It is voodoo to do the same test that you used to generate the cluster (or have a graph that implies such a test). If you want to do a post-hoc test, that is totally fair. Eg, if you do an unsigned test between the two groups, you could then go back and do a signed test on the extraction.

On 9/9/19 1:31 PM, cody samth wrote: > > External Email - Use Caution > > Hi Douglas, > > That's good to know its still a form of voodoo correlation. If > researchers wanted to avoid this, when looking for an interaction > effect in a DODS model with a continuous variable yet still wanted to > know the direction of the relationships how could that be done? As > currently if group 1>group 2. That could theoretically be interpreted > as 1) both groups are negative 2) both groups are positive 3) one > group positive one group negative. Would this be done by looking at > the beta values for the slope of that variable? > > > > Regarding graphing the results I'm not too familiar with matlab > however after running the contrast you suggested in matlab and > plotting the yhat value for 2 of my clusters the R2 is 1.000 for > both which leads me to believe I somehow ended up saving the predicted > values. (Rather than the actual values of thickness for each > participant) Did I load everything into matlab correctly? > These were my inputs > > X = load('Xg.dat') > Y = load('ocn.dat') > beta = inv(X'*X)*(X'*Y) > beta2 = load('beta2') ; file where I saved the beta values for the > mean thickness and slope of my variable of interest; that was computed > in the previous step > X2 = load('X2.dat') ; removed nuisance columns from the Xg.dat file > yhat = X2*beta2 ' saved these values and plotted them against my > variable of interest > > > On Mon, Sep 9, 2019 at 10:37 AM Greve, Douglas N.,Ph.D. > <dgr...@mgh.harvard.edu <mailto:dgr...@mgh.harvard.edu>> wrote: > > Right, the ocn.dat files have data that is uncorrected in that > sense and might need to nuisance factors removed before plotting. > There is a design matrix in there (Xg.dat). You can load that into > matlab along with the ocn.dat, compute beta = inv(X'*X)*(X'*ocn) > to get the betas. You can then compute yhat = X2*beta2 where X2 > has nuisance columns removed and beta2 has the same nuisance > coefficients removed, then treat yhat as your data to be plotted. > > Note that plotting the results is still a form a voodoo > correlations because your eye will compute the correlation even if > you don't explicitly do so (though it generally does not stop > anyone:). > > > > On 9/8/2019 7:37 PM, cody samth wrote: >> >> External Email - Use Caution >> >> Hi Douglas, thanks for your response. >> >> >They should not, but the reason it fairly convoluted. When you >> get a >> >cluster after running mri_glmfit-sim, that cluster is on >> fsaverage which >> >is an average of 40 subjects. The area of a vertex is computed >> as the >> >average of the areas of the vertices from the 40 that mapped >> into that >> >vertex. This is the number that is used to compute the surface >> area of >> >the cluster in the summary file. Now, when you map your subjects >> into >> >the fsaverage space, they may have more or less surface area >> mapping >> >into that cluster relative to the 40 (looks like more from #2 >> below). >> >Also, you probably smoothed the surface area, which could have an >> >unpredictable effect. >> Thanks that makes sense. >> >> >> 3) ocn.dat files are the input values meaning they're raw and >> would >> >> need to be corrected in a statistically (in a similar way that I >> >> modeled it in freesurfer) before graphing right? >> >Not sure what you mean by "corrected" here. In general, you need >> to be >> >very careful when you extract data from a cluster. It would be >> circular >> >to do the same test that you used to generate the cluster, >> though this >> >happens a lot (see "VooDoo correlations" by Ed Vul). >> My apologies corrected wasn't the best way to phrase that question. >> My interpretation of the ocn.dat file is that the each row >> contains the >> average input value for a subject prior to controlling for >> covariates. >> Therefore, to graph these results wouldn't these values need to >> undergo >> some method to control for covariates such as ICV, sex or age to >> better >> reflect the clusters observed from the GLM? >> >> Or are the values in the ocn.dat file already reflective of the >> test/glm >> used to generate the cluster? >> >> _______________________________________________ >> Freesurfer mailing list >> Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> >> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer > > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer