A Comparison of Functional Networks Derived From Representational Similarity, Functional Connectivity, and Univariate Analyses

Abstract

The invention of representational similarity analysis [RSA, following multi-voxel pattern analysis (MVPA)] has allowed cognitive neuroscientists to identify the representational structure of multiple brain regions, moving beyond functional localization. By comparing these structures, cognitive neuroscientists can characterize how brain areas form functional networks. Univariate analysis (UNIVAR) and functional connectivity analysis (FCA) are two other popular methods to identify functional networks. Despite their popularity, few studies have examined the relationship between networks from RSA with those from UNIVAR and FCA. Thus, the aim of the current study is to examine the similarities between neural networks derived from RSA with those from UNIVAR and FCA to explore how these methods relate to each other. 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 RSA, UNIVAR, 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 unique organization that cannot be explained by any of the other methods. Thus, we conclude that the RSA, UNIVAR and FCA methods provide similar but not identical information on how brain regions are organized in functional networks.

Linear and nonlinear profiles of weak behavioral and neural differentiation between numerical operations in children with math learning difficulties

“…The third and final aim of our study was to probe disruptions in communication of neural representations across brain regions in children at the lower end of the distribution of abilities, including those with MLD (Figure 1E&F). In a further advance over previous research in the field, we examined representational similarity at a network level and determined impairments in co-occurring patterns of deficits across multiple brain regions (Anzellotti & Coutanche, 2018;Pillet et al, 2020). This approach was used to characterize the organization of multivariate representational networks in children with low math skills and determine specific pathways of impaired communication.…”

Contextual expectations shape cortical reinstatement of sensory representations

“…Using multivariate connectivity, we tested if the representational similarity structure seen in the hippocampus was related to representational similarity seen in V1/V2 and PMC, with the inference being that correlated representational states between regions indicates representations are shared between the regions (Pillet et al, 2020). Importantly, we calculated multivariate connectivity between the hippocampus at position 3 in the sequence (a rabbit image in all sequences) and V1/V2 and PMC at position 4, meaning that in addition to testing for shared representations across regions, we further tested the idea that information is shared between past hippocampal responses and future cortical responses.…”

“…Using multivariate pattern similarity analysis, our design allows us to ask (1) which regions represent information about the currently viewed item in the sequence context, (2) are these item representations specific to a given sequence context, and (3) do regions reflecting item information also represent information about the expected item, revealed using the catch trials. To more specifically test how the hippocampus interacts with other regions to enable such predictions, we employed multivariate representational connectivity (Anzellotti and Coutanche, 2018; Kriegeskorte et al, 2008; Pillet et al, 2020) to test whether the representational similarity structure in the hippocampus at one point in the sequence related to the structure in cortical regions at a later point in the sequence, which would provide evidence that hippocampal representations relate to the reactivation of expected future activity patterns in cortex.…”

Neural similarity between mentalizing and live social interaction

“…Moreover, examining the similarity of activation patterns across brain regions can provide model-free insights about how different regions cluster or segregate in their representational content, even if they do not fit an a priori model. For example, if the response patterns across conditions for a set of brain regions are correlated, we can think of them as being "representationally connected", and can cluster brain regions based on their similarity structure to derive representational connectivity networks (27,34). Model free assessments of the raw neural response patterns can further uncover unexpected similarity structures across conditions.…”