External Email - Use Caution
Dr. Greve,
I am sorry if my questions were not clear in previous email.
Basically, I do not know what to conclude from this gamma comparison i.e. with and without ICV as covariate. Clearly, adding ICV as covariate here, is reducing effect size all over the brain and without ICV effect size is higher at specific locations.
So should I go ahead with or without ICV as covariate?
Thanks.
On Wed, Jul 25, 2018 at 7:33 AM, Douglas Greve dgreve@mgh.harvard.edu wrote:
The gammas do look different, but it is hard to tell whether they are, eg, changing sign. Not sure what you want me to comment on.
On 7/24/18 2:17 PM, Martin Juneja wrote:
External Email - Use CautionJust to add some more info here: The peak location of regions, X1 and X2, which I found without including ICV as covariate are very close with the peak locations I found in Gamma_Without_ICV (~5.15), whereas Gamma_With_ICV is almost all over the brain (range -0.6 to +0.6). I am not sure if this additional info adds anything to interpret gamma.mgh with and without ICV as covariate.
On Tue, Jul 24, 2018 at 10:10 AM, Martin Juneja mj70481@gmail.com wrote:
Hi Dr. Greve,
So I checked both. The rstd.mgh files are very similar in both cases (with and without ICV as covariate), but gamma.mgh files are very different for both cases. Here I am attaching screen shot for both cases: Gamma_With_ICV as covariate and Gamma_Without_ICV as covariate.
Could you please have a look at the attached screen shots and provide your thoughts/interpretation of this comparison?
Thanks.
On Tue, Jul 24, 2018 at 9:35 AM, Douglas N. Greve <dgreve@mgh.harvard.edu
wrote:
For noise compare the values in the rstd.mgh file, for effect size look in the gamma.mgh file
On 07/24/2018 12:27 PM, Martin Juneja wrote:
External Email - Use CautionHi Dr. Greve,
Thanks for your quick reply. Could you please give me more details how can I check this whether its because of noise or its because of less CV difference? I am not sure what method/way is the best and commonly used to confirm these factors.
Thanks.
On Tue, Jul 24, 2018 at 7:06 AM, Douglas Greve <dgreve@mgh.harvard.edu mailto:dgreve@mgh.harvard.edu> wrote:
your results could have vanished after ICV correction for one of two reasons: the CV difference became less or the values became noisier (or a combination). So check in your data which one of those things happened. On 7/23/18 8:30 PM, Martin Juneja wrote:External Email - Use Caution Hello experts, I am interested in identifying regions of interest by comparing cortical volume (CV) between controls and patients. After including age and sex as my covariates, I identified regions X1 and X2, which showed significantly lower CV for patients (as compared to controls). But after I include ICV as another covariate, my results show that for none of the areas there is any significant difference in CV, i.e. my results vanish. When I checked subjectwise ICV for each group, I found that there is almost significant difference (two-sampled t-test, p = 0.067) in ICV between two groups, but interestingly mean group ICV for patients group was larger compared than mean ICV for controls. But as I said earlier, regions X1 and X2 had significantly lower CV for patients (as compared to controls), when I didn't include ICV as covariate. Could you please help me in interpreting these results? Is there any advice regarding inclusion of ICV as covariate? Or my results are purely because of differences in ICV between groups, and there is no real findings regarding the regions identified (X1 and X2)? Thanks a lot ! _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer <https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer>_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>
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