MVPA is not a universal solution for fMRI; it's always necessary to carefully think about what types of activity changes you want to detect.
For example, suppose these are timecourses for two people completing a task with two types of trials. It's clear that in both people there is a very strong effect of the trials: for person #1 the BOLD goes up during trial type A and down during trial type B; the reverse is true for person #2.
"Standard" MVPA (e.g. linear svm) will detect both of these patterns equally well: there is a consistent difference between trial types A and B in both people. In addition, the difference in direction is usually not reflected in the analysis: often only each subject's accuracy is taken to the second level.
This can be a feature, or a bug, depending on the hypotheses: If you want to identify regions with consistent differences in activation in each person, regardless of what those differences are, it's a feature. If you want to identify regions with a particular sort of difference in activation, it can be a bug.
My suggestion is usually that if what you want to detect is a difference in the amount of BOLD (e.g. "there will be more BOLD in this brain region during this condition compared to that condition") then it's probably best to look at some sort of mass-univariate/GLM/SPM analysis. But if you want to detect consistent differences regardless of the amount or direction of BOLD change (e.g. "the BOLD in this brain region will be different in my conditions"), then MVPA is more suitable.
Note also that a linear svm is perfectly happy to detect areas in which adjacent voxels have opposite changes in BOLD - the two timecourses above can be within the same ROI yet be detected quite well as an informative area. As before, this can be a feature or a bug. So, again, if you want to detect consistent regional differences in the overall amount of BOLD, you probably don't want to use "standard" MVPA.