Similarity representation of pattern-information fMRI

Abstract

Representational similarity analysis (RSA) is a rapidly developing multivariate platform to investigate the structure of neural activities. Similarity/dissimilarity is the core concept of RSA, realized by the construction of a representational dissimilarity matrix, that addresses the closeness/distance for each pair of research elements (e.g., one minus the correlation between the brain responses to 2 different stimuli) and in turn, constitutes a multivariate pattern as its analytic foundation. This approach is also welcome for its sensitivity in detecting subtle differences of distributed experimental effects in the brain. Importantly, RSA is not only an experimental tool but a promising data-analytical framework that can integrate cross-modal imaging signals, explore brain-behavior link, and verify computational models according to measured neural activities. RSA substantiates its integrative power by relating similarity structure in one domain (e.g., stimulus features) to that in another domain (e.g., neural activities). This review summarizes dissimilarity/similarity definition of RSA, introduces how to derive the dissimilarity structure in neural response pattern, and carry out connectivity analysis based on RSA platform. Several recent advances are highlighted, such as the extraction of across-subjects regularity, cross-validation of brain reactivity in human beings and monkeys, the incorporation of computational models and behavioral profiles into RSA. Voxel receptor field modeling, another promising multivariate tool of pattern elucidation, is presented and compared. The application of RSA is expected to surge and extend in many fields of neuroscience, computation, psychology and medicine. We also discuss the limitations of RSA and some critical questions that need to be addressed in future research.representational similarity analysis, pattern information, condition-rich experiments, decode Citation:Xue S W, Weng X C, He S, et al. Similarity representation of pattern-information fMRI.

Categorical representation from sound and sight in the ventral occipito-temporal cortex of sighted and blind

“…This approach can be considered a type of connectivity where similar RDMs of two ROIs indicate shared representational structure and therefore is supposed to be a proxy for information exchange (Kriegeskorte, Mur, Ruff, et al, 2008). Representational connectivity between two ROIs does not imply a direct structural connection but can provide connectivity information from a functional perspective, assessing to what extent two regions represent information similarly (Xue, Weng, He, & Li, 2013 to allowed the extraction of reliable dissimilarity matrices in most of the participants.…”

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

“…Second-order RSA and FCA are similar in that they are both based on a measure of the similarity between brain regions. When using correlations, correlating the averaged BOLD timeseries signals between the regions of interest (ROIs) in FCA is methodologically similar to correlating RDMs of those ROIs in second-order RSA (Xue et al, 2013). UNIVAR and RSA, or at least MVPA, have been frequently compared when describing functional properties of one region of the brain (e.g., see Jimura and Poldrack, 2012;Coutanche, 2013;Davis et al, 2014;Gilron et al, 2017).…”

Faculty Opinions recommendation of Categorical representation from sound and sight in the ventral occipito-temporal cortex of sighted and blind.

“…This approach can be considered a type of connectivity where similar RDMs of two ROIs indicate shared representational structure and therefore is supposed to be a proxy for information exchange (Kriegeskorte et al, 2008b). Representational connectivity between two ROIs does not imply a direct structural connection but can provide connectivity information from a functional perspective, assessing to what extent two regions represent information similarly (Xue et al, 2013).…”