Caveats and nuances of model-based and model-free representational connectivity analysis

Hamid Karimi-Rouzbahani*, Alexandra Woolgar, Richard Henson, Hamed Nili

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
10 Downloads (Pure)

Abstract

Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarizing activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions, and transformation of representational structure across brain regions. The article is accompanied by scripts (https://osf.io/3nxfa/) that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively.

Original languageEnglish
Article number755988
Pages (from-to)1-13
Number of pages13
JournalFrontiers in Neuroscience
Volume16
DOIs
Publication statusPublished - 10 Mar 2022

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Erratum published in Frontiers in Neuroscience volume 16, 23 June 2023. https://doi.org/10.3389/fnins.2022.957057

Keywords

  • functional connectivity
  • multi-dimensional connectivity
  • multivariate pattern analysis
  • representational connectivity analysis
  • representational similarity analysis

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