Hi Alex, as far as I know qdec can do what you want (compare e.g. the atrophy rate across the two groups).
However, you don't need to use qdec for the second stage, you can run mri_glmfit directly on the differences (or slopes). Basically you get a thickness_yearly_change (or pct change) in each base directory and can then see if that correlates with any covariate or group membership etc. Qdec just makes is easier to use. Best, Martin On Tue, 2012-12-04 at 19:19 -0500, Alex Hanganu wrote: > Hello Martin, > > thanks for the quick reply ! > > as I understood, from the "LongitudinalTutorial", after long_mris_slopes > the results can be seen for each subject, and in order to see the group > results, all subjects should be analysed in the Qdec, and there I could > see the results for each group, but couldn't do the inter-group, this is > why we tried the "RepeatedMeasuresAnova" > > Indeed, there is some difference in the time distance, so we will try > your new idea ! > > I know about Jorge's work - Marvellous ! but it seems to need too much > time to be comprehended and applied. We hoped to be done with these > results and start already writing the paper :) > > Thank you very much for Your help !!! > > Sincerely, > Alex. > > > > > Le 04/12/2012 6:44 PM, Martin Reuter a écrit : > > Hi Alex, > > > > I am not familiar with the way Doug describes the repeated measure anova > > on the wiki. Of course in a longitudinal setting repeated measures are > > correlated and I am not sure if this is considered in that model there. > > > > Since you have only 2 time points, why not simply compare the difference > > (or weighted by the time distance, if the time points are not the same > > distance apart)? > > > > You would compute (tp2-tp1)/time for each subject and then compare this > > across groups with a standard glm. > > Since in the longitudinal stream both thickness maps are registered, you > > can simply use mris_calc to compute the difference directly. > > > > There are also scripts for this (long_mris_slopes, even for more than 2 > > time points, where we fit a line into each subject). See the > > http://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/LongitudinalTutorial > > This is a simple approach, first reducing the variable of interest > > (change across time) to a single number per subject and then running a > > standard test. It should be sufficient for your setting. > > > > You can also do more complex modeling using our new linear mixed effects > > models if you want (see older email from Jorge about that). It considers > > both temporal and spacial correlation of measures. This model is > > especially useful if you have differently many time points and time > > distances per subject. > > > > Best, Martin > > > > > > On Tue, 2012-12-04 at 18:26 -0500, Alex Hanganu wrote: > >> Dear Freesurfer Experts, > >> > >> We are analysing longitudinal data - the difference between 2 groups (P > >> and M) [19 and 17 subjects] with 2 time points for each group (A, B). > >> > >> We are using the example of "Repeated Measures Anova" > >> (http://surfer.nmr.mgh.harvard.edu/fswiki/RepeatedMeasuresAnova) > >> > >> For our first approach - we took all the subjects - 36 classes, and > >> tried to create the contrast for: > >> > >> P(B-A) - M(B-A) > >> 2 within subject factors, and 2 inter-subject > >> > >> We considered the recent explanations > >> (http://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg25459.html), > >> and, as we understood, our null hypothesis is: > >> > >> PB-PA=0 AND MB-MA=0 -> > >> Combining: PB-PA-MB+MA=0 > >> > >> and the matrix seems to be: > >> 0 0 0 .... (36 zeros) -1 > >> 0 0 0 .... (36 zeros) 1 > >> 0 0 0 .... (36 zeros) 1 > >> 0 0 0 .... (36 zeros) -1 > >> > >> but we still get a dimension mismatch between X and C: X has 72, C has 37. > >> > >> The fsgd file is like this: > >> Class subject 1 > >> . > >> . > >> . > >> Class subject 36 > >> Variables TP1-vs-TP2 > >> Input groupPsubj1-A Subject1 -1 > >> Input groupPsubj1- B Subject1 1 > >> Input groupPsubj2-A Subject2 -1 > >> . > >> . > >> . > >> Input groupMsubj35-A Subject35 1 > >> Input groupMsubj35-B Subject35 -1 > >> Input groupMsubj36-A Subject36 1 > >> Input groupMsubj36-B Subject36 -1 > >> > >> ============== > >> > >> Our second approach - we run mris_glmfit for each group separately and > >> then we wanted to use mris_calc to compute the difference: > >> > >> mris_calc -o avg.mgh groupP/glm-dir/Contrast/sig.mgh add > >> groupM/glm-dir/Contrast/sig.mgh > >> > >> though the sig.mgh for each group shows significant results, the avg.mgh > >> reveals no effect. > >> > >> Can you please help with this analysis ? > >> > >> Thanks! > >> > >> Sincerely, > >> Alex. > >> ____________________ > > > > _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu 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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