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,