Quantitative evaluation of simulated functional brain networks in graph theoretical analysis

Won Hee Lee,Ed Bullmore,Sophia Frangou

doi:10.1016/j.neuroimage.2016.08.050

Abstract

There is increasing interest in the potential of whole-brain computational models to provide mechanistic insights into resting-state brain networks. It is therefore important to determine the degree to which computational models reproduce the topological features of empirical functional brain networks. We used empirical connectivity data derived from diffusion spectrum and resting-state functional magnetic resonance imaging data from healthy individuals. Empirical and simulated functional networks, constrained by structural connectivity, were defined based on 66 brain anatomical regions (nodes). Simulated functional data were generated using the Kuramoto model in which each anatomical region acts as a phase oscillator. Network topology was studied using graph theory in the empirical and simulated data. The difference (relative error) between graph theory measures derived from empirical and simulated data was then estimated. We found that simulated data can be used with confidence to model graph measures of global network organization at different dynamic states and highlight the sensitive dependence of the solutions obtained in simulated data on the specified connection densities. This study provides a method for the quantitative evaluation and external validation of graph theory metrics derived from simulated data that can be used to inform future study designs.

Highlights

Graph theory has been widely used to assess the topological properties of structural and functional brain networks inferred from neuroimaging data (Bullmore and Sporns, 2009; Bullmore and Bassett, 2011; Rubinov and Sporns, 2010)
We used a calibrated Kuramoto model based on 66 cortical regions constrained by white matter structural connectivity
We assessed the presence of critical synchronization using a power-law probability distribution of phase-lock intervals (PLI) derived from simulated fMRI data

Summary

Introduction

Graph theory has been widely used to assess the topological properties of structural and functional brain networks inferred from neuroimaging data (Bullmore and Sporns, 2009; Bullmore and Bassett, 2011; Rubinov and Sporns, 2010). Computational studies coupled with empirical neuroimaging data have demonstrated the role of multiple time-scales in the patterns of functional connectivity (Honey et al, 2007; Rubinov et al, 2009), the emergence of resting-state activity from the local dynamics through structural connections of a small-world organized network (Honey et al, 2007), and the structure–function relation of resting-state networks (Honey et al, 2009) They have identified the role of local network oscillations (Cabral et al, 2011; Deco et al, 2009) and the contributions of coupling strength, signal propagation delay, and noise, to the activity and organization of resting-state networks (Deco et al, 2009; Ghosh et al, 2008a, 2008b). Whole-brain models have been applied to neurological and psychiatric disorders to examine the impact of disrupted structural connectivity on neural dynamics (Adhikari et al, 2015; Alstott et al, 2009; Honey and Sporns, 2008; van Hartevelt et al, 2014; Vasa et al, 2015) and on disease states (Cabral et al, 2012a, 2012b, 2013)

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Conclusion