Non-stationary FWHM and its effect on statistical inference of fMRI data
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Non-stationary FWHM and its effect on statistical inference of fMRI data K.J. Worsley Department of Mathematics and Statistics, McGill University and McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University References Cao et al. (1999). Advances in Applied Probability , 31:577-593. Kiebel et al. (1999). NeuroImage, 10:756-766. Poline et al. (1996). Journal of Computer Assisted Tomography , 19:788-796. Taylor et al. (2001). NeuroImage, 13:S264. Worsley et al. (1999). Human Brain Mapping, 8:98-101. Worsley et al. (2002). NeuroImage, 15:1:15. Simulations Gaussian white noise was simulated on a 32 x 32 x 32 voxel grid and smoothed with an isotropic Gaussian kernel of FWHM varying from 0.5 to 10 voxels. This was repeated to give df independent copies. The FWHM was estimated using both methods, averaged over the volume, and compared to the true FWHM. Results: % Bias of FWHM Application to an fMRI data set 2.3 x 2.3 x 7 mm voxels, 6mm FWHM smoothing for motion correction, 118 scans (df = 112), 4 runs (df = 3). No correction Df correctio Continuity correction Df and continuity corre Conclusions • Df correction substantially reduces bias. • Continuity correction reduces bias for true FWHM < 3 • Corrected estimator is accurate to 2% if true FWHM > 2 voxels and df > 3 0 5 10 15 20 FW HM (m m ) over scans (448 df) 0 5 10 15 20 FW HM (m m ) over runs (3 df) 0 5 10 15 20 sm oothed FW HM overruns 0.5 1 1.5 sm oothed (runs FW HM /scans FW HM) Resels=1.90 P=0.007 Resels=0.57 P=0.387 Conclusions • FWHM ~ 6mm outside the brain as expected, both over scans and over runs • FWHM ~ 6mm in white matter, ~ 12mm in grey matter, 8.8mm brain average • Clusters of the same extent in white matter contain more resels and are much more significant than in grey matter (red circles) • FWHM is up to 50% larger over runs than over scans, indicating that activation is smoother (more extensive) than noise. -10 -5 0 5 10 df true FW H M (voxels) 10 20 30 2 4 6 8 10 -10 -5 0 5 10 df true FW H M (voxels) 10 20 30 2 4 6 8 10 -10 -5 0 5 10 df true FW H M (voxels) 10 20 30 2 4 6 8 10 -10 -5 0 5 10 df true FW H M (voxels) 10 20 30 2 4 6 8 10 Summary We propose an improved method for estimating the local FWHM or residual smoothness that corrects for two sources of bias encountered with fMRI or VBM data (but not usually PET data): • Low FWHM relative to voxel size, resulting in discontinuous spatial sampling. • Low residual degrees of freedom (df) from a mixed effects analyses for combining a small number of runs, sessions or subjects. To overcome the bias of the simple estimator of FWHM and resel density (=1/FWHM^3) based on numerical spatial derivatives of normalized linear model residuals (Kiebel et al., 1999) we propose: • A df correction based on the expectation of the simple estimator. • A continuity correction based on a Taylor series expansion of the bias in terms of the variance matrix of the numerical derivatives. The correction is fast and accurate to within 2% for FWHM > 2 voxels and df as low as 3. We then show how to account for this in statistical inference for T or F statistic images. more information: www.math.mcgill.ca/keith/fmristat
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Non-stationary FWHM and its effect on statistical inference of fMRI data K.J. Worsley Department of Mathematics and Statistics, McGill University and McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University. Summary - PowerPoint PPT Presentation
Transcript of Non-stationary FWHM and its effect on statistical inference of fMRI data
Non-stationary FWHM and its effect on statistical inference of fMRI data
K.J. WorsleyDepartment of Mathematics and Statistics, McGill University and
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University
ReferencesCao et al. (1999). Advances in Applied Probability, 31:577-593.Kiebel et al. (1999). NeuroImage, 10:756-766.Poline et al. (1996). Journal of Computer Assisted Tomography, 19:788-796.Taylor et al. (2001). NeuroImage, 13:S264.Worsley et al. (1999). Human Brain Mapping, 8:98-101.Worsley et al. (2002). NeuroImage, 15:1:15.
Simulations
Gaussian white noise was simulated on a 32 x 32 x 32 voxel grid and smoothed with an isotropic Gaussian kernel of FWHM varying from 0.5 to 10 voxels. This was repeated to give df independent copies. The FWHM was estimated using both methods, averaged over the volume, and compared to the true FWHM.
Results: % Bias of FWHM
Application to an fMRI data set2.3 x 2.3 x 7 mm voxels, 6mm FWHM smoothing for motion correction, 118 scans (df = 112), 4 runs (df = 3).
No correction Df correction
Continuity correction Df and continuity correction
Conclusions • Df correction substantially reduces bias.• Continuity correction reduces bias for true FWHM < 3 • Corrected estimator is accurate to 2% if true FWHM > 2 voxels and df > 3
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20FWHM (mm) over scans (448 df)
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20FWHM (mm) over runs (3 df)
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20smoothed FWHM over runs
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1.5smoothed (runs FWHM / scans FWHM)
Resels=1.90P=0.007
Resels=0.57P=0.387
Conclusions • FWHM ~ 6mm outside the brain as expected, both over scans and over runs• FWHM ~ 6mm in white matter, ~ 12mm in grey matter, 8.8mm brain average• Clusters of the same extent in white matter contain more resels and are much more significant than in grey matter (red circles)• FWHM is up to 50% larger over runs than over scans, indicating that activation is smoother (more extensive) than noise.
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SummaryWe propose an improved method for estimating the local FWHM or residual smoothness that corrects for two sources of bias encountered with fMRI or VBM data (but not usually PET data):
• Low FWHM relative to voxel size, resulting in discontinuous spatial sampling.• Low residual degrees of freedom (df) from a mixed effects analyses for combining a small number of runs, sessions or subjects.
To overcome the bias of the simple estimator of FWHM and resel density (=1/FWHM^3) based on numerical spatial derivatives of normalized linear model residuals (Kiebel et al., 1999) we propose:
• A df correction based on the expectation of the simple estimator.• A continuity correction based on a Taylor series expansion of the bias in terms of the variance matrix of the numerical derivatives.
The correction is fast and accurate to within 2% for FWHM > 2 voxels and df as low as 3. We then show how to account for this in statistical inference for T or F statistic images.
For more information: www.math.mcgill.ca/keith/fmristat
