Hello !
Concerning the use of Ancova or Scaling in normalization for fMRI time-series, I
observed that both of them could lead to some strange effects when analyzing a very
stable time-series where the main effect (as assessed by Eigenimages analysis) is
related to the paradigm.
In this particular case, I was analyzing a simple Control / Activation study (with
C/A/C/A/C/A/C/A/C/A). With scaling, I observed some significant "deactivations" (C-A
contrast), that were not observed with Ancova. When reanalyzing the data without any
normalization, I observed no deactivations, but more significant activations (A-C
contrast) than in the Ancova or the Scaling analysis.
It indeed seems logical that if the time-series is very stable, using Scaling will
produce artifactual deactivations, simply because the mean of each image of the
time-series is significantly affected by the increase in signal brought about by
strong activation in several voxels. Using Ancova actually produced a function of no
interest that was very similar to my paradigm, and I think this is why activations
were weaker in this analysis than in the no-normalization analysis.
So, my question finally is : is it not better to first analyze the data without any
normalization to assess with eigenimages if there is or not in the data a major
global signal change unrelated to the paradigm that should be accounted for by
normalization ? If there is none, analysis without any kind of normalization is
probably the best.
Thanks in advance,
Elie Lobel
Service Hospitalier Frederic Joliot
Orsay, France
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