Hi Brianna,
I recommend to run all scans of a patient with one single subject template (base).
Then , if you are interested if atrophy rates change after intervention, you could use a piecewise linear model, with a node at the surgery. You can then compare your change in slopes with the change in the control group (without intervention).
If you are interested not in atrophy rates, but only if volume or thickness changes, the model would be different, but you still need a controll group to test if what you find is different from control group behaviour.
Best martin
Sent via my smartphone, please excuse brevity.
-------- Original message -------- From: Brianna Damadian bdamadia@buffalo.edu Date:07/03/2014 1:57 AM (GMT+01:00) To: Freesurfer support list freesurfer@nmr.mgh.harvard.edu Subject: Re: [Freesurfer] Longitudinal pipeline + LME question
Hi Martin,
Thanks for the response. What we are trying to do is look at changes within the same subject after a certain date (date of a surgical procedure). Would it make more sense to run all scans before and after this date in the same longitudinal pipeline and then look for differences or to run all pre-intervention scans with a separate longitudinal template from the post-intervention scans and then look for differences between the "pre" group as a whole and the "post" group as a whole? We are looking for the method that will give us the least variability due to processing differences. Any input you have would be great.
Thanks!
On Jul 2, 2014, at 5:31 PM, Martin Reuter mreuter@nmr.mgh.harvard.edu wrote:
Hi Brianna,
Yes, that is the best option in my opinion. You should include field strength as a covariate, and maybe other acquisition parameters.
Best Martin
Sent via my smartphone, please excuse brevity.
-------- Original message -------- From: Brianna Damadian Date:07/02/2014 10:04 PM (GMT+01:00) To: Freesurfer@nmr.mgh.harvard.edu Subject: [Freesurfer] Longitudinal pipeline + LME question
Hello experts,
We are setting out to look at a sample of 10 (subset of eventual larger sample) who have at least 2 sets of MRI. Some have many more than 2. However, some of the scans were done on different scanners (a few on 1.5T versus most on 3T) and with slightly different parameters (namely, different TR) - not exactly ideal.
We are looking for changes in total ventricular volume as well as total gray matter and white matter (among others). I am new to this software, but from what I have read in the literature the best approach would be to use the longitudinal pipeline in FreeSurfer and then use the Linear Mixed Effect model (LME) for statistical analysis. In your opinion, is this the correct approach based on our sample/goals?
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