We want to compute the spatial correlation between two volumes, one with relatively low spatial smoothness and another one with high spatial smoothness since it comes from a template of RS networks (from FSL). The volume of the RS networks has several sub-bricks.
We would like to match the smoothness of the volumes before computing the spatial correlation. How can we do it? We thought of using a combination of 3dFWHMx (with the -out), then estimating the average FWHM value, and then 3dBlurToFWHM using -blurmaster and the estimated FWHM as -FWHM. However, we don't know whether this is the correct approach.
That sounds reasonable. The help says the errts time series dataset is preferable, but that makes it an iterative procedure. I've tried in the past just with the input dataset (the default), and it worked well for comparing data across sites. From the help of 3dBlurToFWHM:
FILE RECOMMENDATIONS for -blurmaster:
For FMRI statistical purposes, you DO NOT want the FWHM to reflect
the spatial structure of the underlying anatomy. Rather, you want
the FWHM to reflect the spatial structure of the noise. This means
that the -blurmaster dataset should not have anatomical structure. One
good form of input is the output of '3dDeconvolve -errts', which is
the residuals left over after the GLM fitted signal model is subtracted
out from each voxel's time series. You can also use the output of
'3dREMLfit -Rerrts' or '3dREMLfit -Rwherr' for this purpose.
You CAN give a multi-brick EPI dataset as the -blurmaster dataset; the
dataset will be detrended in time (like the -detrend option in 3dFWHMx)
which will tend to remove the spatial structure. This makes it
practicable to make the input and blurmaster datasets be the same,
without having to create a detrended or residual dataset beforehand.
Considering the accuracy of blurring estimates, this is probably good
enough for government work [that is an insider's joke :-].
N.B.: Do not use catenated runs as blurmasters. There should
be no discontinuities in the time axis of blurmaster, which would
make the simple regression detrending do peculiar things.
The
National Institute of Mental Health (NIMH) is part of the National Institutes of
Health (NIH), a component of the U.S. Department of Health and Human
Services.