Dear SPM Users and Authors:
I still have not heard back from anyone regarding my message from March
30, so I thought I would reiterate my problem and ask if anyone else has
ever tried viewing the images which SPM performs its statistics on and
whether or not they found the same random patterns of zeros as I did.
I tried for the first time recently to use the "generate normalized
image data" option in SPM which should allow me to generate
and save the images resulting from the application of the global
normalization, scaling and gray matter threshold (in the statistics
portion
of SPM). I used a multi-subject, different conditions (PET) design type
with 6 subjects and 2 conditions each (a drug and a placebo). I used
proportional scaling with a gray matter threshold of 0.8 and an
uncorrected F threshold of 0.99.
Upon viewing the resulting PET images, I discovered that they showed a
random pattern of voxels throughout the brain with values of zero, having
no relation to white or gray matter areas. These patterns were the same
for every one of the images generated (even across subjects). The
realigned, normalized and smoothed (at FWHM of 12x12x12) PET images I
entered into my design matrix had no such pattern/problem. Once again I
ask if anyone has observed such a phenomenon before and whether anyone
would know the cause. I am concerned as I am not sure why such patterns
would be generated and how SPM statistics
would handle these random zeros when performing its analyses.
I would be more than happy to ftp my pre and/or post statistical analysis
images to anyone who would be willing to take a look at them.

Thank you in advance for your help,
Leann Kinnunen
Department of Psychology
University of Chicago

Hi Leann,

It sounds like you are using MEDx. The normalized images correspond
to the data in the XA.mat file generated during statistical analysis
in matlab SPM.  The data in these image are the result of removing confounds, global normalization, and scaling.  Further, they are thresholded by the Gray Matter Threshold and masked by the F-map
after it has been thresholded based on the UFp cutoff.

I think you will find that if you lower the gray matter threshold
to about 0.10 and leave the UFp threshold at 0.99 that these images
will look more like the PET images that you started with.  Also,
you could use these images in the Eigen image analysis and you
should find that the first or second components match fairly
closely the major result from your statistical analysis.

Best Regards,
-John