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 <ggonesc@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 * greve@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: ggonesc@upo.es http://www.upo.es/neuroaging/es/ _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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