Using graph theory as a common language to combine neural structure and function in models of healthy cognitive performance

Litwińczuk, Marta Czime ORCID: https://orcid.org/0000-0003-3532-7575, Muhlert, Nils ORCID: https://orcid.org/0000-0002-6414-5589, Trujillo-Barreto, Nelson ORCID: https://orcid.org/0000-0001-6581-7503 and Woollams, Anna ORCID: https://orcid.org/0000-0002-7400-8094 (2023) Using graph theory as a common language to combine neural structure and function in models of healthy cognitive performance. Human Brain Mapping, 44 (8). pp. 3007-3022. ISSN 1065-9471

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Abstract

Graph theory has been used in cognitive neuroscience to understand how organisational properties of structural and functional brain networks relate to cognitive function. Graph theory may bridge the gap in integration of structural and functional connectivity by introducing common measures of network characteristics. However, the explanatory and predictive value of combined structural and functional graph theory have not been investigated in modelling of cognitive performance of healthy adults. In this work, a Principal Component Regression approach with embedded Step-Wise Regression was used to fit multiple regression models of Executive Function, Self-regulation, Language, Encoding and Sequence Processing with a collection of 20 different graph theoretic measures of structural and functional network organisation used as regressors. The predictive ability of graph theory-based models was compared to that of connectivity-based models. The present work shows that using combinations of graph theory metrics to predict cognition in healthy populations does not produce a consistent benefit relative to making predictions based on structural and functional connectivity values directly.