A Data-Driven Method to Reduce the Impact of Region Size on Degree Metrics in Voxel-Wise Functional Brain Networks

Abstract

Degree, which is the number of connections incident upon a node, measures the relative importance of the node within a network. By computing degree metrics in voxel-wise functional brain networks, many studies performed high-resolution mapping of brain network hubs using resting-state functional magnetic resonance imaging. Despite its extensive applications, defining nodes as voxels without considering the different sizes of brain regions may result in a network where the degree cannot accurately represent the importance of nodes. In this study, we designed a data-driven method to reduce this impact of the region size in degree metrics by (1) disregarding all self-connections among voxels within the same region and (2) regulating connections from voxels of other regions by the sizes of those regions. The modified method that we proposed allowed direct evaluation of the impact of the region size, showing that traditional degree metrics overestimated the degree of previous identified hubs in humans, including the visual cortex, precuneus/posterior cingulate cortex, and posterior parietal cortex, and underestimated the degree of regions including the insular cortex, anterior cingulate cortex, parahippocampus, sensory and motor cortex, and supplementary motor area. However, the locations of prominent hubs were stable even after correcting the impact. These findings were robust under different connectivity thresholds, degree metrics, data-preprocessing procedures, and datasets. In addition, our modified method improved test–retest reliability of degree metrics as well as the sensitivity in group-statistic comparisons. As a promising new tool, our method may reveal network properties that better represent true brain architecture without compromising its data-driven advantage.

Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly‐available nodes approach

“…The degree, BC, and clustering coefficient have good performances of node aggregation degree in the weighted network (Bloznelis, 2013; Sporns et al., 2007) and can well reflect the prevalence of each node and the situation around in brain network (Rubinov & Sporns, 2010). The degree k i is the number of connections of the node i (Liu & Tian, 2014). The BC is the number of shortest paths through a node.…”

Scale-Dependent Variability and Quantitative Regimes in Graph-Theoretic Representations of Human Cortical Networks

“…The present investigation also examines the behavior of five additional graph-theoretical metrics (assortativity, participation coefficient, rich-club coefficient, betweenness centrality, and global efficiency), and the comparison is made across a wider range of cortical parcel sizes [corresponding to between 2 and 50,000 nodes in this study vs. between 82 and 4000 nodes in the study of Zalesky and colleagues (2010)]. In a recent study by Liu and Tian (2014) concerning the impact of parcel size on degree metrics in functional network models, the authors proposed that a substantial proportion of previous network-theoretic studies of brain circuitry modeling overestimated the proportion of hubs in the brain. This appears to be in agreement with our own result indicating that as the spatial scale decreases, so does the rich-club coefficient of network model graphs (Fig.…”