A Neighborhood Method for Statistical Analysis of fMRI Data
Fayyaz Ahmad, Ghanim Ullah, Sung-Ho Kim
DOI: 10.4236/ojbiphy.2012.21003   PDF   HTML     4,961 Downloads   11,406 Views   Citations


In an effort to cope with the fact that functional magnetic resonance imaging (fMRI) data are spatiotemporally correlated, we propose a novel statistical method with a view to improve the detection of brain regions with increased neu-ronal activity in fMRI. In this method, we make use of information from neighboring voxels of a voxel, for estimation at the voxel. We examined performance of the method against the statistical parametric mapping (SPM) method using both simulated and real data. The proposed method is shown to be considerably better than the SPM in the context of receiver operating characteristics (ROC) curves.

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F. Ahmad, G. Ullah and S. Kim, "A Neighborhood Method for Statistical Analysis of fMRI Data," Open Journal of Biophysics, Vol. 2 No. 1, 2012, pp. 15-22. doi: 10.4236/ojbiphy.2012.21003.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] C. J. Lueck, S. Zeki, K. J. Friston, M. P. Deiber, P. Cope, V. J. Cunningham, A. A. Lammertsma, C. Kennard and R. S. Frackowiak, “The Colour Centre in the Cerebral Cortex of Man,” Nature, Vol. 340, No. 6232, 1989, pp. 386- 389. doi:10.1038/340386a0
[2] K. J. Friston, P. J. Jezzard and R. Turner, “Analysis of Functional MRI Time-Series,” Human Brain Mapping, Vol. 1, No. 2, 1994, pp. 153-171. doi:10.1002/hbm.460010207
[3] K. J. Worsley and K. J. Friston, “Analysis of fMRI Time- Series Revisited-Again,” NeuroImage, Vol. 2, No. 3, 1995, pp. 173-181. doi:10.1006/nimg.1995.1023
[4] K. J. Friston, A. P. Holmes, J. B. Poline, P. J. Grasby, S. C. Williams, R. S. Frackowiak and R. Turner, “Analysis of FMRI Time Series Revisited,” NeuroImage, Vol. 2, No. 3, 1995, pp. 45-53. doi:10.1006/nimg.1995.1007
[5] D. A. Harville, “Bayesian Inference for Variance Components Using Only Error Contrasts,” Biometrika, Vol. 61, No. 2, 1974, pp. 383-385. doi:10.1093/biomet/61.2.383
[6] P. A. Valdes-Sosa, “Spatio-Temporal Autoregressive Models Defined over Brain Manifolds,” Neuroinformatics, Vol. 2, No. 2, 2004, pp. 239-250. doi:10.1385/NI:2:2:239
[7] K. J. Worsley, “Non-Stationary FWHM and Its Effect on Statistical Inference for fMRI Data,” NeuroImage, Vol. 15, No. 346, 2002, pp. 779-790.
[8] P. L. Purdon, V. Solo, R. M. Weissko and E. Brown, “Locally Regularized Spatio-Tem Poral Modeling and Model Comparison for Functional MRI,” NeuroImage, Vol. 14, No. 4, 2001, pp. 912-923. doi:10.1006/nimg.2001.0870
[9] E. T. Bullmore, S. Rabe-Hesketh, R. G. Morris, C. R. Steven, L. Gregory, J. A. Gray and M. J. Brammer, “Functional Magnetic Resonance Image Analysis of a Large-Scale Neurocognitive Network,” NeuroImage, Vol. 1, No. 4, 1996, pp. 16-33. doi:10.1006/nimg.1996.0026
[10] N. R. Draper and H. Smith, “Applied Regression Analysis,” 3rd Edition, Wiley Academic Press, New York, 2003.
[11] B. Everitt and E. Bullmore, “Mixture Model Mapping of Brain Activation in Functional Magnetic Resonance Images,” Human Brain Mapping, Vol. 7, No. 1, 1999, pp. 1-14. doi:10.1002/(SICI)1097-0193(1999)7:1<1::AID-HBM1>3.0.CO;2-H
[12] G. S. Watson, “Serial Correlation in Regression Analysis,” Biometrika, Vol. 42, No. 3-4, 1955, pp. 327-341. doi:10.1093/biomet/42.3-4.327
[13] G. A. F. Seber, “Linear Regression Analysis,” Wiley Press, New York, 1977.
[14] F. E. Satterthwaite, “An Approximate Distribution of Estimates of Variance Components,” Biometrics, Vol. 2, No. 6, 1946, pp. 110-114. doi:10.2307/3002019
[15] A. Fayyaz, M. Maqbool and L. Namgill, “Regularization of Voxelwise Autoregressive Model for Analysis of Functional Magnetic Resonance Imaging Data,” Concepts in Magnetic Resonance Part A, Vol. 38, No. 5, 2011, pp. 187-196.
[16] H. Y. Kim, H. Yong and J. O. Giacomantone, “A New Technique to Obtain Clear Statistical Parametric Map by Applying Anisotropic Diffusion to FMRI,” ICIP, Vol.3, No. 5, 2005, pp. 724-727.
[17] H. Zhang, W. L. Luo and T. E. Nichols, “Diagnosis of Single-Subject and Group FMRI Data with SPMd,” Human Brain Mapping, Vol. 27, No. 5, 2006, pp. 442-451. doi:10.1002/hbm.20253
[18] P. A. Bandettini, A. Jesmanowicz, E. C. Wong and J. S. Hyde, “Processing Strategies for Time-Course Data sets in Functional MRI of the Human Brain,” Magnetic Resonance Medicine, Vol. 30, No. 2, 1993, pp. 161-173. doi:10.1002/mrm.1910300204
[19] S. J. Richard, Frackowiak, K. J. Friston, J. D. Raymond, J. P. Cathy and P. William, “The General Linear Model,” Human Brain Function, 2nd Edition, Willey Academic Press, Oxford, 2003.
[20] C. F. J. Wu, “On the Converengence Properties of the EM Algorithm,” The Annals of Statistics, Vol. 11, No. 1, 1983, pp. 95-103. doi:10.1214/aos/1176346060
[21] S. H. Kim, “Calibrated Intials for an EM Applied to Recursive Models of Categorical Variables,” Copmutational Statistical and Data Analysis, Vol. 1, No. 40, 2002, pp. 97-110. doi:10.1016/S0167-9473(01)00 105-0

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