The proposed modeling strategies have been implemented into the open-source program 3dMVM in AFNI, which offers the investigator all the testing results in the output including XUV and the auxiliary approaches (MVT, XMV, and AUC). MVT for the components from ESM presents a unique challenge when one or more within-subject factors are included in the model, and we offer a testing strategy that still fits in the MVM framework (Appendix B). As an alternative, these tests could be conducted in the traditional univariate GLM except for the two multivariate methods, MVT and XMV. In other words, some of the testing methods (MVT and XMV) are truly multivariate, while others (XUV, AUV, and L2D) are essentially univariate. However, as we demonstrated in Chen et al. (2014), these univariate tests are sometimes difficult to perform under the univariate framework, as shown by the implementation challenges faced by some of the neuroimaging packages. Instead, these univariate tests can be more conveniently formulated under the MVM platform by treating the levels of each within subject factor as simultaneous variables in (1) and then constructing the univariate testing statistics through a conversion process. For example, those univariate tests presented in Figure 2 cannot be performed under the univariate GLM framework due to the incorporation of a covariate (age) in the presence of two within subject factors (Condition and HDR effects). It is in this sense that we frame our discussion here under the MVM perspective.