Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function.

Abstract

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 predictive models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify components reflecting five cognitive domains: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression (PCR) approach was then used to fit predictive models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Self-regulation, Encoding and Sequence Processing were best modelled by FC, whereas Executive Function and Language were best modelled by CC. The present study demonstrates that integrating structural and functional connectivity can help predict cognitive performance, but that the added explanatory value may be (cognitive) domain-specific. Implications of these results for studies of the brain basis of cognition in health and disease are discussed. HighlightsO_LIWe assessed the relationship between cognitive domains and structural, functional and combined structural-functional connectivity. C_LIO_LIWe found that Executive Function and Language components were best predicted by combined models of functional and structural connectivity. C_LIO_LISelf-regulation, Encoding and Sequence Processing were best predicted by functional connectivity alone. C_LIO_LIOur findings provide insight into separable contributions of functional, structural and combined connectivity to different cognitive domains. C_LI