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Hi Maksim,

1. Yes the median image will be very similar to the first three time points. In 
fact, if the 4th image was an MRI of a knee it would not matter at all.
2. There is a potential bias this way, however, I expect it to be very small. I 
don’t know of anyone ever investigating this thoroughly.

A potential workaround:
Just create the template with the first and the last time points only by 
passing them only in the base creation step. Then add the other time points 
into the existing base later, I think there was an add_tp script for that.
The disadvantage of this approach is that the base is the mean image between to 
time points that are further apart, leading to blurry edges in the image, and 
less clearly defined surfaces. So you remove bias, but increase the chance of 
reduced quality surfaces slightly.

My recommendation: in a catch-22 I would choose the easier option, which is 
include all in the base and hope the bias is small. 2 years are not that much 
different, unless there is really heavy atrophy. We are talking about adult 
brains, right?

Best, Martin

On 26. Sep 2023, at 13:34, Maksim Slivka <maksim.sli...@psykologi.uio.no> wrote:



Hi FreeSurfer Community,

I plan to use longitudinal FreeSurfer to process a set of longitudinal scans 
that are likely to create a bias in the base template. Specifically, our study 
consists of 3 scans acquired within the first 20 weeks and a subsequent scan 
acquired 2 years after the third scan.

From the original publication on longitudinal FreeSurfer, I understand that the 
longitudinal scans are treated equally and do not require to be evenly 
distributed in time, and that the cross and base images are only used to 
initialize the longitudinal processing. However, my concern is that the 
resulting template, based on the median, will be too similar to the first three 
scans.

Would this negatively impact the results of the 4th scan, due to the inherent 
bias of the template? Has any group investigated this before?

Any feedback would be appreciated!

Best regards,
Maksim

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