Re: [Freesurfer] ICV normalization (longitudinal pipeline)

[Martin Reuter]

Hi Martin,

ICV is head size. It is unlikely that anything except head growth in children 
changes head size. I don’t know of drugs that do that ;-). 

So it is best to assume that head size is fixed for adults. Usually results 
should have less variance if you remove the noise from the ICV estimate, thus 
smaller p-values. Not sure what happens in your case, but I would not trust 
that.

(also how large is your dataset, how far apart are the time points, and what 
kind of treatment will change head size??)

Best, Martin



> On 1. Nov 2018, at 16:13, Martin Juneja  wrote:
> 
> Hello FS experts,
> 
> I am using longitudinal processing pipeline 
> (https://surfer.nmr.mgh.harvard.edu/fswiki/LongitudinalProcessing 
> ) to 
> calculate the cortical volume (CV) over a course of treatment (two 
> conditions: pre condition and post condition). In fact, I am interested in 
> comparing normalized CV (NCV) i.e. raw CV/ ICV between pre- and 
> post-condition.
> 
> (1). Now, longitudinal pipeline assumes that there is no change in ICV so it 
> gives me identical ICV for pre and post condition. That way, when I compared 
> NCV between pre and post condition, it gives me differences at significant 
> level of 0.16.
> (2). However, when I checked ICV (from cross-sectional pipeline i.e. each 
> time-point separately) and compared between pre and post-conditions, ICV 
> values are different. Its possible that these values could be different 
> because of treatment (may be !). When I compared NCV between pre and post 
> condition (calculated by dividing raw CV from longitudinal pipeline with ICV 
> values form cross-sectional pipeline), it gives me significant differences at 
> 0.02.
> 
> Could you please share your thoughts on this i.e. (a) whether I can use 
> approach 2 or (b) if I am using longitudinal pipeline, dividing raw CV from 
> longitudinal pipeline and dividing by ICV calculated from cross-sectional is 
> in correct way to do this analysis?
> 
> Thanks.
> ___
> 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

[Freesurfer] ICV normalization (longitudinal pipeline)

[Martin Juneja]

External Email - Use Caution

Hello FS experts,

I am using longitudinal processing pipeline (
https://surfer.nmr.mgh.harvard.edu/fswiki/LongitudinalProcessing) to
calculate the cortical volume (CV) over a course of treatment (two
conditions: pre condition and post condition). In fact, I am interested in
comparing normalized CV (NCV) i.e. raw CV/ ICV between pre- and
post-condition.

(1). Now, longitudinal pipeline assumes that there is no change in ICV so
it gives me identical ICV for pre and post condition. That way, when I
compared NCV between pre and post condition, it gives me differences at
significant level of 0.16.
(2). However, when I checked ICV (from cross-sectional pipeline i.e. each
time-point separately) and compared between pre and post-conditions, ICV
values are different. Its possible that these values could be different
because of treatment (may be !). When I compared NCV between pre and post
condition (calculated by dividing raw CV from longitudinal pipeline with
ICV values form cross-sectional pipeline), it gives me significant
differences at 0.02.

Could you please share your thoughts on this i.e. (a) whether I can use
approach 2 or (b) if I am using longitudinal pipeline, dividing raw CV from
longitudinal pipeline and dividing by ICV calculated from cross-sectional
is in correct way to do this analysis?

Thanks.
___
Freesurfer mailing list
Freesurfer@nmr.mgh.harvard.edu
https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer