Bootstrap generation and evaluation of an fMRI simulation database

Magnetic Resonance Imaging

Volumn 27, Issue 10, 2009, Pages 1382-1396

  Bellec, Pierre ^a   Perlbarg, Vincent ^b,c   Evans, Alan C ^a  

^a MCGILL UNIVERSITY   (Canada)

^b UPMC Univ Paris 06   (France)

^c SORBONNE UNIVERSITÉ   (France)

Author keywords

Bootstrap; Evaluation; fMRI; Motion; Parametric model; Simulation

Indexed keywords

ADULT; ARTICLE; BOOTSTRAPPING; BRAIN FUNCTION; BRAIN SIZE; COMPUTER SIMULATION; CONTROLLED STUDY; DATA BASE; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; HUMAN EXPERIMENT; MALE; NONLINEAR SYSTEM; NORMAL HUMAN; PARAMETRIC TEST; PRIORITY JOURNAL; SIGNAL NOISE RATIO; SPATIAL AUTOCORRELATION ANALYSIS; TASK PERFORMANCE;

ADULT; BRAIN MAPPING; COMPUTER SIMULATION; DATABASES, FACTUAL; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; MODELS, THEORETICAL; MONTE CARLO METHOD; NORMAL DISTRIBUTION; TIME FACTORS;

EID: 70449525316     PISSN: 0730725X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.mri.2009.05.034     Document Type: Article

References (44)

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