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Thanks Doug. I appreciate it. I have more questions.
1) Aren't both models answering the same question regardless of which side
of the equation cortical thickness or syndromes is placed?
2) Also, I noticed in the mri_glmfit --help, it said that forward model 2
is inverted to solve for the regressor of interest. Correct if I'm wrong,
does it mean if my matrix is 0 0 0 1 where 1 represents neuropsych
syndrome, it will solve for it?
The forward model is given by:
y = W*X*B + n
where X is the Ns-by-Nb design matrix, y is the Ns-by-Nv input data
set, B is the Nb-by-Nv regression parameters, and n is noise. Ns is
the number of inputs, Nb is the number of regressors, and Nv is the
number of voxels/vertices (all cols/rows/slices). y may be surface
or volume data and may or may not have been spatially smoothed. W
is a diagonal weighing matrix.
During the estimation stage, the forward model is inverted to
solve for B:
B = inv(X'W'*W*X)*X'W'y
3) Lastly, how do I extract the beta-values after running mri_glmfit-sim
without matlab?
Many thanks,
Paul
On Tue, Feb 23, 2021 at 12:37 AM miracle ozzoude <miracoo...@gmail.com>
wrote:
> Hello Experts,
>
> I've a question about mri_glmfit. I want to investigate the association
> between thickness and neuropsychiatric syndromes, controlling for age and
> cognition. Which model below is mri_glmfit performing?
>
> Model 1
> neuropsych syndromes ~ age + cognition + cortical thickness
> or
> Model 2
> cortical thickness ~ age + cognition + neuropsych syndrome
>
> Many thanks,
> Paul
>
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