Hi Martin,

Please see inline below:

Le 22 nov. 2016 à 17:04, Martin Reuter <mreuter@nmr.mgh.harvard.edu> a écrit :

Hi Matthieu, 
(also inline)

On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte <matthieuvanhoutte@gmail.com> wrote:

Hi Martin,

Thanks for replying. Please see inline below:

Le 21 nov. 2016 à 20:26, Martin Reuter <mreuter@nmr.mgh.harvard.edu> a écrit :

Hi Matthieu, 

a few quick answers. Maybe Jorge knows more. 
Generally number of subjects / time points etc. cannot be specified generally. All depends on how noisy your data is and how large the effect is that you expect to detect. You can do a power analysis in order to figure out how many subject / time points would be needed. There are some tools for that in the LME toolbox:

1. see above
2. yes, also time points can miss from the middle. If you have mainly missing time points at the end, this will bias your analysis to some extend, as the remaining ones may be extremely healthy, as probably the more diseased ones drop out. You may want to do a time-to-event (or survival-analysis) which considers early drop-out.

Is there any way to do with Freesurfer this kind of analysis ?

Yes, there is also a paper where we do this. It is a combination of LME and Survival Analysis (as for the SA you need to have measurements of all subjects at all time points, so you estimate that from the LME model). 

Thank you for the link, I will take a look at. So if understand, this analysis has to be done after LME statistical analysis ? Thereafter since SA need all time points, LME model will allow me to estimate missing time points ?



3. see above (power analysis)
4. GIGO means garbage in, garbage out, so the less you QC, the more likely will your results be junk. The more you QC the less likely will it be junk, but could still be. The FS wiki has lots of tutorial information on checking freesurfer recons. For longitudinal, you should additionally check the surfaces in the base, the brain mask in the base, and the alignment of the time points (although there is some wiggle space for the alignment, as most things are allowed to evolve further for each time point). 

For the alignment of the time points, should I better comparing brainmask or norm.mgz ?

It does not really matter, I would use norm.mgz. I would load images on top of each other and then use the opacity slider in Freeview to blend between them (that way the eye can pick up small motions). I would not worry too much about local deformations which could be caused by non-linearity (gradient). But if you see global misalignment (rotation, translation) it is a cause for concern) .

Ok thank you. The non-linearity you are talking about are well provoked by MRI system and not non-linear registration between time points and template base, aren’t they ?

Best regards,
Matthieu



In order to avoid bias by adding further time points in the model by the -add recon all command, is this better for each subject to take into account all the time points existing for it or only the ones that I will include in the model (three time points / subject ; if existing 6 time points for any subject ?)


Usually it is recommended to run all time points in the model (so a base with 6 time points) and not use the - - add flag. Also, Linear Mixed Effects models deal well with missing time points. It is perfectly OK to have differently many time points per subject for that. You should still check if there is a bias (e.g. one group always has 3 time points the other 6) that would not be good. Maybe also consult with a local biostatistician if you are not comfortable with the stats. The LME tools are matlab, and so are the survival-analysis scripts. 

Best, Martin



Best regards,
Matthieu


Best, Martin

On Nov 21, 2016, at 7:07 PM, Matthieu Vanhoutte <matthieuvanhoutte@gmail.com> wrote:

Dear Freesurfer’s experts,

I would have some questions regarding the LME model to be used in longitudinal stream:

1) Which are the ratio limits or % of missing timepoints accepted ? (according time, I have less and less subjects time points)

2) Is it possible to include patients that would miss the first timepoint but got the others ?

3) Considering a group in longitudinal study, which is the number of subjects minimal of this group accepted for LME modeling ?

4) Finally, concerning quality control and among a big number of total time points, which essential controls are necessary ? (Control of norm.mgz of the base, alignment of longitudinal timepoints on base,… ?)

Best regards,
Matthieu


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