Abstract
Previous studies have demonstrated task-related changes in brain activation and inter-regional connectivity but the temporal dynamics of functional properties of the brain during task execution is still unclear. In the present study, we investigated task-related changes in functional properties of the human brain network by applying graph-theoretical analysis to magnetoencephalography (MEG). Subjects performed a cue-target attention task in which a visual cue informed them of the direction of focus for incoming auditory or tactile target stimuli, but not the sensory modality. We analyzed the MEG signal in the cue-target interval to examine network properties during attentional control. Cluster-based non-parametric permutation tests with the Monte-Carlo method showed that in the cue-target interval, beta activity was desynchronized in the sensori-motor region including premotor and posterior parietal regions in the hemisphere contralateral to the attended side. Graph-theoretical analysis revealed that, in beta frequency, global hubs were found around the sensori-motor and prefrontal regions, and functional segregation over the entire network was decreased during attentional control compared to the baseline. Thus, network measures revealed task-related temporal changes in functional properties of the human brain network, leading to the understanding of how the brain dynamically responds to task execution as a network.
Highlights
A large body of studies in neuroscience have investigated taskrelated changes in activation of different brain regions to infer functional specialization
Studies started to demonstrate how the brain works as a functional network or a set of sub-networks using functional magnetic resonance imaging [8,9,10,11,12,13] and magnetoencephalogaphy (MEG) [14,15,16,17,18,19,20]
The clustering coefficient is a network measure of functional segregation primarily quantifying the presence of interconnected groups of brain regions, whereas betweenness centrality is a measure of centrality, which is considered to act as an important control of information flow [22]
Summary
A large body of studies in neuroscience have investigated taskrelated changes in activation of different brain regions to infer functional specialization. The clustering coefficient is a network measure of functional segregation primarily quantifying the presence of interconnected groups of brain regions, whereas betweenness centrality is a measure of centrality (global hub), which is considered to act as an important control of information flow [22]. Most of these studies using network measures examined functional properties of the brain network in a resting state, i.e., the default-mode network [14,15,19], yet task-related temporal changes in functional properties of the human brain network remain unclear
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