title: Effects of lesions on synchrony and metastability in cortical networks.
abstract: At the macroscopic scale, the human brain can be described as a complex network of white matter tracts integrating grey matter assemblies - the human connectome. The structure of the connectome, which is often described using graph theoretic approaches, can be used to model macroscopic brain function at low computational cost. Here, we use the Kuramoto model of coupled oscillators with time-delays, calibrated with respect to empirical functional MRI data, to study the relation between the structure of the connectome and two aspects of functional brain dynamics - synchrony, a measure of general coherence, and metastability, a measure of dynamical flexibility. Specifically, we investigate the relationship between the local structure of the connectome, quantified using graph theory, and the synchrony and metastability of the model's dynamics. By removing individual nodes and all of their connections from the model, we study the effect of lesions on both global and local dynamics. Of the nine nodal graph-theoretical properties tested, two were able to predict effects of node lesion on the global dynamics. The removal of nodes with high eigenvector centrality leads to decreases in global synchrony and increases in global metastability, as does the removal of hub nodes joining topologically segregated network modules. At the level of local dynamics in the neighbourhood of the lesioned node, structural properties of the lesioned nodes hold more predictive power, as five nodal graph theoretical measures are related to changes in local dynamics following node lesions. We discuss these results in the context of empirical studies of stroke and functional brain dynamics.
authors.affiliation: Computational, Cognitive & Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London, UK; Department of Computing, Imperial College London, London, UK. Electronic address: fv247@cam.ac.uk.
authors.forename: František
authors.initials: F
authors.lastname: Váša
authors.affiliation: Department of Computing, Imperial College London, London, UK.
authors.forename: Murray
authors.initials: M
authors.lastname: Shanahan
authors.affiliation: Computational, Cognitive & Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London, UK; Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London, UK.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Hellyer
authors.affiliation: Computational, Cognitive & Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London, UK.
authors.forename: Gregory
authors.initials: G
authors.lastname: Scott
authors.affiliation: Department of Psychiatry, University of Oxford, Oxford, UK.
authors.forename: Joana
authors.initials: J
authors.lastname: Cabral
authors.affiliation: Computational, Cognitive & Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London, UK.
authors.forename: Robert
authors.initials: R
authors.lastname: Leech
mesh_terms: D002540:Cerebral Cortex / Q000293:injuries* / Q000503:physiopathology*; D003198:Computer Simulation; D063132:Connectome; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*
keywords: Connectome; Graph theory; Kuramoto model; Metastability; Neural dynamics; Stroke