Regarding the discussion on betas and % signal change, I'd be interested if
anyone had comments on the validity of looking at % signal change. I guess
I'm asking for comments as to why we (the community) use *statistical*
parametric maps, as opposed to *change* maps (like % signal change).
My vague impression (I'm confining my remarks to fMRI):
Pros: in the best possible world, there would be no noise. We could make
statements like "this task had a large effect on signal; this stimulus had a
small effect on signal". (Recall the point made in statistics texts that
you can have a statistically significant effect that is not important, in
that the amount of change induced is small---especially when the available
degrees of freedom is high.)
Cons: we live in a world where there is lots of noise. Hence, statistical
images are a necessity. Furthermore (at least for fMRI), drawing
conclusions about % signal change implies that there is some kind of zero
baseline (in statistical language, that signal is a ratio measure, not just
an interval measure); and this isn't so clear. (I'd especially appreciate
comments on this last point. One might make some argument that, by
linearizing each step in the path from neuronal activity to raw fMRI data,
there *is* a ratio scale here, but I'm not so convinced.)
Best wishes,
Stephen Fromm, PhD
NIDCD/NIH
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