Individualizing Representational Similarity Analysis.

Seth M Levine,Jens V Schwarzbach

doi:10.3389/fpsyt.2021.729457

Abstract

Representational similarity analysis (RSA) is a popular multivariate analysis technique in cognitive neuroscience that uses functional neuroimaging to investigate the informational content encoded in brain activity. As RSA is increasingly being used to investigate more clinically-geared questions, the focus of such translational studies turns toward the importance of individual differences and their optimization within the experimental design. In this perspective, we focus on two design aspects: applying individual vs. averaged behavioral dissimilarity matrices to multiple participants' neuroimaging data and ensuring the congruency between tasks when measuring behavioral and neural representational spaces. Incorporating these methods permits the detection of individual differences in representational spaces and yields a better-defined transfer of information from representational spaces onto multivoxel patterns. Such design adaptations are prerequisites for optimal translation of RSA to the field of precision psychiatry.

Highlights

Over the past decade, the multivariate method of Representational Similarity Analysis (RSA) [1] has become a popular means of investigating the informational content represented within patterns of activity in the brain
As representational similarity analysis is increasingly being used to investigate questions in cognitive and clinical neuroscience, it is necessary to address a few ways in which this method can be optimized for investigating both individual differences and commonalities in the context of individual differences
While the current approaches have revealed many interesting findings from a general or group-level perspective, we hold that taking advantage of individualized behavioral representational dissimilarity matrix (RDM) and controlling for task-related idiosyncrasies will allow for more informative neuroimaging investigations of differences between individuals’ mental representations

Summary

INTRODUCTION

The multivariate method of Representational Similarity Analysis (RSA) [1] has become a popular means of investigating the informational content represented within patterns of activity in the brain. A step in this direction is seen in two recent studies that combined behavioral RDMs from the multi-arrangement method with fMRI: Bracci et al asked participants to indicate whether a stimulus looked like (i.e., appearance condition) or depicted (i.e., animacy condition) an animal [70], and Tucciarelli et al asked participants to indicate when they observed the same action on two consecutive trials [31] Along these lines, the goal is to at least turn the participants’ attention toward the features that underlie their related behavioral representational spaces. This drawback may be warranted, if it results in higher sensitivity and accuracy of the analysis, which could improve the detection of individual-level information or permit subtyping of participants

CONCLUDING REMARKS

DATA AVAILABILITY STATEMENT

Methods