Just above Chance: Is It Harder to Decode Information from Prefrontal Cortex Hemodynamic Activity Patterns?

The prefrontal cortex (PFC) is central to flexible, goal-directed cognition, and understanding its representational code is an important problem in cognitive neuroscience. In humans, multivariate pattern analysis (MVPA) of fMRI blood oxygenation level-dependent (BOLD) measurements has emerged as an important approach for studying neural representations. Many previous studies have implicitly assumed that MVPA of fMRI BOLD is just as effective in decoding information encoded in PFC neural activity as it is in visual cortex. However, MVPA studies of PFC have had mixed success. Here we estimate the base rate of decoding information from PFC BOLD activity patterns from a meta-analysis of published MVPA studies. We show that PFC has a significantly lower base rate (55.4%) than visual areas in occipital (66.6%) and temporal (71.0%) cortices and one that is close to chance levels. Our results have implications for the design and interpretation of MVPA studies of PFC and raise important questions about its functional organization.