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 <mailto: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 >>> <mailto: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 >>> >>> <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 > <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 ? 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 >>>> <matthieuvanhou...@gmail.com <mailto: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 >>>> >>>> >>>> _______________________________________________ >>>> Freesurfer mailing list >>>> Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> >>>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>>> <https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer> >>>> >>>> >>> >>> _______________________________________________ >>> Freesurfer mailing list >>> Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> >>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer >>> <https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer> >>> >>> >>> The information in this e-mail is intended only for the person to whom it is >>> addressed. 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