Dear FS's experts,

Could you answer me about questions from below inline precedent mail ?

Best regards,
Matthieu


2016-11-22 21:31 GMT+01:00 Matthieu Vanhoutte <matthieuvanhou...@gmail.com>:

> Hi Martin,
>
> Please see inline below:
>
> Le 22 nov. 2016 à 17:04, Martin Reuter <mreu...@nmr.mgh.harvard.edu> a
> écrit :
>
> Hi Matthieu,
> (also inline)
>
> On Nov 21, 2016, at 10:28 PM, Matthieu Vanhoutte <
> matthieuvanhou...@gmail.com> wrote:
>
> Hi Martin,
>
> Thanks for replying. Please see inline below:
>
> Le 21 nov. 2016 à 20:26, Martin Reuter <mreu...@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:
> https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels#
> Poweranalysis
>
> 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 ?
>
>
> https://surfer.nmr.mgh.harvard.edu/fswiki/SurvivalAnalysis
> 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 ?


>
>
> 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 <
> matthieuvanhou...@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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