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 r

SUBMITTER: Pillet I 

PROVIDER: S-EPMC6960203 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature