Brain Network Structure and Dynamics

Various levels of the brain are interconnected in a hierarchical manner, with tightly interconnected local regions more loosely connected to one another to form larger assemblies, which are more loosely connected still. Some key open questions include:

  1. Why does the brain have this structure?
  2. Could evolution have selected something different?
  3. How do brain regions dynamically connect and reconnect to perform ever-changing information processing tasks? and
  4. How can we measure and quantify the structure and dynamics of networks in real brains?

We have incorporated simple but realistic models of physiology into a variety of new, more realistic, network structures to address these questions.

Our models enable the key requirement of network stability to be incorporated, and we have already found that this greatly constrains possible network structures, and hence the types of brain that could be selected for by evolution. The physiological basis of the model enables it to predict experimental observables such as electroencephalographic or functional magnetic resonance imaging (fMRI) measurements. New analysis and characterization methods are also being explored, based on field theory and matrix analysis. Numerous areas exist for PhD, MSc, or Honors projects, which could include theoretical, computational, and/or data-related components in cooperation with our international and local collaborators.

Structural connection matrices.

Schematic connection matrices (CMs) of networks, with neural populations labeling rows and columns, and white entries for a connection between a given row and column. (a) Regular network. (b) Random network. (c) Small-world network. (d) Hierarchical network (HN) shaded to show levels. (e) Example HN. (f) Cat cortex CM. Image taken from [1].

KEY PUBLICATIONS

  1. Robinson, P. A., Henderson, J. A., Matar, E., Riley, P., and Gray, R. T. (2009). Dynamical Reconnection and Stability Constraints on Cortical Network Architecture, Physical Review Letters, 103, 108104, 1-4.
  2. Henderson, J. A., & Robinson, P. A. (2011). Geometric effects on complex network structure in the cortex. Physical review letters, 107(1), 018102.
  3. Gray, R., Robinson, P. (2013). Stability constraints on large-scale structural brain networks. Frontiers in Computational Neuroscience, 7, 1-13.
  4. Sarkar, S., Henderson, J., Robinson, P. (2013). Spectral Characterization of Hierarchical Network Modularity and Limits of Modularity Detection. PLoS One, 8(1), 1-11.