What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis

• Published in: NeuroImage (Orlando, Fla.) Vol. 97; pp. 271 - 283
• Main Authors: Davis, Tyler, LaRocque, Karen F., Mumford, Jeanette A., Norman, Kenneth A., Wagner, Anthony D., Poldrack, Russell A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 15.08.2014; Elsevier; Elsevier Limited
• Subjects: Animals; Biological and medical sciences; Codes; Computer Simulation; Dimensionality; Distributed representations; Experiments; fMRI analysis; Fundamental and applied biological sciences. Psychology; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; MVPA; Neuroimaging - methods; Simulation; Species Specificity; Variables; Vertebrates: nervous system and sense organs; Voxel-level variability; Distributed representations; Dimensionality; Voxel-level variability; fMRI analysis; MVPA; Human; Nuclear magnetic resonance imaging; Functional imaging
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.04.037

Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise analysis cannot, one conclusion that is often drawn when MVPA and univariate results differ is that the activation patterns underlying MVPA results contain a multidimensional code. In the current study, we conducted simulations to formally test this assumption. Our findings reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels. We also find that MVPA is insensitive to subject-level variability in mean activation across an ROI, which is the primary variance component of interest in many standard univariate tests. Together, these results illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code. Instead, targeted tests of the informational content and/or dimensionality of activation patterns are critical for drawing strong conclusions about the representational codes that are indicated by significant MVPA results. •Significant multi-voxel pattern analysis results may reflect multidimensional coding.•MVPA is sensitive to magnitude of spatial variability in activation.•MVPA is insensitive to subject-level variability in mean activation.•Voxel-wise analyses are sensitive to subject-level variability in mean activation.•Differences between MVPA and voxel-wise results do not indicate multidimensionality.