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> <mailto: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 thecorresponding 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-time1, 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
Web : http://reuter.mit.edu
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