Hello,
I get slightly different results depending upon which of two approaches I use
to denoise data.
Approach 1: Use a 4D fMRI (preprocessed) image and the time course of a
presumed noise source with fsl_regfilt to produce a denoised 4D image.
Regress the denoised image against a task-related time course with FEAT to
yield spatial maps of PE values and z-scores corresponding to task-related
brain activation.
Approach 2: Regress the first 4D image against the task-related time course
with FEAT, adding the noise source time course to the EVs, but not to the
Contrasts & F-tests (Its EV gets a zero).
Both approaches yield similar results and both appear to improve the
sensitivity for detecting activation, but the corresponding spatial map voxel
values generally differ up or down by amounts of about 1%, varying from
nearly zero differences to differing by a few percent for some voxels.
I expected some slight differences in results due to differences in degrees of
freedom or rounding errors, but I did not expect the differences to be this
large. Do you have any idea what might account for the differences?
Cheers,
Robert
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