Task-related changes in functional properties of the human brain network underlying attentional control

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 nonparametric 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.

The Tactile Window to Consciousness is Characterized by Frequency-Specific Integration and Segregation of the Primary Somatosensory Cortex

“…The use of graph theory is a promising approach to analyse functional neural networks, particularly during cognitive tasks. For instance, recent studies have reported changes in graph theoretical metrics during an active working memory task 32 , mathematical processing 33 , attention deployment 34 , and perception 9 16 35 . In particular, based on a visual oddball task, increased clustering, decreased modularity, and increased rich-club coefficients due to stronger interactions between hub-like nodes were proposed as the functional ‘fingerprint’ of cognition 35 .…”

Multi-Dimensional Dynamics of Human Electromagnetic Brain Activity

“…This metric is a computationally expense among centrality metrics but is very popular. A large number of neuroimaging studies have successfully used betweenness centrality to reflect the importance of a network (Hipp et al, 2012 ; Kida and Kakigi, 2013 ; Kida et al, 2013 ) and degree distribution. Computations of betweenness centrality and closeness centrality are both based on the computation of short paths.…”

“…Therefore, larger numbers of vertices are preferable for computing the brain-wide analysis of task-related, rapid changes in graph metrics in MEG and EEG studies. A preliminary MEG study reported task-related, frequency-specific, rapid temporal changes in the brain-wide network organization by examining graph metrics computed from full source-space data (about 900 vertices) in a multisensory attention task (Kida et al, 2013 ). The findings of this study indicated the possibility of the cortical dynamics of functional networks showing frequency-specific, sub-second rapid temporal changes depending on the periods of task execution.…”

Brain‐wide network analysis of resting‐state neuromagnetic data

“…These studies employed arbitrary dipole spacing, which is common in beamformer reconstruction (Hipp et al, 2012 ), and atlas‐based dipole spacing by dividing anatomical labels into subregions (Khan et al, 2018 ). Moreover, a limited number of studies have reported a brain‐wide exhaustive analysis of resting‐ and task‐state MEG connectivity or graphs with a higher spatial resolution (approximately 1000 dipoles and more) using bias‐corrected metrics (Kida et al, 2013 ; Sanchez‐Bornot et al, 2021 ; Siems et al, 2016 ; Siems & Siegel, 2020 ; Youssofzadeh et al, 2017 ). This type of high‐resolution graph‐theory analysis along with bias‐corrected connectivity metrics allows an elaborate map of the functional features of the human brain network to be visualized (Hipp et al, 2012 ; Kida et al, 2015 ; Sanchez‐Bornot et al, 2021 ) and the map to be associated with demographics or cognitive performance.…”