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. <DGREVE@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:
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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?
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