Distinguishing multi-voxel patterns and mean activation: Why, how, and what does it tell us?

Abstract

The introduction of multi-voxel pattern analysis (MVPA) to the functional magnetic resonance imaging (fMRI) community has brought a deeper appreciation for the diverse forms of information that can be present within fMRI activity. The conclusions drawn from MVPA investigations are frequently influenced by both the ability to decode information from multi-voxel patterns and mean activation levels. In practice, MVPA studies vary widely in why and how they test for differing overall response levels. In this article, I examine the place of univariate information in MVPA investigations. I first discuss the variety of interpretations given to finding univariate response differences. I go on to discuss some of the analysis approaches used to investigate and compare univariate and multivariate sources of information, which can illuminate their respective contributions. It will be important for the MVPA and general fMRI community to continue to discuss and debate these important issues.