Individualizing Representational Similarity Analysis

Seth M Levine; Jens V Schwarzbach

doi:10.3389/fpsyt.2021.729457

Individualizing Representational Similarity Analysis

Front Psychiatry. 2021 Oct 11:12:729457. doi: 10.3389/fpsyt.2021.729457. eCollection 2021.

Authors

Seth M Levine¹, Jens V Schwarzbach²

Affiliations

¹ Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
² Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.

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.

Keywords: fMRI; individual differences; multivariate pattern analysis; precision psychiatry; representational similarity analysis; task-based imaging.