About this Journal Submit a Manuscript Table of Contents
Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 967380, 14 pages
http://dx.doi.org/10.1155/2012/967380
Research Article

A Computationally Efficient, Exploratory Approach to Brain Connectivity Incorporating False Discovery Rate Control, A Priori Knowledge, and Group Inference

1Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4
2Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095, USA
3Division of Neurology, Department of Medicine and Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canada V5Z 1M9

Received 18 March 2012; Revised 6 July 2012; Accepted 10 July 2012

Academic Editor: Tianzi Jiang

Copyright © 2012 Aiping Liu 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. S. M. Smith, K. L. Miller, G. Salimi-Khorshidi et al., “Network modelling methods for FMRI,” NeuroImage, vol. 54, no. 2, pp. 875–891, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. K. J. Friston, “Functional and effiective connectivity: a review,” Brain Connectivity, vol. 1, no. 1, pp. 13–36, 2011. View at Publisher · View at Google Scholar
  3. A. R. McIntosh and F. Gonzalez-Lima, “Structural equation modeling and its application to network analysis in functional brain imaging,” Human Brain Mapping, vol. 2, no. 1-2, pp. 2–22, 1994. View at Scopus
  4. K. J. Friston, L. Harrison, and W. Penny, “Dynamic causal modelling,” NeuroImage, vol. 19, no. 4, pp. 1273–1302, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Li, Z. J. Wang, J. J. Eng, and M. J. McKeown, “Bayesian network modeling for discovering “dependent synergies” among muscles in reaching movements,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 1, pp. 298–310, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Benjamini and D. Yekutieli, “The control of the false discovery rate in multiple testing under dependency,” Annals of Statistics, vol. 29, no. 4, pp. 1165–1188, 2001. View at Publisher · View at Google Scholar · View at Scopus
  7. J. D. Storey, “A direct approach to false discovery rates,” Journal of the Royal Statistical Society B, vol. 64, no. 3, pp. 479–498, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Listgarten and D. Heckerman, “Determining the number of non-spuriousarcs in a learned DAG model: investigation of a Bayesian and a frequentist approach,” in Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, 2007.
  9. I. Tsamardinos and L. E. Brown, “Bounding the false discovery rate in local bayesian network learning,” in Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI '08), pp. 1100–1105, July 2008. View at Scopus
  10. P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction, and Search, The MIT Press, 2001.
  11. J. Li and Z. J. Wang, “Controlling the false discovery rate of theassociation/causality structure learned with the pc algorithm,” Journal of Machine Learning Research, vol. 10, pp. 475–514, 2009.
  12. K. J. Worsley, C. H. Liao, J. Aston et al., “A general statistical analysis for fMRI data,” NeuroImage, vol. 15, no. 1, pp. 1–15, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. K. J. Friston, K. E. Stephan, T. E. Lund, A. Morcom, and S. Kiebel, “Mixed-effects and fMRI studies,” NeuroImage, vol. 24, no. 1, pp. 244–252, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. K. E. Stephan, W. D. Penny, J. Daunizeau, R. J. Moran, and K. J. Friston, “Bayesian model selection for group studies,” NeuroImage, vol. 46, no. 4, pp. 1004–1017, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Varoquaux, A. Gramfort, J. B. Poline, and B. Thirion, “Brain covariance selection: better individual functional connectivity models using population prior,” Advances in Neural Information Processing Systems, vol. 23, pp. 2334–2342, 2010.
  16. J. D. Ramsey, S. J. Hanson, and C. Glymour, “Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study,” NeuroImage, vol. 58, no. 3, pp. 838–848, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. S. L. Lauritzen, Graphical Models, Oxford University Press, 1996.
  18. J. Neyman and E. S. Pearson, “On the use and interpretation of certaintest criteria for purposes of statistical inference: part I,” Biometrika, vol. 20A, pp. 175–240, 1928.
  19. R. A. Fisher, “Frequency distribution of the values of the correlation 40 coefficients in samples from an indefinitely large population,” Biometrika, vol. 10, no. 4, pp. 507–521, 1915.
  20. S. J. Palmer, B. Ng, R. Abugharbieh, L. Eigenraam, and M. J. McKeown, “Motor reserve and novel area recruitment: amplitude and spatial characteristics of compensation in Parkinson's disease,” European Journal of Neuroscience, vol. 29, no. 11, pp. 2187–2196, 2009. View at Publisher · View at Google Scholar · View at Scopus