On 4 Feb 2008, at 21:37, Daniel Simmonds wrote:

> Hi,
>    My name is Dani Simmonds, and I do fMRI work in pediatric  
> populations with Stewart Mostofsky at the Kennedy Krieger Institute  
> in Baltimore. We have been using your RWLS method, which I have  
> found to work quite well, and I am curious about your opinion in  
> extrapolating your method to a different type of analysis. We are  
> just beginning to work on functional connectivity analyses, and  
> artifacts are even more problematic for these analyses than for  
> regular GLM analyses, because in "model-free" analyses, your data  
> is your model, and hence artifacts can very easily masquerade as  
> (usually) increased or (sometimes) decreased connectivity (see  
> attached image for an extreme example). I was thinking that the  
> RWLS method would work great here. My idea would be to:
>
> 1) run the regular GLM model with RWLS
> 2) extract the noise regressor estimated by RWLS (which I assume is  
> the regressor from the SPMfp.mat after estimating that I find in  
> SPM.xVi.h)
> 3) extract the time course for the ROI (that has been high-pass  
> filtered)
> 4) orthogonalize the ROI time course to the RWLS noise regressor
> 5) re-run the GLM model with RWLS, additionally adding in the ROI  
> time course as a regressor in order to run a seed-based  
> connectivity analyses (with the ROI as the seed) (alternatively,  
> repeat the process with all the ROI's and cross-correlate them,  
> rather than doing a whole-brain based seed analysis)
>
>    I think that I am on the right track here, but I don't  
> completely understand your method (ie I don't understand how you  
> implement the weighting exactly, but my matlab/spm knowledge is  
> somewhat weak...), and I would just like to hear whether you think  
> this might be a good approach, whether there are some steps that  
> should be modified, or whether you think there are major problems  
> with it. Thank you for the help!
>
> Dani
>
> Daniel Simmonds
> Developmental Cognitive Neurology
> Kennedy Krieger Institute
> [log in to unmask]
>


Dear Daniel,
I think I understand what you are trying to do.

Just to clarify: the SPM.xVi.h-weights that you get from WLS, are  
weights in the regression, and should NOT be confused with noise  
regressors that go in your design matrix. Regressing out artefacts by  
adding something to the design matrix, and re-weighting are two  
fundamentally different approaches to artefact removal. So of you say  
that you orthogonolize the ROI time course with the RWLS "regressor"  
this would be the wrong approach.

Rather you should run your new analysis with the weighting matrix  
that you found in your first analysis. So if your SPM structure  
contains a SPM.xVi.V (variance matrix) or an SPM.xX.W (weighting  
matrix = V^-1/2), the spm_spm will skip the first round and go  
directly to the parameter estimation, using your old weighting matrix.
So, set up your GLM, put the xX.W and xVi.V matrix in from your first  
analysis and re-estimate (your can do this in spm_spm or spm_rwls_spm  
with identical results).


Hope this helps,

Joern




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