Caveats and Nuances of Model-Based and Model-Free Representational Connectivity Analysis
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Caveats and Nuances of Model-Based and Model-Free Representational Connectivity Analysis. / Karimi-Rouzbahani, Hamid; Woolgar, Alexandra; Henson, Richard; Nili, Hamed.
In: FRONT NEUROSCI-SWITZ, Vol. 16, 755988, 10.03.2022.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Caveats and Nuances of Model-Based and Model-Free Representational Connectivity Analysis
AU - Karimi-Rouzbahani, Hamid
AU - Woolgar, Alexandra
AU - Henson, Richard
AU - Nili, Hamed
N1 - Copyright © 2022 Karimi-Rouzbahani, Woolgar, Henson and Nili.
PY - 2022/3/10
Y1 - 2022/3/10
N2 - 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.
AB - 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.
U2 - 10.3389/fnins.2022.755988
DO - 10.3389/fnins.2022.755988
M3 - SCORING: Journal article
C2 - 35360178
VL - 16
JO - FRONT NEUROSCI-SWITZ
JF - FRONT NEUROSCI-SWITZ
SN - 1662-453X
M1 - 755988
ER -