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International Journal of Biomedical Imaging
Volume 2011, Article ID 627947, 15 pages
http://dx.doi.org/10.1155/2011/627947
Research Article

Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI Analysis

1Division of Medical Informatics, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
2Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden

Received 19 April 2011; Accepted 14 July 2011

Academic Editor: Yasser M. Kadah

Copyright © 2011 Anders Eklund 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. H. Gudbjartsson and S. Patz, “The Rician distribution of noisy MRI data,” Magnetic Resonance in Medicine, vol. 34, no. 6, pp. 910–914, 1995. View at Publisher · View at Google Scholar · View at Scopus
  2. W. L. Luo and T. E. Nichols, “Diagnosis and exploration of massively univariate neuroimaging models,” NeuroImage, vol. 19, no. 3, pp. 1014–1032, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. O. Friman, I. Morocz, and C.-F. Westin, “Examining the whiteness of fMRI noise,” in Proceedings of the ISMRM Annual Meeting, p. 699, 2005.
  4. T. E. Lund, K. H. Madsen, K. Sidaros, W. L. Luo, and T. E. Nichols, “Non-white noise in fMRI: does modelling have an impact?” NeuroImage, vol. 29, no. 1, pp. 54–66, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. A. M. Wink and J. B. T. M. Roerdink, “BOLD noise assumptions in fMRI,” International Journal of Biomedical Imaging, vol. 2006, Article ID 12014, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. K. J. Friston, O. Josephs, E. Zarahn, A. P. Holmes, S. Rouquette, and J. B. Poline, “To smooth or not to smooth? Bias and efficiency in fMRI time-series analysis,” NeuroImage, vol. 12, no. 2, pp. 196–208, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. 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
  8. O. Friman, J. Cedefamn, P. Lundberg, M. Borga, and H. Knutsson, “Detection of neural activity in functional MRI using canonical correlation analysis,” Magnetic Resonance in Medicine, vol. 45, no. 2, pp. 323–330, 2001. View at Publisher · View at Google Scholar · View at Scopus
  9. O. Friman, M. Borga, P. Lundberg, and H. Knutsson, “Adaptive analysis of fMRI data,” NeuroImage, vol. 19, no. 3, pp. 837–845, 2003. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Nandy and D. Cordes, “A novel nonparametric approach to canonical correlation analysis with applications to low CNR functional MRI data,” Magnetic Resonance in Medicine, vol. 49, pp. 1152–1162, 2003. View at Google Scholar
  11. J. Mourão-Miranda, A. L. W. Bokde, C. Born, H. Hampel, and M. Stetter, “Classifying brain states and determining the discriminating activation patterns: support Vector Machine on functional MRI data,” NeuroImage, vol. 28, no. 4, pp. 980–995, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. N. Kriegeskorte, R. Goebel, and P. Bandettini, “Information-based functional brain mapping,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 10, pp. 3863–3868, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. F. D. Martino, G. Valente, N. Staeren, J. Ashburner, R. Goebel, and E. Formisano, “Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns,” NeuroImage, vol. 43, no. 1, pp. 44–58, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. M. B. Åberg and J. Wessberg, “An evolutionary approach to the identification of informative voxel clusters for brain state discrimination,” IEEE Journal on Selected Topics in Signal Processing, vol. 2, no. 6, pp. 919–928, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Hochberg and A. C. Tamhane, Multiple Comparison Procedures, John Wiley & Sons, New York, NY, USA, 1987.
  16. S. Siegel, “Nonparametric statistics,” The American Statistician, vol. 11, pp. 13–19, 1957. View at Google Scholar
  17. A. P. Holmes, R. C. Blair, J. D. G. Watson, and I. Ford, “Nonparametric analysis of statistic images from functional mapping experiments,” Journal of Cerebral Blood Flow & Metabolism, vol. 16, no. 1, pp. 7–22, 1996. View at Google Scholar · View at Scopus
  18. E. Bullmore, M. Brammer, S. C. R. Williams et al., “Statistical methods of estimation and inference for functional MR image analysis,” Magnetic Resonance in Medicine, vol. 35, no. 2, pp. 261–277, 1996. View at Publisher · View at Google Scholar · View at Scopus
  19. J. J. Locascio, P. J. Jennings, C. I. Moore, and S. Corkin, “Time series analysis in the time domain and resampling methods for studies of functional magnetic resonance brain imaging,” Human Brain Mapping, vol. 5, no. 3, pp. 168–193, 1997. View at Publisher · View at Google Scholar · View at Scopus
  20. M. J. Brammer, E. T. Bullmore, A. Simmons et al., “Generic brain activation mapping in functional magnetic resonance imaging: a nonparametric approach,” Magnetic Resonance Imaging, vol. 15, no. 7, pp. 763–770, 1997. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Belmonte and D. Yurgelun-Todd, “Permutation testing made practical for functional magnetic resonance image analysis,” IEEE Transactions on Medical Imaging, vol. 20, no. 3, pp. 243–248, 2001. View at Publisher · View at Google Scholar · View at Scopus
  22. E. Bullmore, C. Long, J. Suckling et al., “Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains,” Human Brain Mapping, vol. 12, no. 2, pp. 61–78, 2001. View at Publisher · View at Google Scholar · View at Scopus
  23. T. E. Nichols and A. P. Holmes, “Nonparametric permutation tests for functional neuroimaging: a primer with examples,” Human Brain Mapping, vol. 15, no. 1, pp. 1–25, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. T. Nichols and S. Hayasaka, “Controlling the familywise error rate in functional neuroimaging: a comparative review,” Statistical Methods in Medical Research, vol. 12, no. 5, pp. 419–446, 2003. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Hayasaka and T. E. Nichols, “Combining voxel intensity and cluster extent with permutation test framework,” NeuroImage, vol. 23, no. 1, pp. 54–63, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Breakspear, M. J. Brammer, E. T. Bullmore, P. Das, and L. M. Williams, “Spatiotemporal wavelet resampling for functional neuroimaging data,” Human Brain Mapping, vol. 23, no. 1, pp. 1–25, 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. O. Friman and C. F. Westin, “Resampling fMRI time series,” NeuroImage, vol. 25, no. 3, pp. 859–867, 2005. View at Publisher · View at Google Scholar · View at Scopus
  28. L. Tillikainen, E. Salli, A. Korvenoja, and H. J. Aronen, “A cluster mass permutation test with contextual enhancement for fMRI activation detection,” NeuroImage, vol. 32, no. 2, pp. 654–664, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. R. Nandy and D. Cordes, “A semi-parametric approach to estimate the family-wise error rate in fMRI using resting-state data,” NeuroImage, vol. 34, no. 4, pp. 1562–1576, 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Eklund, M. Andersson, and H. Knutsson, “fMRI analysis on the GPU-possibilities and challenges,” Computer Methods and Programs in Biomedicine. In press. View at Publisher · View at Google Scholar
  31. D. Gembris, M. Neeb, M. Gipp, A. Kugel, and R. Männer, “Correlation analysis on GPU systems using NVIDIA's CUDA,” Journal of Real-Time Image Processing, pp. 1–6, 2010. View at Publisher · View at Google Scholar
  32. A. Eklund, O. Friman, M. Andersson, and H. Knutsson, “A GPU accelerated interactive interface for exploratory functional connectivity analysis of fMRI data,” in Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 1621–1624, 2011.
  33. A. R. Ferreira da Silva, “A bayesian multilevel model for fMRI data analysis,” Computer Methods and Programs in Biomedicine, vol. 102, pp. 238–252, 2011. View at Google Scholar
  34. I. Shterev, S.-H. Jung, S. George, and K. Owzar, “permGPU: using graphics processing units in RNA microarray association studies,” BMC Bioinformatics, vol. 11, p. 329, 2010. View at Google Scholar
  35. J. L. V. Hemert and J. A. Dickerson, “Monte Carlo randomization tests for large-scale abundance datasets on the GPU,” Computer Methods and Programs in Biomedicine, vol. 101, no. 1, pp. 80–86, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. K. J. Friston, P. Jezzard, and R. Turner, “Analysis of functional MRI time-series,” Human Brain Mapping, vol. 1, no. 2, pp. 153–171, 1993. View at Google Scholar · View at Scopus
  37. K. J. Friston, A. P. Holmes, K. J. Worsley, J. P. Poline, C. D. Frith, and R. S. J. Frackowiak, “Statistical parametric maps in functional imaging: a general linear approach,” Human Brain Mapping, vol. 2, no. 4, pp. 189–210, 1994. View at Google Scholar · View at Scopus
  38. S. J. Kiebel, J. B. Poline, K. J. Friston, A. P. Holmes, and K. J. Worsley, “Robust smoothness estimation in statistical parametric maps using standardized residuals from the general linear model,” NeuroImage, vol. 10, no. 6, pp. 756–766, 1999. View at Publisher · View at Google Scholar · View at Scopus
  39. R. S. Frackowiak, K. Friston, and C. Frith, Human Brain Function, Academic Press, New York, NY, USA, 2004.
  40. M. Dwass, “Modified randomization tests for nonparametric hypotheses,” The Annals of Mathematical Statistics, vol. 28, pp. 181–187, 1957. View at Google Scholar
  41. A. M. Smith, B. K. Lewis, U. E. Ruttimann et al., “Investigation of low frequency drift in fMRI signal,” NeuroImage, vol. 9, no. 5, pp. 526–533, 1999. View at Publisher · View at Google Scholar · View at Scopus
  42. O. Friman, M. Borga, P. Lundberg, and H. Knutsson, “Detection and detrending in fMRI data analysis,” NeuroImage, vol. 22, no. 2, pp. 645–655, 2004. View at Publisher · View at Google Scholar · View at Scopus
  43. A. R. Laird, B. P. Rogers, and M. E. Meyerand, “Comparison of Fourier and wavelet resampling methods,” Magnetic Resonance in Medicine, vol. 51, no. 2, pp. 418–422, 2004. View at Publisher · View at Google Scholar · View at Scopus
  44. T. Gautama and M. M. Van Hulle, “Optimal spatial regularisation of autocorrelation estimates in fMRI analysis,” NeuroImage, vol. 23, no. 3, pp. 1203–1216, 2004. View at Publisher · View at Google Scholar · View at Scopus
  45. B. Lenoski, L. C. Baxter, L. J. Karam, J. Maisog, and J. Debbins, “On the performance of autocorrelation estimation algorithms for fMRI analysis,” IEEE Journal on Selected Topics in Signal Processing, vol. 2, no. 6, pp. 828–838, 2008. View at Publisher · View at Google Scholar · View at Scopus
  46. H. Knutsson and C.-F. Westin, “Normalized and differential convolution: methods for interpolation and filtering of incomplete and uncertain data,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 515–523, June 1993. View at Scopus
  47. G. M. Ljung and G. E. P. Box, “On a measure of lack of fit in time series models,” Biometrika, vol. 65, no. 2, pp. 297–303, 1978. View at Google Scholar · View at Scopus
  48. H. Hotelling, “Relation between two sets of variates,” Biometrika, vol. 28, pp. 322–377, 1936. View at Google Scholar
  49. T. K. Nguyen, A. Eklund, H. Ohlsson et al., “Concurrent volume visualization of real-time fMRI,” in Proceedings of the 8th IEEE/EG International Symposium on Volume Graphics, pp. 53–60, Norrköping, Sweden, May 2010.
  50. A. Constantine, “Some non-central distribution problems in multivariate analysis,” Annals of Mathematical Statistics, vol. 34, pp. 1270–1285, 1963. View at Google Scholar
  51. S. Das and P. K. Sen, “Restricted canonical correlations,” Linear Algebra and Its Applications, vol. 210, no. C, pp. 29–47, 1994. View at Google Scholar · View at Scopus
  52. O. Friman, “Subspace models for functional MRI data analysis,” in Proceedings of the 2nd IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1–4, April 2004. View at Scopus
  53. D. S. Moore, G. P. McCabe, and B. A. Craig, Introduction to the Practice of Statistics, W. H. Freeman & Company, 2007.
  54. Nvidia, CUDA Programming Guide, Version 4.0, 2010.
  55. D. Kirk and W. Hwu, Programming Massively Parallel Processors, A Hands on Approach, Morgan Kaufmann, 2010.
  56. M. Ragnehed, M. Engström, H. Knutsson, B. Söderfeldt, and P. Lundberg, “Restricted canonical correlation analysis in functional MRI-validation and a novel thresholding technique,” Journal of Magnetic Resonance Imaging, vol. 29, no. 1, pp. 146–154, 2009. View at Publisher · View at Google Scholar · View at Scopus
  57. R. Viviani, P. Beschoner, K. Ehrhard, B. Schmitz, and J. Thöne, “Non-normality and transformations of random fields, with an application to voxel-based morphometry,” NeuroImage, vol. 35, no. 1, pp. 121–130, 2007. View at Publisher · View at Google Scholar · View at Scopus
  58. E. T. Bullmore, J. Suckling, S. Overmeyer, S. Rabe-Hesketh, E. Taylor, and M. J. Brammer, “Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain,” IEEE Transactions on Medical Imaging, vol. 18, no. 1, pp. 32–42, 1999. View at Google Scholar · View at Scopus