Computational neuroscience: Simulating brain dynamics and generative modelling of brain networks

Research Opportunity
PhD students, Masters by Research, Post Doctor Researchers
Department / Centre
Royal Melbourne Hospital
Primary Supervisor Email Number Webpage
Associate Professor Andrew Zalesky +61390357747 Personal web page
Co-supervisor Email Number Webpage
Dr Caio Seguin Personal web page

Summary Simulate a person's brain activity based on their connectome and develop models to grow brain networks in silico

Project Details

This research theme aims to use cutting-edge network models of large-scale brain activity to gain fundamental insight into brain function in both health and psychiatric disorders. Several projects are available.

Project I. This project will use computational modelling to investigate the impact of focal perturbations on network-wide brain activity. Focal perturbations can represent the acute effects of brain stimulation, as well as long-term changes in structural brain connectivity due to disease, pharmacological interventions and training. A key advantage of this in silico approach relative to empirical experiments is that the characteristics and location of the perturbation can be systematically changed and investigated. A key application is in determining the optimal brain location to administer focal stimulation (e.g. deep brain stimulation) in order to elicit a desired change in network-wide brain activity. In this case, brain regions can be modelled as populations of inhibitory (I) and excitatory (E) neurons. These populations (nodes) are interconnected with each other according to an individual's connectome. A simple approach to model the impact of brain stimulation is to change the E-to-I ratio of the targeted node. Check out some of the seminal work by Alstott et al, 2009 and Gollo et al, 2017

Project II. Brain networks are astoundingly complex! One approach to reduce this complexity and understand brain network organization is with generative network modelling. It turns out that networks showing several of the key organizational properties of the human connectome can be grown in silico using a few simple topological rules. These include probabilistic rules to encourage connections between nodes in close spatial proximity and nodes connected to similar neighbours (homophily). The extent to which these rules are enforced can be tuned to grow networks that resemble empirically measured connectomes. The tuning parameters can be compared between populations and provide new insight into brain network organization. Check out the seminal research on generative modelling in network neuroscience by Vertes et al, 2012 and Betzel et al, 2016.

Check out our lab website for further details:

Further reading

  • Alstott J, Breakspear M, Hagmann P, Cammoun L, Sporns O. Modeling the impact of lesions in the human brain. PLoS Comput Biol. 2009 Jun;5(6):e1000408.
  • Betzel RF, Avena-Koenigsberger A, Goñi J, He Y, de Reus MA, Griffa A, Vértes PE, Mišic B, Thiran JP, Hagmann P, van den Heuvel M, Zuo XN, Bullmore ET, Sporns O. Generative models of the human connectome. Neuroimage. 2016 Jan 1;124(Pt A):1054-1064.
  • Gollo LL, Roberts JA, Cocchi L. Mapping how local perturbations influence systems-level brain dynamics. Neuroimage. 2017 Oct 15;160:97-112.
  • Vértes PE, Alexander-Bloch AF, Gogtay N, Giedd JN, Rapoport JL, Bullmore ET. Simple models of human brain functional networks. Proc Natl Acad Sci U S A. 2012 Apr 10;109(15):5868-73.

Faculty Research Themes


School Research Themes

Neuroscience & Psychiatry

Research Opportunities

PhD students, Masters by Research, Post Doctor Researchers
Students who are interested in joining this project will need to consider their elegibility as well as other requirements before contacting the supervisor of this research

Graduate Research application

Honours application

Key Contact

For further information about this research, please contact a supervisor.

Department / Centre


Research Node

Royal Melbourne Hospital

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