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 mj70481@gmail.com wrote:
Hello FS experts,
I am using longitudinal processing pipeline (https://surfer.nmr.mgh.harvard.edu/fswiki/LongitudinalProcessing 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?
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