Dear SPM:
I'm running a set of analyses in which I'm removing certain
covariates of no interest from a dataset- specifically movement
parameters and a set of sign and cosign function which effect a
low-pass filter.
When I run the analysis in spm97 I get about a 100 dB signal
reduction in the high frequencies above the filter cut-off point
(assessed by looking at the power spectrum of the adjusted data);
however in spm99 the same covariates produce only about a 20 dB
reduction.
Why? and how can I get better results out of spm99?
I've looked at the covariates in the G matrix for SPM96/7 and xX.X
for spm99 and they look exactly the same, so I'm guessing this has
something to do with how the convolution matrix is implemented
differently. But that's just a guess.
By the way the analysis uses scaling to adjust global signal, but
otherwise no highpass or lowpass filters (via spm99, I put in my own
lowpass filter as above), or AR adjustment to the data. Basically I'm
just trying to use spm99 as a general linear engine to remove some
covariates of no interest. I liked the results out of SPM96/7 because
of the great signal reduction in the high frequencies, but I thought
I'd move to spm99 for other advantages in display.
help.
Darren
Darren R. Gitelman, M.D.
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