Dear SPMers,
We are encountering the somewhat puzzling and annoying problem that the
bounding box size we specify for our normalized images determines the
extent of significant activations we observe.
We have taken the EPI data of a single subject and normalized it using 4
different bounding boxes:
The SPM99 default: -78:78 -112:76 -50:85
The SPM99 template: -90:91 -126:91 -72:109
SPM99 option 4: [-95:95 -112:76 -50:95]
A bounding box which returns ~the original volume (64x64x27) of our data:
[-120:119 -139:100 -64:77]
We were careful to make sure that our bounding box did not crop the brain
any more than any of the SPM bounding boxes.
We made the voxel size of the resulting normalized images the same as the
voxel size of the original data: 3.75 x 3.75 x 5.
We apply identical smoothing kernels ([6 6 8]) to each of the normalized
images, run it through our design matrix, etc., and plot the same
contrasts. The t-images differ **substantially** as a function of the
bounding box. When we use a bounding box which is the same as our
original data, we end up with one (1) significant voxel. When we use the
smallest of the bounding boxes (SPM99 default) we end up with 1461
significant voxels. The other bounding boxes yield intermediate numbers
of significant voxels as a function of bounding box size.
Given the same voxel size, same normalization parameters, etc., it
seems the volume of the brain (suprathreshold voxels) should be the
same independently of bounding box size. Why then does the bounding box
size have such a profound effect on the statistics?
This is a particular problem for us for the following two reasons:
1) In the interest of performing a statistical analysis over a group of
subjects, we normalize each individual's EPI images so that they can be
compared across subjects. However, we are also interested in looking at
the data of individual subjects on their individual high-res T1 images
because we have reason to expect significant topographical differences in
activation patterns. However, the bounding box problem prevents us from
doing this because the unnormalized image size is 64x64x27 whereas the
normalized (with bounding box 'Template') is 49x58x37 and the resulting
significance maps cannot be compared.
Should we crop our unnormalized images so that they have approximately the
same bounding box as the normalized images?
2) As mentioned above, it is disconcerting that the seemingly innocuous
and irrelevant bounding box parameter should be one of the strongest
determinants of finding an effect in the data.
Thanks in advance for clarification!
Petr Janata
Barbara Tillmann
--------------------------------------------
Petr Janata, Ph.D.
Research Associate
Dept. of Psychological and Brain Sciences
Dartmouth College
6207 Moore Hall
Hanover NH, 03755
phone: 603 646 0062
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