For each subject, we conducted our image analyses in a similar fashion to that described in our first paper [18] using fMRISTAT, which involves a set of four MATLAB functions that utilize the general linear model for analyses [56].
Pattern recognition utilized the generalized linear model, maximum likelihood fitting, for Poisson distributed uncertainties.
The adjoint variable method is utilized to derive the shape sensitivities by combining the general linear elastic equation with a weak imposition of Dirichlet boundary conditions.
The least squares method was used to fit the parameters for the general linear model and these parameters were utilized to generate prediction equations and best-fit lines were visualized by plotting leverage graphs using standard coding procedures.
using the general linear model procedure.
and the general linear boundary condition ℓ ( z ) = c 0. (3).
fMRI data were analyzed using the general linear model [24].
ANOVAs were examined in SPSS using the general linear model.
Statistical mapping was based on the General Linear Model [51].
The general linear model for this analysis included 13 regressors.
