Using computational models to relate structural and functional brain connectivity

Hlinka, Jaroslav; Coombes, Stephen

Abstract

Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead to
the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in
functional connectivity arise in a structured network of interacting neural populations is as yet poorly understood. Here we use
a modeling approach to explore the way in which this can arise and to highlight the important role that local population
dynamics can have in shaping emergent spatial functional connectivity patterns. The local dynamics for a neural population is
taken to be of the Wilson–Cowan type, whilst the structural connectivity patterns used, describing long-range anatomical
connections, cover both realistic scenarios (from the CoComac database) and idealized ones that allow for more detailed
theoretical study. We have calculated graph–theoretic measures of functional network topology from numerical simulations of
model networks. The effect of the form of local dynamics on the observed network state is quantified by examining the
correlation between structural and functional connectivity. We document a profound and systematic dependence of the
simulated functional connectivity patterns on the parameters controlling the dynamics. Importantly, we show that a weakly
coupled oscillator theory explaining these correlations and their variation across parameter space can be developed. This
theoretical development provides a novel way to characterize the mechanisms for the breakdown of functional connectivity in
diseases through changes in local dynamics.

Citation

Hlinka, J., & Coombes, S. Using computational models to relate structural and functional brain connectivity. European Journal of Neuroscience, 36(2), https://doi.org/10.1111/j.1460-9568.2012.08081.x

• Journal Article Type: Article
• Deposit Date: Mar 25, 2014
• Journal: European Journal of Neuroscience
• Print ISSN: 0953-816X
• Electronic ISSN: 0953-816X
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 36
• Issue: 2
• DOI: https://doi.org/10.1111/j.1460-9568.2012.08081.x
• Public URL: https://nottingham-repository.worktribe.com/output/1007161
• Publisher URL: http://onlinelibrary.wiley.com/doi/10.1111/j.1460-9568.2012.08081.x/pdf