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On Fri, Jun 8, 2012 at 12:35 PM, Alexa Morcom <[log in to unmask]> wrote:

> Dear SPM colleagues
>
> I'm attempting to scale regressors in a design matrix so that their sum of
> squares equals 1. The reason to do this is that I have subjects with
> different HRF timings in my task and I want to be able to calculate
> 'amplitude' images (or 'derivative boost') from canonical and temporal
> derivative images using the formula in Vince Calhoun's 2004 paper, but this
> formula makes assumptions about the scaling of canonical and derivative
> regressors.
>

The key in their methods is that the HRFs are scaled correctly. If you
scale the design matrix, then you will be biased by the number trials in
each condition.


>
> I've looked at previous helpful postings by Donald McLaren
> See http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=SPM;fa723f27.0908  (Aug
> 17 2009)
> ..as well as the 2010 Neuroimage paper by Jason Steffener and others which
> highlights the scaling issue.
>
> My first question is a check on some basic GLM maths. I thought that T
> values should not change with regressor scaling, even though beta values
> do, as the variance estimate changes too by the right amount. Can someone
> confirm whether this is correct, as I have different T results following my
> scaling operation. If this is correct, something is not right.
>

If you scale the HRF regressors (not DM), then the statistics shouldn't
change. However, if you scale each column of the DM, then the T-statistics
can change as you are changing the relationships between different columns.


>
> IF something is not right, can someone advise on what is not working about
> normalising columns of SPM.xX.X so that sum of squares of each column = 1?
> This is how I understand the recommendations in Steffener et al 2010 and
> this is what I've done (followed by re-estimation of the model & contrasts,
> obviously). But I may well have misunderstood something in the method.
>

I'd email Jason about how to normalise the HRFs rather than the DM.


>
> Thanks!
>
> Alexa
>
>
>
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