Magnetic Resonance Imaging
Volume 27, Issue 10 , Pages 1382-1396 , December 2009

Bootstrap generation and evaluation of an fMRI simulation database

  • Pierre Bellec

      Affiliations

    • McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada H3A2B4
    • Corresponding Author InformationCorresponding author. Fax: +1 514 398 8948.
  • ,
  • Vincent Perlbarg

      Affiliations

    • Inserm, UMR S 678, Laboratoire d'Imagerie Fonctionnelle, F-75634 Paris, France
    • UPMC Univ Paris 06, UMR S 678, Laboratoire d'Imagerie Fonctionnelle, F-75634 Paris, France
  • ,
  • Alan C. Evans

      Affiliations

    • McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada H3A2B4

Received 29 November 2007 ,Revised 24 March 2009 ,Accepted 10 May 2009.

References 

  1. Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A. 1990;87:9868–9872
  2. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001;412:150–157
  3. Fox PT, Laird AR, Lancaster JL. Coordinate-based voxel-wise meta-analysis: dividends of spatial normalization. Report of a virtual workshop. Hum Brain Mapp. 2005;25:1–5
  4. Della-Maggiore V, Chau W, Peres-Neto PR, McIntosh AR. An empirical comparison of SPM preprocessing parameters to the analysis of fMRI data. Neuroimage. 2002;17:19–28
  5. Logan BR, Rowe DB. An evaluation of thresholding techniques in fMRI analysis. Neuroimage. 2004;22:95–108
  6. Marchini J, Presanis A. Comparing methods of analyzing fMRI statistical parametric maps. Neuroimage. 2004;22:1203–1213
  7. Baumgartner R, Ryner L, Richter W, Summers R, Jarmasz M, Somorjai R. Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis. Magn Reson Imaging. 2000;18:89–94
  8. Lu Y, Jiang T, Zang Y. Region growing method for the analysis of functional MRI data. Neuroimage. 2003;20:455–465
  9. Dimitriadou E, Barth M, Windischberger C, Hornik K, Moser E. A quantitative comparison of functional MRI cluster analysis. Artif Intell Med. 2004;31:57–71
  10. Worsley KJ, Chen JI, Lerch J, Evans AC. Comparing functional connectivity via thresholding correlations and singular value decomposition. Philos Trans R Soc Lond B Biol Sci. 2005;360:913–920
  11. Ardekani BA, Bachman AH, Helpern JA. A quantitative comparison of motion detection algorithms in fMRI. Magn Reson Imaging. 2001;19:959–963
  12. Freire L, Mangin JF. Motion correction algorithms may create spurious brain activations in the absence of subject motion. Neuroimage. 2001;14:709–722
  13. Pickens DR, Li Y, Morgan VL, Dawant BM. Development of computer-generated phantoms for fMRI software evaluation. Magn Reson Imaging. 2005;23:653–663
  14. Morgan VL, Dawant BM, Li Y, Pickens DR. Comparison of fMRI statistical software packages and strategies for analysis of images containing random and stimulus-correlated motion. Comput Med Imaging Graph. 2007;31:436–446
  15. Honey CJ, Kotter R, Breakspear M, Sporns O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci U S A. 2007;104:10240–10245
  16. Ghosh A, Rho Y, McIntosh AR, Kotter R, Jirsa VK. Cortical network dynamics with time delays reveals functional connectivity in the resting brain. Cogn Neurodyn. 2008;2:115–120
  17. Buxton RB, Wong EC, Frank LR. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn Reson Med. 1998;39:855–864
  18. Aubert A, Costalat R. A model of the coupling between brain electrical activity, metabolism, and hemodynamics: application to the interpretation of functional neuroimaging. Neuroimage. 2002;17:1162–1181
  19. Drobnjak I, Gavaghan D, Süli E, Pitt-Francis J, Jenkinson M. Development of a functional magnetic resonance imaging simulator for modeling realistic rigid-body motion artifacts. Magn Reson Med. 2006;56:364–380
  20. Baumgartner R, Windischberger C, Moser E. Quantification in functional magnetic resonance imaging: Fuzzy clustering vs. correlation analysis. Magn Reson Imaging. 1998;16:115–125
  21. Beckmann CF, Smith SM. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging. 2004;23:137–152
  22. Bellec P, Perlbarg V, Jbabdi S, Pélégrini-Issac M, Anton J, Doyon J, et al. Identification of large-scale networks in the brain using fMRI. Neuroimage. 2006;29:1231–1243
  23. Bullmore E, Long C, Suckling J, Fadili J, Calvert G, Zelaya F, et al. Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains. Hum Brain Mapp. 2001;12:61–78
  24. Jahanian H, Hossein-Zadeh GA, Soltanian-Zadeh H, Ardekani BA. Controlling the false positive rate in fuzzy clustering using randomization: application to fMRI activation detection. Magn Reson Imaging. 2004;22:631–638
  25. Xu Y, Wu G, Rowe DB, Ma Y, Zhang R, Xu G, et al. Complex-model-based estimation of thermal noise for fMRI data in the presence of artifacts. Magn Reson Imaging. 2007;25:1079–1088
  26. Kim B, Yeo DT, Bhagalia R. Comprehensive mathematical simulation of functional magnetic resonance imaging time series including motion-related image distortion and spin saturation effect. Magn Reson Imaging. 2008;26:147–159
  27. Sorenson JA, Wang X. ROC methods for evaluation of fMRI techniques. Magn Reson Med. 1996;36:737–744
  28. Shao J, Tu D. The Jackknife and Bootstrap. New York: Springer; 1995;
  29. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. New York/London: Chapman & Hall/CRC; 1994;
  30. Perlbarg V, Bellec P, Anton JL, Pélégrini-Issac M, Doyon J, Benali H. CORSICA: correction of structured noise in fMRI by automatic identification of ICA components. Magn Reson Imaging. 2007;25:35–46
  31. Stein C. Inadmissibility of the usual estimator for the mean of a multivariate normal distribution. In: Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability. Vol. 1:Los Angeles: University of California Press; 1956;p. 197–206
  32. Lund TE, Madsen KH, Sidaros K, Luo WL, Nichols TE. Non-white noise in fMRI: does modelling have an impact?. Neuroimage. 2006;29:54–66
  33. Bellec P, Marrelec G, Benali H. A bootstrap test to investigate changes in brain connectivity for functional MRI. Stat Sin. 2008;18:1253–1268
  34. Luo WL, Nichols TE. Diagnosis and exploration of massively univariate neuroimaging models. Neuroimage. 2003;19:1014–1032
  35. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A. 2003;100:253–258
  36. De Luca M, Beckmann CF, De Stefano N, Matthews PM, Smith SM. fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage. 2006;29:1359–1367
  37. Riera JJ, Wan X, Jimenez JC, Kawashima R. Nonlinear local electrovascular coupling. I: a theoretical model. Hum Brain Mapp. 2006;27:896–914
  38. Riera JJ, Jimenez JC, Wan X, Kawashima R, Ozaki T. Nonlinear local electrovascular coupling. II: from data to neuronal masses. Hum Brain Mapp. 2007;28:335–354
  39. Boada F, Collins D, Drobnjak EW, Evans A, Griffin M, Jenkinson M, et al. MIDAS — a multi-site fMRI simulator consortium. In: Tenth Int Conf on Functional Mapping of the Human Brain. 2004;
  40. Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized talairach space. J Comput Assist Tomogr. 1994;18:192–205
  41. McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, Bell AJ, et al. Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp. 1998;6:160–188
  42. Thomas CG, Harshman RA, Menon RS. Noise reduction in BOLD-based fMRI using component analysis. Neuroimage. 2002;17:1521–1537
  43. Bell AJ, Sejnowski TJ. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 1995;7:1129–1159
  44. Judge GG, Hill CR, Bock ME. An adaptive empirical Bayes estimator of the multivariate normal mean under quadratic loss. J Econom. 1990;44:189–213

PII: S0730-725X(09)00121-0

doi: 10.1016/j.mri.2009.05.034

Magnetic Resonance Imaging
Volume 27, Issue 10 , Pages 1382-1396 , December 2009