Comparing the functional structure of neural networks from representational similarity analysis with those from functional connectivity and univariate analyses

In fMRI research, univariate analysis (UNIVAR), representational similarity analysis (RSA, following multi-voxel pattern analysis (MVPA)), and functional connectivity analysis (FCA) are the most commonly used methods by cognitive neuroscientists investigating the functional organization of the human brain. Despite their popularity, few studies have examined the relationship between the network structures as identified through these different methods. Thus, the current study aims to evaluate the similarities between neural networks derived from UNIVAR, RSA, and FCA, and to clarify how these methods relate to each other. To achieve this goal, we analyzed the data of a previously published study with the three methods and compared the results by performing (partial) correlation and multiple regression analysis. Our findings reveal that neural networks resulting from UNIVAR, RSA, and FCA methods are highly similar to each other even after ruling out the effect of anatomical proximity between the network nodes. Nevertheless, the neural network from each method shows idiosyncratic structure that cannot be explained by any of the other methods. Thus, we conclude that the UNIVAR, RSA, and FCA methods provide similar but not identical information on how brain regions are organized in functional networks.