Combining network topology and information theory to construct representative brain networks.

Abstract

<jats:p> Network neuroscience employs graph theory to investigate the human brain as a complex network, and derive generalisable insights about the brain’s network properties. However, graph-theoretical results obtained from network construction pipelines that produce idiosyncratic networks, may not generalise when alternative pipelines are employed. This issue is especially pressing because a wide variety of network construction pipelines have been employed in the human network neuroscience literature, making comparisons between studies problematic. </jats:p><jats:p> Here, we investigate how to produce networks that are maximally representative of the broader set of brain networks obtained from the same neuroimaging data. We do so by minimising an information-theoretic measure of divergence between network topologies, known as the Portrait Divergence. </jats:p><jats:p> Based on functional and diffusion MRI data from the Human Connectome Project, we consider anatomical, functional and multimodal parcellations at three different scales, and 48 distinct ways of defining network edges. We show that the highest representativeness can be obtained by using parcellations in the order of 200 regions and filtering functionalnetworks based on Efficiency-Cost Optimisation – though suitable alternatives are also highlighted. Overall, we identify specific node definition and thresholding procedures that neuroscientists can follow in order to derive representative networks from their human neuroimaging data. </jats:p>