Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2007, Article ID 15635, 12 pages
http://dx.doi.org/10.1155/2007/15635
Research Article

A Feature-Selective Independent Component Analysis Method for Functional MRI

1Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
2The MIND Institute, University of New Mexico, Albuquerque, NM 87106, USA
3Department of ECE, University of New Mexico, Albuquerque, NM 87106, USA
4Department of Psychiatry, Yale University, New Haven, CT 06520, USA

Received 6 May 2007; Revised 9 August 2007; Accepted 5 October 2007

Academic Editor: Yue Wang

Copyright © 2007 Yi-Ou Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. J. McKeown, S. Makeig, G. G. Brown et al., “Analysis of fMRI data by blind separation into independent spatial components,” Human Brain Mapping, vol. 6, no. 3, pp. 160–188, 1998. View at Publisher · View at Google Scholar
  2. B. B. Biswal and J. L. Ulmer, “Blind source separation of multiple signal sources of fMRI data sets using independent component analysis,” Journal of Computer Assisted Tomography, vol. 23, no. 2, pp. 265–271, 1999. View at Publisher · View at Google Scholar
  3. V. D. Calhoun, T. Adali, G. D. Pearlson, and J. J. Pekar, “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. View at Publisher · View at Google Scholar
  4. V. D. Calhoun and T. Adali, “Unmixing fMRI with independent component analysis,” IEEE Engineering in Medicine and Biology Magazine, vol. 25, no. 2, pp. 79–90, 2006. View at Publisher · View at Google Scholar
  5. S. A. Engel, G. H. Glover, and B. A. Wandell, “Retinotopic organization in human visual cortex and the spatial precision of functional MRI,” Cerebral Cortex, vol. 7, no. 2, pp. 181–192, 1997. View at Publisher · View at Google Scholar
  6. R. D. Pascual-Marqui, C. M. Michel, and D. Lehmann, “Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain,” International Journal of Psychophysiology, vol. 18, no. 1, pp. 49–65, 1994. View at Publisher · View at Google Scholar
  7. C. G. Phillips, S. Zeki, and H. B. Barlow, “Localization of function in the cerebral cortex. Past, present and future,” Brain, vol. 107, no. 1, pp. 328–361, 1984. View at Publisher · View at Google Scholar
  8. F. de Martino, F. Gentile, F. Esposito et al., “Calssification of fMRI independent components using IC-fingerprints and support vector machine classifiers,” NeuroImage, vol. 34, pp. 177–194, 2007. View at Publisher · View at Google Scholar
  9. R. A. Choudrey and S. J. Roberts, “Bayesian ICA with hidden Markov model sources ,” in in Proceedings of the International Conference on Independent Component Analysis (ICA), Nara, Japan, 2003.
  10. D. B. Rowe, “Bayesian source separation of fMRI signals,” in Proceedings of the 20th International Conference on Maximum Entropy and Bayesian Methods, A. Mohammad-Djafari, Ed., Gif sur Yvette, France, July 2000.
  11. B. A. Pearlmutter and L. C. Parra, “Maximum likelihood blind source separation: a context-sensitive generalization of ICA,” in Advances in Neural Information Processing Systems 9 (NIPS '97), M. C. Mozer, M. I. Jordan, and T. Petsche, Eds., pp. 613–619, The MIT Press, Denver, Colo, USA, December 1997.
  12. V. D. Calhoun, T. Adali, M. C. Stevens, K. A. Kiehl, and J. J. Pekar, “Semi-blind ICA of fMRI: a method for utilizing hypothesis-derived time courses in a spatial ICA analysis,” NeuroImage, vol. 25, no. 2, pp. 527–538, 2005. View at Publisher · View at Google Scholar
  13. J. S. Damoiseaux, S. A. R. B. Rombouts, F. Barkhof et al., “Consistent resting-state networks across healthy subjects,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 37, pp. 13848–13853, 2006. View at Publisher · View at Google Scholar
  14. V. G. van de Ven, E. Formisano, D. Prvulovic, C. H. Roeder, and D. E. J. Linden, “Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest,” Human Brain Mapping, vol. 22, no. 3, pp. 165–178, 2004. View at Publisher · View at Google Scholar
  15. M. J. Jafri and V. D. Calhoun, “Functional classification of schizophrenia using feed forward neural networks,” NeuroImage, vol. 22, pp. 1214–1222, 2004. View at Google Scholar
  16. D. Cordes, V. M. Haughton, K. Arfanakis et al., “Mapping functionally related regions of brain with functional connectivity MR imaging,” American Journal of Neuroradiology, vol. 21, no. 9, pp. 1636–1644, 2000. View at Google Scholar
  17. S. Achard, R. Salvador, B. Whitcher, J. Suckling, and E. Bullmore, “A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs,” Journal of Neuroscience, vol. 26, no. 1, pp. 63–72, 2006. View at Publisher · View at Google Scholar
  18. G. H. Glover, T.-Q. Li, and D. Ress, “Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR,” Magnetic Resonance in Medicine, vol. 44, no. 1, pp. 162–167, 2000. View at Publisher · View at Google Scholar
  19. A. J. Bell and T. J. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution,” Neural Computation, vol. 7, no. 6, pp. 1129–1159, 1995. View at Google Scholar
  20. L. Freire, A. Roche, and J. F. Mangin, “What is the best similarity measure for motion correction in fMRI time series?” IEEE Transactions on Medical Imaging, vol. 21, no. 5, pp. 470–484, 2001. View at Publisher · View at Google Scholar
  21. L. Freire and J.-F. Mangin, “Motion correction algorithms may create spurious brain activations in the absence of subject motion,” NeuroImage, vol. 14, no. 3, pp. 709–722, 2001. View at Publisher · View at Google Scholar
  22. K. J. Friston, J. Ashburner, C. D. Frith, J.-B. Poline, J. D. Heather, and R. S. J. Frackowiak, “Spatial registration and normalization of images,” Human Brain Mapping, vol. 3, no. 3, pp. 165–189, 1995. View at Publisher · View at Google Scholar
  23. Y.-O. Li, T. Adali, and V. D. Calhoun, “Estimating the number of independent components for fMRI data,” Human Brain Mapping.
  24. S. Z. Li, Markov Random Field Modeling in Computer Vision, Springer, Berlin, Germany, 1995.
  25. N. Correa, T. Adali, and V. D. Calhoun, “Performance of blind source separation algorithms for fMRI analysis using a group ICA method,” Magnetic Resonance Imaging, vol. 25, no. 5, pp. 684–694, 2007. View at Publisher · View at Google Scholar
  26. N. Correa, Performance of blind source separation algorithms for functional magnetic resonance imaging, M.S. thesis, University of Maryland Baltimore County, Baltimore, Md, USA, 2005.
  27. E. Seifritz, F. Esposito, F. Hennel et al., “Spatiotemporal pattern of neural processing in the human auditory cortex,” Science, vol. 297, no. 5587, pp. 1706–1708, 2002. View at Publisher · View at Google Scholar
  28. M. J. McKeown, L. K. Hansen, and T. J. Sejnowsk, “Independent component analysis of functional MRI: what is signal and what is noise?” Current Opinion in Neurobiology, vol. 13, no. 5, pp. 620–629, 2003. View at Publisher · View at Google Scholar
  29. T.-W. Lee, M. Girolami, and T. J. Sejnowski, “Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources,” Neural Computation, vol. 11, no. 2, pp. 417–441, 1999. View at Publisher · View at Google Scholar
  30. A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Networks, vol. 13, no. 4-5, pp. 411–430, 2000. View at Publisher · View at Google Scholar
  31. F. Esposito, T. Scarabino, A. Hyvärinen et al., “Independent component analysis of fMRI group studies by self-organizing clustering,” NeuroImage, vol. 25, no. 1, pp. 193–205, 2005. View at Publisher · View at Google Scholar
  32. M. Zibulevsky, P. Kisilev, Y. Y. Zeevi, and B. A. Pearlmutter, “Blind source separation via multinode sparse representation,” in Advances in Neural Information Processing Systems 14 (NIPS '01), pp. 1049–1056, The MIT Press, Vancouver, Canada, December 2001.
  33. T. Tanaka and A. Cichocki, “Subband decomposition independent component analysis and new performance criteria,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '04), vol. 5, pp. 541–544, Montreal, Canada, 2004. View at Publisher · View at Google Scholar
  34. W. D. Penny, N.J. Trujillo-Barreto, and K. J. Friston, “Bayesian fMRI time series analysis with spatial priors,” NeuroImage, vol. 24, no. 2, pp. 350–362, 2005. View at Publisher · View at Google Scholar
  35. M. W. Woolrich, T. E. J. Behrens, and S. M. Smith, “Constrained linear basis sets for HRF modelling using Variational Bayes,” NeuroImage, vol. 21, no. 4, pp. 1748–1761, 2004. View at Publisher · View at Google Scholar
  36. S.-I. Amari, T.-P. Chen, and A. Cichocki, “Stability analysis of learning algorithms for blind source separation,” Neural Networks, vol. 10, no. 8, pp. 1345–1351, 1997. View at Publisher · View at Google Scholar
  37. A. Hyvärinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Transactions on Neural Networks, vol. 10, no. 3, pp. 626–634, 1999. View at Publisher · View at Google Scholar