> 
> 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
> 


Dear Elie,

your question pertains to the special, and rather problematic, case
where changes in your global signal is almost entirely caused by
changes on local signal (focal activations) and therfore highly
corelated to the latter. As you correctly points out entering this
signal as a confound (be it through proportional scaling or ANCOVA) may
lead to cancellation of some of your local signal and, more seriously,
to false "deactivations".

The reason for the global normalisation in the first place was the
observation that it accounted for unwanted scan-scan variance due to
differences in injected activity, pCO2 level, arousal level etc. in the
early PET days. The part of the global variance that were actually
attributable to local variance was so relatively small that the problem
you describe was not initially observed.

In the best of worlds, which we are now with the refinement of
neuroimaging techniques approaching, all change in global variance
should be attributable to change in local activity and there would be
no need for global normalisation. There are still sources of
variability such as drifts in the MR scanner and changes in pCO2
levels, but if we assume that these occurr at a slower time scale than
our experimental paradigm the low frequency components of the fMRI
design matrix should take care of those.

Hence, one might actually make a case for the abolishment of global
normalisation for fMRI, or at least for some care in it's use,
especially looking out for any correlations between global values and
experimental design. Clearly, as you suggest, a PCA on the unadjusted
data might give useful clues as to whether global normalisation is
beneficial or not.

If one still wants to use global normalisation on a data set such as
that described by you a method has been suggested for dealing with your
specific problem. Basically it circumvents the problem by assuming that
any areas that are seen as activated in a first analysis step are
potential sources of bias when estimating global activity, and another
analysis is performed where these areas are excluded when calculating
global activity. This process may be repeated iteratively until no
changes occurr in subsequent calculations of global activity, at which
stage the estimates are presumably unbiased. You may find a full
description of the method in Andersson JLR, How to estimate global
activity independent of changes in local activity, NeuroImage 1997:
4;237-244.

Other methods of dealing with this problem has been suggested, and
hopefully they will find their way into the final release of SPM98.

			Good luck Jesper



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