I had this exact question yesterday :-) The functions necessary to run it
are in $FREESURFER_HOME/matlab and $FREESURFER_HOME/fsfast/**toolbox
Just add the path to these directories in your matlab (file -> setpath) and
you can run the function from your matlab command window
*Doug -* I had a follow up question about using this function on clusters
identified in mri_glmfit-sim. Is the function as written below calculating
the pcc across the whole brain or is it just calculating the rho using the
betas specific to the cluster found in mri_glmfit-sim by only taking those
betas from beta.mgh that are specific to the contrast and cluster results
in C.dat? The reason I ask is that I have a cluster showing a significant
relationship between thickness and a continuous behavioral variable (a
positive relationship as denoted by a positive log10p of the Maxvertex in
the cluster) and I'd like to get the correlation coefficient for it but
when I run the PCC function I get zero so I'm wondering if this is because
it is calculating across the whole brain rather than just within the
cluster? (btw I get the same issue with a similar relationship showing
positive corr between thickness and two behav variables).
Thanks,
Laura.
On Thu, Mar 21, 2013 at 10:37 AM, Gabriel Gonzalez Escamilla <ggon...@upo.es
> wrote:
> Thanks Doug for your quick answer,
>
> Sorry for so late answer.
>
> One question about this, is about the fast_vol2mat, is this a function? if
> so, where can I get it?
>
> As the PCC is the R value, I'm guessing that I can just square at it, to
> obtain R2.
>
> When you asked me to divide the beta by sqrt(rvar), is there any place
> where I can find is this is the correct way to get the standardized beta?
>
> Best regards,
> Gabriel
>
>
>
> El 14/03/13, *Douglas Greve * <gr...@nmr.mgh.harvard.edu> escribió:
>
> Hi Gabriel, I've attached a matlab routine which will compute the PCC.
> If you cd into the GLM dir, then
>
> X = load('Xg.dat');
> beta = MRIread('beta.mgh');
> C = load('yourcontrast/C.dat');
> rvar = MRIread('rvar.mgh');
>
> betamat = fast_vol2mat(beta);
> rvarmat = fast_vol2mat(rvar);
>
> rhomat = fast_glm_pcc(betamat,X,C,rvarmat);
>
> rho = beta;
> rho.vol = fast_mat2vol(rhomat,rho.volsize);
> MRIwrite(rho,'yourcontrast/pcc.mgh')
>
> The R2 should just be the square of the PCC, right?
>
> For the standardized beta, do you just divide the beta by sqrt(rvar)?
>
> doug
>
>
> On 3/14/13 1:39 PM, Gabriel Gonzalez Escamilla wrote:
>
>
> Dear Freesurfers
>
> I'm performing regression analyses including confounding variables, and I
> would like to know how to obtain the following information:
>
> A) The squre R
>
> and
>
> B) The standarized beta coefficient of an independient variable; and the
> partial correlation with its p-values
>
>
> Many thanks in advanced,
> Gabriel
>
>
>
> _______________________________________________
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>
>
> --
> --------------------------
> PhD. student Gabriel González-Escamilla
> Laboratory of Functional Neuroscience
> Department of Physiology, Anatomy, and Cell Biology
> University Pablo de Olavide
> Ctra. de Utrera, Km.1
> 41013 - Seville
> - Spain -
>
> Email: ggon...@upo.es
> http://www.upo.es/neuroaging/es/
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--
--
Laura M. Tully, MA
Social Neuroscience & Psychopathology, Harvard University
Center for the Assessment and Prevention of Prodromal States, UCLA Semel
Institute of Neuroscience
ltu...@mednet.ucla.edu
ltu...@fas.harvard.edu
310-267-0170
--
My musings as a young clinical scientist:
http://theclinicalbrain.blogspot.com/
Follow me on Twitter: @tully_laura
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