A new spatially constrained NMF with application to fMRI

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn , Issue , 2011, Pages 5052-5055

  Ferdowsi, Saideh ^a   Abolghasemi, Vahid ^a   Makkiabadi, Bahador ^a   Sanei, Saeid ^a  

^a UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE AREA; CONSTRAINED OPTIMIZATION PROBLEMS; DATA-DRIVEN METHODS; DECOMPOSITION PROCESS; DETECTION PERFORMANCE; FMRI DATA; NONNEGATIVE MATRIX FACTORIZATION; PART-BASED REPRESENTATION; SPATIAL INFORMATIONS;

BLIND SOURCE SEPARATION; CONSTRAINED OPTIMIZATION; FACTORIZATION;

MATRIX ALGEBRA;

ALGORITHM; ARTICLE; BRAIN; BRAIN MAPPING; COMPUTER ASSISTED DIAGNOSIS; HUMAN; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; BRAIN; BRAIN MAPPING; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84055184816     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2011.6091251     Document Type: Conference Paper

References (15)

1. S. Ogawa, T. Lee, A. Nayak, and P. Glynn, "Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields.," vol. 14, pp. 68-78, 1990.
2. V. D. Calhoun, T. Adali, L. K. Hansen, J. Larsen, and J. J. Pekar, "ICA of functional mri data: An overview," in Proceedings of the International Workshop on Independent Component Analysis and Blind Signal Separation, pp. 281-288, 2003.
3. X. Wang, J. Tian, X. Li, J. Dai, and L. Ai, "Detecting brain activations by constrained non-negative matrix factorization from task-related BOLD fMRI," Medical Images, vol. 5369, pp. 675-682, 2004.
4. M. O. Ulfarsson and V. Solo, "Sparse variable principal component analysis with application to fMRI," Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on, pp. 460 -463, 2007.
5. G. Lohmann, K. G. Volz, and M. Ullsperger, "Using non-negative matrix factorization for single-trial analysis of fMRI data," NeuroImage, vol. 37, no. 4, pp. 1148 - 1160, 2007.
6. S. Ferdowsi, V. Abolghasemi, and S. Sanei, "A constrained nmf algorithm for bold detection in fMRI," Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop on, pp. 77-82, 2010.
7. D. D. Lee and H. S. Seung, "Learning the parts of objects by nonnegative matrix factorization," Nature, vol. 401, no. 6755, pp. 788-791, October 1999.
8. K. J. Friston, P. Fletcher, O. Josephs, A. Holmes, M. D. Rugg, and R. Turner, "Event-related fmri: Characterizing differential responses," NeuroImage, vol. 7, no. 1, pp. 30 - 40, 1998.
9. D. D. Lee and H. S. Seung, "Algorithms for non-negative matrix factorization," In NIPS, pp. 556-562, 2000.
10. N. Gillis and F. Glineur, "Nonnegative factorization and the maximum edge biclique problem," Tech. Rep., Universit catholique de Louvain, Center for Operations Research and Econometrics (CORE), Nov. 2008.
11. Machine Learning for Signal Processing Laboratory, "http://mlsp. umbc.edu," .
12. Statitstical Parameter Mapping (SPM) homepage, "http://www.fil.ion. ucl.ac.uk/spm/," .
13. WFU PickAtlas Toolbox, "http://fmri.wfubmc.edu/software/pickatlas, " .
14. K. J. Friston, K. J. Worsley, R. S. J. Frackowiak, J. C. Mazziotta, and A. C. Evans, "Assessing the significance of focal activations using their spatial extent.," Human Brain Mapping, no. 1, pp. 214-220, 1994.
15. E. Fishler, M. Grosmann, and H. Messer, "Detection of signals by information theoretic criteria: general asymptotic performance analysis," Signal Processing, IEEE Transactions on, vol. 50, no. 5, pp. 1027 -1036, May 2002.