Hi Koushik,
yes, this paper compares cross and long processing:
http://reuter.mit.edu/papers/reuter-long12.pdf http://www.sciencedirect.com/science/article/pii/S1053811912002765
improvements are region dependent. When comparing the methods, you can keep sample size and number of time points fixed (the only thing that changes is the processing approach, not the data). In a power analysis we see a sample size reduction, required to find the same effects, of around 50% (for subcortical volume estimates on 1.5T data). This can be different for other regions and data.
Best, Martin
On 01/01/2016 04:10 PM, Govindarajan, Koushik Athreya wrote:
Thanks for the clarification Martin. Is there some literature on how much the sensitivity and reliability improves? Does it depend on sample size and number of serial time points?
Happy new year to you too, Koushik A. Govindarajan
From: freesurfer-bounces@nmr.mgh.harvard.edu [freesurfer-bounces@nmr.mgh.harvard.edu] on behalf of Martin Reuter [mreuter@nmr.mgh.harvard.edu] Sent: Friday, January 01, 2016 10:58 AM To: Freesurfer support list Subject: Re: [Freesurfer] Question on longitudinal stats directory
Hi Koushik,
the cross sectional results are from processing the images independently. Those results are more noisy than necessary. By using information from across time points within the same subject (longitudinal stream) noise can be removed, so you will get more reliable and more sensitive estimates in the longitudinal dirs.
Happy new year, Martin
On 12/29/2015 01:09 PM, Govindarajan, Koushik Athreya wrote:
Hi there,
I have run my subjects from multiple time points through a cross-sectional stream and a longitudinal stream. When I look at the 'stats' directory for the same subject from the cross-sectional pipeline and the longitudinal pipeline, I see that the numbers are different. For example, the cortical thickness values in the lh.aparc file for the same subject are different. Is one estimation more accurate than the other? Where is the difference coming from?Thanks Koushik Govindarajan
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