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 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
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Dr. Martin Reuter
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Harvard Medical School
Assistant in Neuroscience
Dept. of Radiology, Massachusetts General Hospital
Dept. of Neurology, Massachusetts General Hospital
Research Affiliate
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Dept. of Electrical Engineering and Computer Science,
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reu...@mit.edu
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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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