Modelling and simulation of interconnected neuronal populations
Project Leader: Andrew Zalesky
Staff: David Grayden, Tony Burkitt, Rao Kotagiri
Collaborators: Fernando Calamante (Florey Institute of Neuroscience and Mental Health), Leonardo Gollo (Queensland Institute of Medical Research), Michael Breakspear (Queensland Institute of Medical Research)
Primary Contact: Andrew Zalesky (firstname.lastname@example.org)
Keywords: Brain Connectivity; computational neuroscience; neural models; neuroimaging
Disciplines: Biomedical Engineering
Domains: Convergence of engineering and IT with the life sciences
Research Centre: Neuroengineering Research Laboratory
The spatiotemporal coordination of neural activity is constrained by the brain’s anatomy, namely, the network of axonal fibre pathways that enable communication between distant brain regions. This project aims to simulate hundreds of distinct neuronal populations that are interconnected via a biologically realistic network of axonal fibre pathways. The neuroanatomical network will be mapped using diffusion-MRI data acquired in living humans to yield a large-scale “wiring diagram” for the whole brain, telling us which neuronal populations are to be interconnected with each other in the network model. Neuronal population dynamics will be modelled and simulated using the Morris-Lecar neural mass model. The student will use the model to systematically disrupt groups of connections, as is consistent with brain disease, and investigate the consequence of these connectivity disruptions on the spatiotemporal coordination of neural activity and the brain’s functional modules. The student will then optimise and rewire the brain’s actual neuroanatomical network to improve its resilience and robustness to attack. This project will involve numerical solution of large systems of delay differential equations and is suited to a student with a background in numerical computation. VLSCI computational resources may be utilised for this purpose.
Keywords: Neural mass model, brain connectivity, diffusion-MRI, network robustness.
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