Dynamic properties of simulated brain network models and empirical resting-state data.

Amrit Kashyap,Shella Keilholz

doi:10.1162/netn_a_00070

Abstract

Brain network models (BNMs) have become a promising theoretical framework for simulating signals that are representative of whole-brain activity such as resting-state fMRI. However, it has been difficult to compare the complex brain activity obtained from simulations to empirical data. Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the brain network model. We conclude that the dynamic properties that explicitly examine patterns of signal as a function of time rather than spatial coordination between different brain regions in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole-brain activity.

Highlights

The complex activity patterns produced by the brain are critical for understanding behavior and the function of the central nervous system
We first demonstrate that the simulated models reproduce common metrics in brain network modeling: average FC and power spectrum (Figure 2)
The Brain network models (BNMs) were simulated using the same structural connectivity as an input, randomly initialized and numerically integrated to evolve the state space according to each specific model

Summary

Introduction

The complex activity patterns produced by the brain are critical for understanding behavior and the function of the central nervous system. Studies have used resting-state fMRI (rs-fMRI) scans and functional connectivity (FC) analysis to describe the coordination between different brain regions of interest (ROIs) during rest, task, and other behavioral paradigms (Smith et al, 2009). Analysis of FC data has moved beyond looking at average statistical relationships maintained over the course of a long scan Functional connectivity: The definition of a network based on coordinated activity between different regions. There are two types of functional connectivity: average or static functional connectivity, which is estimated across long scans, and dynamic or transient functional connectivity,which is calculated over short time segments

Methods

Results

Discussion

Conclusion