Hi Martin,
could you please confirm whether the glm analysis was correctly performed ?
the command line is:mri_glmfit --glmdir DIR --y
lh.thickness-pc1.stack.fwhm15.mgh --label lh.fsaverage.cortex.label --fsgd
FSGD_FILE --C Contrast-010..0.mtx --surf fsaverage lh
I get results, but I when I do the same command for the analysis from the
2-stage-model webpage, the results are different. For example, on the webpage
the cmd is:mri_glmfit --osgm --glmdir DIR --y Y.mgh --label LABEL.label --surf
fsaverage lh
I tried to receive the same result with:mri_glmfit --glmdir DIR --y Y.mgh
--label LABEL.label --fsgd FSGD_FILE --C Contrast-100...0.mtx --surf fsaverage
lh
but the results are different. In this case, how can I be sure that the first
analysis was performed correctly ?
the fsgd file was constructed using the base-subjects and the values were taken
as in the "cross" file that is used by qdec.
Thanks,Alex
On Thursday, October 23, 2014 2:52 PM, Martin Reuter
<mreu...@nmr.mgh.harvard.edu> wrote:
Hi Alex,
you are not looking at a "one sample group mean" (osgd) so don't pass that
flag. Your design is probably something like
1 A other_co_vars_to_regress_out
(these are column vectors).
so contrast in that case would be [ 0 1 0... ]
That should create all outputs. All of this is really cross sectional analysis
where the depending variable is simply the 'change in thickness' instead of
thickness itself. Take a look at the glm tutorial on the wiki, which describes
the process.
Best, Martin
On 10/23/2014 02:40 PM, Alex Hanganu wrote:
Hi Martin,
thanks for confirming. I duplicated the parameter and got good results in
qdec.
I also tried to repeat the analysis with mri_glmfit but I can't manage to
come to an end. In order to analyse the correlation between pc1 and parameter
'A', it seems that I have to construct an fsgd file, that is different from the
.qdec file included in the "long_mris_slopes" command. Nevertheless, after
doing so (presumably all "Inputs" were attibuted to subject.long.base-time1
and subject.long.base-time2) I thought that a contrast is needed, yet the "--C"
and the "--osgm" flags cannot be used together. - How can the correlation
between -pc1 and parameter 'A' be performed in this case ?
Additionally, after performing the "mri_glmfit" described in the
2-stage-model page, in the tksurfer how can I see the plot ? The y.fsgd file
wasn't created. Is there another method ?
Thanks, Alex
Le mardi 21 octobre 2014 16h40, Martin Reuter
<mreu...@nmr.mgh.harvard.edu> a écrit :
Hi Alex,
you have to duplicate the parameter (it is basically fixed across time). If
you put 0 for tp2, it will average the two values, which is not what you want.
Otherwise I think it is the correct approach.
Best, Martin
On 10/21/2014 04:31 PM, Alex Hanganu wrote:
Dear Martin,
thank you very much for your answer ! and thanks for all the details ! - yes,
we have exactly 2 time points in all subjects and the parameter is a single
number.
In qdec - it seems that qdec table has to include the parameter 'A' both at
time 1 and at time 2 in order for "long_qdec_table" command to create the
"cross" file. I put a zero at time 2. In qdec design we analyzed parameter 'A'
with -pc1 and -spc. I'm not sure that this is the correct approach.
I'll continue with LME and mri_glmfit.
Sincerely, Alex
Le mardi 21 octobre 2014 9h19, Martin Reuter
<mreu...@nmr.mgh.harvard.edu> a écrit :
Hi Alex,
the parameter is a single number that happens to be measured at time 1 right,
eg baseline age? Lets call that parameter 'A' for the discussion below. Also
you have exactly 2 time points in all subjects?
There is two alternatives:
1. Simple approach (2-stage-model): You compute the atrophy rate (e.g. percent
thickness change) on the cortex (long_mris_slopes) for each subject. At this
point you have 1 measure per subject and work cross-sectionally. You can use
qdec or mri_glmfit to correlate 'A' (independent parameter) with the thickness
change (dependent variable). This is OK if you have the same number of time
points and the same time distance in all subjects. Details here:
https://surfer.nmr.mgh.harvard.edu/fswiki/LongitudinalTwoStageModel
2. Better approach: use Linear Mixed Effects models (we have matlab tools for
that). This model is more flexible (different manycolumn of ones, time points,
different time intervals, even subjects with a single time point can be
added). You'd setup a system like
Y_ij = beta_0 + b_i + beta_1 * A_i + beta_2 t_ij + beta_3 A_i * tij + error_ij
where Y_ij is the thickness of subject i at time point j (known)
t_ij is the time from baseline of the j measurement in subject i (known),
A_i is the variable you measure at baseline in subject i (known),
the model will estimate the following:
b_i (a random effect) is the subject specific intercept (offset from the
global intercept beta_0)
beta_1 another intercept offset caused by A
beta_2 the slope with respect to time (fixed effect, so it will be the same
for all subjects, can also be modelled as a mixed effect)
beta_3 the interaction of A and time (<- you are interested in this)
Testing if the interaction beta_3 is different from zero will show you where
A has an effect on the slope.
For the model above the X matrix would have 4 columns:
1 A T (A.*T)
where 1 is a column of 1's, A the A_ij (Ai repeated j times for each
subject), T=t_ij and the coordinate wise product of A and T. Contast [ 0 0 0
1] tests the interaction. You'd tell the function that you want the intercept
to be a random effect by passing [ 1] (selecting the first column). If you
also want to have t_ij as a random, you can pass [1 3 ] . Details here:
https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels
Best, Martin
On 10/20/2014 03:20 PM, Alexandru Hanganu wrote:
Dear FreeSurfer Experts,
How could the longitudinal analysis be performed in order to show whether a
parameter at time 1 is predictive of changes in cortical thickness over time ?
and can the corresponding regions be shown in FreeSurfer ?
In a statistical analysis, as we see it, we must perform the correlation
between the parameter at time 1 and the cortical thickness difference (or ROI)
time 2-time 1, yet in this case we cannot see it on the cortex.
Thank you,
Alex
--
Dr. Martin Reuter
Instructor in Neurology
Harvard Medical School
Assistant in Neuroscience
Dept. of Radiology, Massachusetts General Hospital
Dept. of Neurology, Massachusetts General Hospital
Research Affiliate
Computer Science and Artificial Intelligence Lab,
Dept. of Electrical Engineering and Computer Science,
Massachusetts Institute of Technology
A.A.Martinos Center for Biomedical Imaging
149 Thirteenth Street, Suite 2301
Charlestown, MA 02129
Phone: +1-617-724-5652
Email:
mreu...@nmr.mgh.harvard.edu
reu...@mit.edu
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