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Introduction: Cognitive neuroscience explores the mechanisms of cognition by studying its structural and functional brain correlates. Many studies have combined structural and functional neuroimaging techniques to uncover the complex relationship between them. In this study, we report the first systematic review that assesses how information from structural and functional neuroimaging methods can be integrated to investigate the brain substrates of cognition. Procedure: Web of Science and Scopus databases were searched for studies of healthy young adult populations that collected cognitive data and structural and functional neuroimaging data. Results: Five percent of screened studies met all inclusion criteria. Next, 50% of included studies related cognitive performance to brain structure and function without quantitative analysis of the relationship. Finally, 31% of studies formally integrated structural and functional brain data. Overall, many studies consider either structural or functional neural correlates of cognition, and of those that consider both, they have rarely been integrated. We identified four emergent approaches to the characterization of the relationship between brain structure, function, and cognition; comparative, predictive, fusion, and complementary. Discussion: We discuss the insights provided in each approach about the relationship between brain structure and function and how it impacts cognitive performance. In addition, we discuss how authors can select approaches to suit their research questions. Impact statement The relationship between structural and functional brain networks and their relationship to cognition is a matter of current investigations. This work surveys how researchers have studied the relationship between brain structure and function and its impact on cognitive function in healthy adult populations. We review four emergent approaches of quantitative analysis of this multivariate problem; comparative, predictive, fusion, and complementary. We explain the characteristics of each approach, discuss the insights provided in each approach, and how authors can combine approaches to suit their research questions.

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve models of cognitive performance, and whether this differs by cognitive domain. Principal Component Analysis (PCA) was applied to cognitive data from the Human Connectome Project. Four components were obtained, reflecting Retention and Retrieval, Processing Speed, Self-regulation, and Encoding. The PCA-Regression approach was applied to predict cognitive performance using structural, functional and joint structural-functional components. Model quality was evaluated using model evidence, model fit and generalisability. Functional connectivity components produced the most effective models of Retention and Retrieval and Encoding, whereas joint structural-functional components produced most effective models of Processing Speed, and Self-regulation. The present study demonstrates that multimodal data fusion using structural and functional connectivity can help predict cognitive performance, but that the additional explanatory value (relative to overfitting) may depend on the specific selection of cognitive domain. We discuss the implications of these results for studies of the brain basis of cognition in health and disease.

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.

Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if parcellation schemes can systematically impact the predictive model error. In this work, structural, functional and combined connectivity were each defined with 5 parcellation schemes. Principal Component Regression with elastic net regularisation was fitted to each connectivity to predict individual differences in age, education, sex, Executive Function, Self-regulation, Language, Encoding and Sequence Processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme proved superior at predictive modelling. In addition, although parcellation schemes have influenced the global organisation of each connectivity type, this difference could not account for the error of the models. Taken together, this demonstrates that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals achieved by higher resolution of parcellation likely disrupts model generalisability.