, Tzyy Ping ^a   Sejnowski, Terrence J ^a,b   Makeig, Scott ^a  

^a UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^b SALK INSTITUTE FOR BIOLOGICAL STUDIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CORRELATION METHODS; EQUIVALENCE CLASSES; INDEPENDENT COMPONENT ANALYSIS; MATRIX ALGEBRA; MEDICAL COMPUTING; SORTING;

DATA SETS; EQUIVALENCE RELATIONS; REPRODUCIBILITY; VECTOR ANGLE;

MAGNETIC RESONANCE IMAGING;

EID: 33846896936     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. T.-P. Jung, S. Makeig, M. J. MoKeown, A. J. Bell, T.-W. Lee, and T. J. Sejnowski, "Imaging brain dynamics using independent component analysis," IEEE Proc., vol. 89, pp. 1107-1122, July 2001.
2. S. Makeig, T.-P. Jung, A. J. Bell, D. Ghahremani, and T. J. Sejnowski, "Blind separation of auditory event-related brain responses into independent components," Proc. Natl. Acad. Sci. USA, vol. 94, pp. 10979-10984, September 1997.
3. S. Makeig, M. Westerfield, T.-P. Jung, S. Enghoff, J. Twonsend, E. Courchesne, and T. J. Sejnowski, "Dynamic brain sources of visual evoked responses," Science, vol. 295, pp. 690-694, January 2002.
4. M. J. MoKeown, V. Varadarajan, S. Huettel, and G. McCarthy, "Deterministic and stochastic features of fMRI data: implications for analysis of event-related experiments," J. Neurosci. Methods, vol. 118, pp. 103-113, 2002.
5. [S] M. J. McKeown, "Deterministic and stochastic features of fMRI data: Implications for data averaging," in Exploratory Analysis and Data Modeling in Functional Neuroimaging, F. T. Sommer and A. Wichert Eds. Cambridge: MIT Press, 2002, pp. 63.
6. th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), Nara Japan].
7. F. Meinecke, A. Ziehe, M. Kawanabe, and K..-R. Muller, Estimating the reliability of ICA projections, vol. 14. Cambridge, MA: MIT Press, 2002.
8. F. Meinecke, A. Ziehe, M. Kawanabe, and K.-R. Muller, "A resampling approach to estimate the stability of one-dimensional or multidimensional independent components," IEEE Trans. Biomedical Engr., vol. 49, pp. 1514-1525, 2002.
9. J.-R. Duann, T.-P. Jung, W.-J. Kuo, T.-C. Yeh, S. Makeig, J.-C. Hsieh, and T. J. Sejnowski, "Single-trial variability in event-related BOLD signal," NeuroImage, vol. 15, pp. 823-835, 2002.
10. M. J. McKeown, S. Makeig, G. G. Brown, T.-P. Jung, S. S. Kindermann, A. J. Bell, and T. J. Sejnowski, "Analysis of fMRI data by blind separation into independent spatial components" Human Brain Mapp. vol. 6, pp. 160-188.
11. A. J. Bell, and T. J. Sejnowski, "An information-maximation approach to blind separaion and blind deconvolution," Neural Comput. vol. 7, pp. 1129-1159, 1995.
12. S.-I. Amari, "Natural gradient works efficiently in learning," Neural Computation, vol. 10, pp. 251-276, 1998.
13. T.-W. Lee, M. Girolami, and T. J. Sejnowski, "Independent component analysis using an extended Infomax algorithm for mixed sub-Gaussian and super-Gaussian sources," Neural Computation, vol. 11, pp. 609-633, 1999.
14. S. Makeig et al., World Wide Web publication, now available from http://www.sccn.ucsd.edu/fmrlab, 1997.
15. J.-R. Duann, T.-P. Jung, and S. Makeig, FMRLAB: Matlab software for independent component analysis of fMRI data. World Wide Web publication http://www.sccn.ucsd.edu/fmrlab, 2003.