In this study, we used this methodology to construct a structural brain network using the RGMV measurements.
Here, we aimed to reveal the network architecture of the directed influence brain network using multivariate Granger causality analysis and graph theory on resting-state fMRI recordings.
We also demonstrated the surface representation of the overlapping community structure in the structural brain network using the Caret software [48] (Figure 5).
Finally, leverage was evaluated in terms of its ability to detect hubs in the brain network using functional cartography methods [21].
We also computed a partition for the non-overlapping community structure in the structural brain network using the modularity optimization method [42], which was the same as the method of partitioning the maximal clique network.
After creating the brain network using the selected threshold, we then determined which nodes were connected to the largest number of other nodes, i.e. which nodes are "hubs" [1], [6].
Based on graph theory, we constructed structural brain networks using 90 cortical and subcortical regions as a set of nodes and the interregional correlations of grey matter volumes across individual brains as edges between nodes, and further analyzed the topological properties of the age-specific networks.
In this special issue, we have invited a few papers that studied structural and functional brain networks using various neuroimaging methods including resting-state fMRI, diffusion tensor imaging (DTI), arterial spin labeling (ASL) perfusion MRI, functional near-infrared spectroscopy (fNIRS), magnetoencephalogram (MEG), and imaging genetics.
The present study evaluated the topological properties of whole brain networks using graph theoretical concepts and investigated the time-evolution characteristic of brain network in mild cognitive impairment patients during a selective attention task.
The first examined the neural bases of dehumanization and its relation to these two brain networks, using images and voice-over social narratives which either implicitly contrasted or implicitly likened humans to either animals or machines.
