Representational Connectivity Analysis: Identifying Networks of Shared Changes in Representational Strength through Jackknife Resampling

Abstract

Acknowledgments:We thank Mac Shine for invaluable discussions during the conception of this project, and thank Heather Bruett, Griffin Koch, John Paulus and Xueying Ren for conversations related to the work. We are also grateful to Samuel Nastase and co-authors for making their dataset available. AbstractThe structure of information in the brain is crucial to cognitive function. The representational space of a brain region can be identified through Representational Similarity Analysis (RSA) applied to functional magnetic resonance imaging (fMRI) data. In its classic form, RSA collapses the time-series of each condition, eliminating fluctuations in similarity over time. We propose a method for identifying representational connectivity (RC) networks, which share fluctuations in representational strength, in an analogous manner to functional connectivity (FC), which tracks fluctuations in BOLD signal, and informational connectivity, which tracks fluctuations in pattern discriminability. We utilize jackknife resampling, a statistical technique in which observations are removed in turn to determine their influence. We applied the jackknife technique to an existing fMRI dataset collected as participants viewed videos of animals (Nastase et al., 2017). We used ventral temporal cortex (VT) as a seed region, and compared the resulting network to a second-order RSA, in which brain regions' representational spaces are compared, and to the network identified through FC. The novel representational connectivity analysis identified a network comprising regions associated with lower-level visual processing, spatial cognition, perceptual-motor integration, and visual attention, indicating that these regions shared fluctuations in representational similarity strength with VT. RC, second-order RSA and FC identified areas unique to each method, indicating that analyzing shared fluctuations in the strength of representational similarity reveals previously undetectable networks of regions. The RC analysis thus offers a new way to understand representational similarity at the network level.

Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks

“…Hence, NSAR as a connectivity and surface area-based measure is more reflective of functional rather than anatomical lateralization. As a result, future studies might benefit from exploring the Coutanche et al (2023) method, which employs a surface-fingerprinting technique and multivariate laterality index for computing functional lateralization, offering a potentially complementary approach to NSAR in assessing functional lateralization.…”

Caveats and Nuances of Model-Based and Model-Free Representational Connectivity Analysis

“…One approach to multi-dimensional connectivity is Representational Connectivity Analysis (RCA; Kriegeskorte et al, 2008 ), which utilizes the versatility of Representational Similarity Analysis (RSA) to move from the direct comparison of representations to the comparison of representational geometries ( Kriegeskorte et al, 2008 ). Recent implementations of RCA can be divided into model-free [e.g., Information Flow Analysis ( Goddard et al, 2016 ), RSA-Granger Analysis ( Kietzmann et al, 2019 ), static RSA ( Karimi-Rouzbahani et al, 2021c ), and jackknife-resampling RCA ( Coutanche et al, 2020 )] and model-based ( Clarke et al, 2018 ; Karimi-Rouzbahani et al, 2021a ) methods, each having specific characteristics. Here, we describe model-free and model-based RCA and point out their differences.…”

“…From a broad perspective, model-free RCA ( Basti et al, 2020 ; Coutanche et al, 2020 ; Karimi-Rouzbahani et al, 2021c ; Shahbazi et al, 2021 ) evaluates whether there is any commonality in the distributed patterns of activity for two brain regions. The commonality might reflect shared information due to similar encoding in the two regions.…”

Functional connectivity and information pathways in the human entorhinal-hippocampal circuitry

“…Moreover, recent methodological advances can be employed in the future that study functional connectivity based on the underlying content representations between regions (e.g. Coutanche et al, 2020).…”