IVA for multi-subject FMRI analysis: A comparative study using a new simulation toolbox

IEEE International Workshop on Machine Learning for Signal Processing

Volumn , Issue , 2011, Pages

  Dea, Josselin T ^a   Anderson, Matthew ^a   Allen, Elena ^b   Calhoun, Vince D ^b,c   Adali, Tülay ^a  

^a UNIVERSITY OF MARYLAND BALTIMORE COUNTY   (United States)

^b MIND RESEARCH NETWORK   (United States)

^c UNIVERSITY OF NEW MEXICO   (United States)

Author keywords

fMRI; functional; group ICA; independent component analysis; independent vector analysis; IVA; simulation

Indexed keywords

FMRI; FUNCTIONAL; INDEPENDENT COMPONENTS; INDEPENDENT VECTOR ANALYSIS; IVA; SIMULATION;

INDEPENDENT COMPONENT ANALYSIS; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; SEPARATION; SIGNAL PROCESSING;

BLIND SOURCE SEPARATION;

EID: 82455198925     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2011.6064618     Document Type: Conference Paper

References (21)

1. A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis. Wiley-Interscience, 2001.
2. F. Esposito, T. Scarabino, A. Hyvarinen, J. Himberg, E. Formisano, S. Comani, G. Tedeschi, R. Goebel, E. Seifritz, and F. D. Salle, "Independent component analysis of fMRI group studies by self-organizing clustering," NeuroImage, vol. 25, no. 1, pp. 193-205, 2005. (Pubitemid 40289149)
3. V. D. Calhoun, T. Adali, V. B. McGinty, J. J. Pekar, T. D. Watson, and G. D. Pearlson, "fMRI activation in a visual-perception task: Network of areas detected using the general linear model and independent components analysis," NeuroImage, vol. 14, no. 5, pp. 1080-1088, 2001. (Pubitemid 33049849)
4. V. D. Calhoun, T. Adali, J. J. Pekar, and G. D. Pearlson, "A method for making group inferences from functional MRI data using independent component analysis," Human Brain Mapping, vol. 14, no. 3, pp. 140-151, 2001. (Pubitemid 33032347)
5. M. Svensén, F. Kruggel, and H. Benali, "ICA of fMRI group study data," NeuroImage, vol. 16, no. 3, Part 1, pp. 551-563, 2002.
6. C. Beckmann and S. Smith, "Tensorial extensions of independent component analysis for multi-subject fMRI analysis," NeuroImage, vol. 25, no. 1, pp. 294-311, 2005. (Pubitemid 40289157)
7. V. J. Schmithorst and S. K. Holland, "Comparison of three methods for generating group statistical inferences from independent component analysis of functional magnetic resonance imaging data," Journal of Magnetic Resonance Imaging, vol. 19, no. 3, pp. 365-368, 2004.
8. N. M. Correa, T. Adali, Y.-O. Li, and V. D. Calhoun, "Comparison of blind source separation algorithms for fMRI using a new Matlab toolbox: GIFT," in Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on, vol. 5, march 2005, pp. v/401-v/404 Vol. 5.
9. E. A. Allen, E. B. Erhardt, Y.Wei, T. Eichele, and V. Calhoun, "Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study," In Submission. 2011.
10. J.-H. Lee, T.-W. Lee, F. A. Jolesz, and S.-S. Yoo, "Independent vector analysis (IVA): Multivariate approach for fMRI group study," NeuroImage, vol. 40, no. 1, pp. 86-109, 2008.
11. Y.-O. Li, T. Adali, W. Wang, and V. D. Calhoun, "Joint blind source separation by multiset canonical correlation analysis," IEEE Trans. Signal Process., vol. 57, no. 10, pp. 3918-3929, Oct. 2009.
12. H. Hotelling, "Relations between two sets of variates," Biometrika, vol. 28, no. 3/4, pp. 321-377, 1936.
13. J. R. Kettenring, "Canonical analysis of several sets of variables,"Biometrika, vol. 58, no. 3, pp. 433-451, 1971.
14. I. Rustandi, M. A. Just, and T. M. Mitchell, "Integrating multiple-study multiple-subject fMRI datasets using canonical correlation analysis,"in Proceedings of the MICCAI 2009 Workshop: Statistical modeling and detection issues in intra- and inter-subject functional MRI data analysis, Sep. 2009.
15. M. Anderson, T. Adali, and X.-L. Li, "Joint blind source separation of multivariate Gaussian sources: Algorithms and performance analysis,"IEEE Trans. Signal Process., to be published.
16. X.-L. Li, T. Adali, and M. Anderson, "Joint blind source separation by generalized joint diagonalization of cumulant matrices," Signal Processing, vol. In press, corrected proof, 2011.
17. J. Cardoso and A. Souloumiac, "Blind beamforming for non-Gaussian signals," Radar and Signal Processing, IEE Proceedings F, vol. 140, no. 6, pp. 362-370, Dec. 1993.
18. A. J. Bell and T. J. Sejnowski, "An information maximization approach to blind separation and blind deconvolution," Neural Comput., vol. 7, pp. 1129-1159, 1995.
19. J.-F. Cardoso, "Infomax and maximum likelihood for blind source separation,"IEEE Signal Processing Letters, vol. 4, no. 4, pp. 112-114, 1997. (Pubitemid 127554963)
20. D.-T. Pham and J.-F. Cardoso, "Blind separation of instantaneous mixtures of non stationary sources," IEEE Trans. Signal Process, vol. 49, no. 9, pp. 1837-1848, 2001. (Pubitemid 32866193)
21. E. Erhardt, E. Allen, Y. Wei, T. Eichele, and V. Calhoun, "SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability,"In Submission. 2011.