Hi,

>But what about the fact that MRI signals are known to drift in time?  That
>would seem to me to make it difficult to refer to "a baseline".

Another possible answer to this is that so far as I know, whenever we model
autocorrelation in the fMRI scanner (although others might come up with a better
way) we model it as a stationary process, which I think means that the temporal
autocorrelation is assumed to be some mean value + random autocorrelated noise,
with the mean value being some constant fixed value.

So, even though the timeseries of a voxel may drift slowly, if the noise-producing
process IS stationary, then the mean over the whole session (a.k.a. the intercept
term) is still your best guess as to what the 'baseline' value is.  (I think this
would still hold given linear nonstationary drift, too.)  Again, please correct me
if you think this isn't right...

Tor




Michael Zeineh wrote:

> Hi Stephen,
>
> One can do voxelwise drift correction to combat this. Most signal
> drift I've noticed has been due to residual motion artifact. For
> linear drift, this can be quite effective.
>
> You are correct that this introduces more uncertainty into the
> scaling process, and voxels can be affected differently depending on
> how much drift there is in different parts of the image. Yet, this
> will still hold as a measure of % change.
>
> Michael
>
> At 3:17 PM -0400 4/16/01, Stephen Fromm wrote:
> >Dear Michael,
> >
> >But what about the fact that MRI signals are known to drift in time?  That
> >would seem to me to make it difficult to refer to "a baseline".
> >
> >Best wishes,
> >
> >Stephen Fromm, PhD
> >NIDCD/NIH
> >
> >----- Original Message -----
> >From: "Michael Zeineh" <[log in to unmask]>
> >To: <[log in to unmask]>
> >Sent: Monday, April 16, 2001 3:05 PM
> >Subject: Re: percent signal change questions (a simple hack)
> >
> >
> > > Good points, Tor.
> > >
> > > I completely forgot about the scaling issue. Whether or not your beta
> > > represents % change really does depend on the scaling.
> > >
> > > If the data are not scaled at all, then the betas will not represent %
> > > change. If 2 given voxels both change intensity by 1 signal unit, they
> >will
> > > both exhibit the same beta. However, they may differ significantly in
> > > baseline signal intensity, and hence % change.
> > >
> > > The way I scale my own data is to normalize (that is, divide) each voxel
> > > either by a baseline time point, or even better, by the intercept of the
> > > regression at each voxel. I do not know if this is what the global mean
> > > scaling option in SPM does. This is equivalent to normalizing the response
> > > function to 100% change at each voxel, similar to what you mention.
> > > Multiplying by 100 turns the proportion into a percent. I think this can
> > > either be applied to the raw time series or the betas themselves.
> > >
> > > Michael
> > >
> > > At 12:20 PM 4/16/2001 -0400, Tor Dessart Wager wrote:
> > > >Hi everyone,
> > > >
> > > >I have to add my 2 cents to the discussion on percent signal change...I
> > > >think that beta values are SOMETIMES equal to percent signal change, but
> > > >not always.  Betas can have arbitrary scaling, so they don't always give
> > > >you % change, but they should give you something proportional to it.
> > > >
> > > >Even if your model has high error (high variance), the beta is always
> >your
> > > >best guess as to what the change in signal is.  If the error variance is
> > > >high, the beta might not be significantly different from zero - but this
> > > >is a different question than "what is the % signal change."  So, you
> >might
> > > >have a timeseries where you estimate the % change, and it's, say, .1%.
> > > >If properly scaled, a beta of .1 = a % change of .1% - but whether one
> > > >should infer that's a "real" change, or random noise, depends on the
> > > >significance of the beta.
> > > >
> > > >About scaling: if you mean center your predictors, and then measure
> >betas,
> > > >you lose information about the baseline level of signal, so you lose info
> > > >about % change from that baseline.  I think - correct me - that if your
> > > >HRF is normalized so that the height of the impulse response function is
> > > >1% of the baseline signal, then a beta of 1 = 1% signal change.  This
> > > >isn't guaranteed to be the case.  So I'm not sure right now how to
> > > >normalize betas to get % change, but maybe someone can say...
> > > >
> > > >Anyway, about the issue of parametric maps:  I think you have to have a
> > > >statistical map so that you know what's significant.  This is also good
> >if
> > > >you're trying to find reliable changes.  If, however, you're more
> > > >concerned with which are the LARGEST changes rather than which are the
> > > >most reliable, you might want to make a % signal change map for
> > > >significant voxels only.  I think this would be a very useful way to
> > > >summarize results.
> > > >
> > > >So please correct my faulty thinking on any of these points...
> > > >
> > > >Thanks,
> > > >Tor
> > > >
> > > >
> > > >_____________________________
> > > >Tor Wager
> > > >Department of Psychology
> > > >University of Michigan
> > > >Cognition and Perception Area
> > > >525 East University
> > > >Ann Arbor, MI 48109-1109
> > > >
> > > >Office: 734-936-1295
> > > >Home:   734-995-8975
> > > >Email:  [log in to unmask]
> > > >_____________________________
> > > >
> > > >On Mon, 16 Apr 2001, Stephen Fromm wrote:
> > > >
> > > > > 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
> > > > >