Task vs. rest—different network configurations between the coactivation and the resting-state brain networks

Xin Di,Bharat B Biswal,Suril Gohel,Eun H Kim

doi:10.3389/fnhum.2013.00493

Abstract

There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest.

Highlights

The human brain exhibits organized spontaneous fluctuations in the resting-state (Biswal et al, 1995), enabling researchers to study large-scale brain segregations and integrations (Bullmore and Sporns, 2009, 2012; Menon and Uddin, 2010)
Studies have shown that task-related coactivation patterns correspond well with the brain systems that are measured during the restingstate (Toro et al, 2008; Smith et al, 2009)
Both the coactivation network and the resting-state network revealed smaller global efficiency and larger clustering coefficient compared with the reference random networks, which characterizes the small world network properties (Figure 2)

Summary

Introduction

The human brain exhibits organized spontaneous fluctuations in the resting-state (Biswal et al, 1995), enabling researchers to study large-scale brain segregations and integrations (Bullmore and Sporns, 2009, 2012; Menon and Uddin, 2010). The whole brain segregation and integration can be studied using graph theory based analysis (Bullmore and Sporns, 2009; Wang et al, 2010). Based on the economic theory of brain network organization, the brain network should be in an energy saving mode during the resting-state, while exhibiting dynamic network reconfiguration in the presence of a task demand to facilitate global and between systems information transmissions (Bullmore and Sporns, 2012). We predict that even though the connectivity in task conditions and the resting-state may be similar, substantial differences of network configurations may take place to support different task demands

Methods

Results

Conclusion