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?
>>
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