If you are using surface area or volume, then you need ICV
On 7/25/18 1:51 PM, Martin Juneja wrote:
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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 <dgr...@mgh.harvard.edu
<mailto:dgr...@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:
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Just 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
<mj70...@gmail.com <mailto:mj70...@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
<dgr...@mgh.harvard.edu <mailto:dgr...@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 Caution
>
> Hi 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
<dgr...@mgh.harvard.edu <mailto:dgr...@mgh.harvard.edu>
> <mailto:dgr...@mgh.harvard.edu
<mailto:dgr...@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 !
>>
>>
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