When Multi-Voxel Pattern Similarity and Global Activation are Intertwined: Approaches to Disentangling Correlation from Activation
Authors: LaRocque Karen F. , Davis Tyler H. , Mumford Jeanette A. , Poldrack Russell A. , Wagner Anthony D. 
Published: May 2023

Pattern similarity analysis, which uses correlation to examine similarities between neural activation patterns evoked by different trials or conditions, are often leveraged to test hypotheses not easily answerable with univariate comparisons, such ashowevents are represented or processed and therelationshipsbetween representations or processing of events. In principle, univariate analyses of global activation and multivariate analyses of pattern similarity can be used to answer substantively different questions about psychological and neural processing. For this to hold, it is necessary that pattern similarity estimates are not contaminated by differences in global activation across experimental events. Here, we report simulated data that demonstrate that global activation and pattern similarity (as assessed by correlation), although theoretically independent, are often intertwined. We present two plausible scenarios that illustrate how condition-specific changes in global activation can elicit condition-specific increases in pattern similarity by interacting with underlying across-voxel activation patterns. First, we consider a scenario in which a target region contains subpopulations of voxels such that only some voxels in a region are sensitive to a psychological variable and the remaining voxels are not modulated by this variable. In this scenario, this spatial pattern of responsive and unresponsive voxelsadds new, shared across-voxel variabilityfor events in the ‘active’ condition, thereby increasing pattern similarity between these events. Second, we consider a scenario in which trials from all conditions elicit a shared across-voxel pattern of activation, but this shared across-voxel pattern is amplified for trials within one condition due to greater global activation. In this scenario, the change in activation for a given conditionincreases the ability to detect pre-existing, shared across-voxel variabilityacross events in that condition, thereby increasing pattern similarity between these events. Given the observed influence of global activation on pattern similarity, we then assess whether it is possible to statistically separate the contributions of global activation and pattern similarity to observed activation patterns (using regression approaches, matching activation across conditions, and inclusion of control conditions). Additional simulations demonstrate that use of these techniques is not always effective in removing the influence of global activation on pattern similarity ––the efficacy of these techniques depends on a variety of signal parameters that will likely vary across experiments and participants, highlighting the need for tailored control analyses that are targeted at addressing the particular hypotheses and potential global activation confounds of a given experiment.

[Note:The reported simulations and this resulting white paper were generated in 2014. We share, without update, this paper given the continued relevance of understanding and controlling for global activation confounds when conducting multi-variate pattern analyses.]