Using computational models to relate structural and functional brain connectivity

Stephen Coombes,Jaroslav Hlinka

doi:10.1111/j.1460-9568.2012.08081.x

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.

Highlights

Modern brain imaging methods allow the quantitative study of both local activity dynamics and the interdependency between activities in anatomically distant areas
functional connectivity (FC) is typically assessed using brain activity data acquired during a relaxed resting condition, it can be assessed from measurements taken during a particular task
We assumed a parcellation of the cerebral cortex into functional areas, such that each area corresponds to a functional unit that can be represented by a single instance of a localized neural population model

Summary

Introduction

Modern brain imaging methods allow the quantitative study of both local activity dynamics and the interdependency between activities in anatomically distant areas. The latter, known as functional connectivity (FC) analysis, is of growing interest in the clinical and experimental neuroscience community. FC is typically assessed using brain activity data acquired during a relaxed resting condition, it can be assessed from measurements taken during a particular task. The resting condition poses minimal demands on experimental preparation and the measured subject whilst still providing reliable information about a range of brain networks (Shehzad et al, 2009; Smith et al, 2009)

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Conclusion