Characteristics and variability of structural networks derived from diffusion tensor imaging - PubMed (original) (raw)
Characteristics and variability of structural networks derived from diffusion tensor imaging
Hu Cheng et al. Neuroimage. 2012.
Abstract
Structural brain networks were constructed based on diffusion tensor imaging (DTI) data of 59 young healthy male adults. The networks had 68 nodes, derived from FreeSurfer parcellation of the cortical surface. By means of streamline tractography, the edge weight was defined as the number of streamlines between two nodes normalized by their mean volume. Specifically, two weighting schemes were adopted by considering various biases from fiber tracking. The weighting schemes were tested for possible bias toward the physical size of the nodes. A novel thresholding method was proposed using the variance of number of streamlines in fiber tracking. The backbone networks were extracted and various network analyses were applied to investigate the features of the binary and weighted backbone networks. For weighted networks, a high correlation was observed between nodal strength and betweenness centrality. Despite similar small-worldness features, binary networks and weighted networks are distinctive in many aspects, such as modularity and nodal betweenness centrality. Inter-subject variability was examined for the weighted networks, along with the test-retest reliability from two repeated scans on 44 of the 59 subjects. The inter-/intra-subject variability of weighted networks was discussed in three levels - edge weights, local metrics, and global metrics. The variance of edge weights can be very large. Although local metrics show less variability than the edge weights, they still have considerable amounts of variability. Weighting scheme one, which scales the number of streamlines by their lengths, demonstrates stable intra-class correlation coefficients against thresholding for global efficiency, clustering coefficient and diversity. The intra-class correlation analysis suggests the current approach of constructing weighted network has a reasonably high reproducibility for most global metrics.
Copyright © 2012 Elsevier Inc. All rights reserved.
Figures
Fig. 1
Top panel is the inter-subject variability for 59 subjects showing mean and standard deviation of the ROI size of the nodes (a) and correlation matrix of the ROI sizes between subjects (b); Bottom panel is the bias test of the weighting scheme showing the scatter plots of standard deviation of nodal strength and nodal ROI size, with weighting scheme 1 (c) and weighting scheme 2 (d). The cross correlation coefficient between nodal strength and node size is −0.06 in (c) and −0.14 in (d).
Fig. 2
Matrix and graph representations of the backbone networks. Top row: network in matrix forms for a) BBN1; b) BBN2; c) WBN1; d) WBN2; corresponding graph forms are shown in the bottom. In matrix representations, the nodes and edges are clustered into different modules, which are distinguished by different colors in graph representations. The node positions in the graph is an approximate 2D mapping of the physical location of the ROIs in the brain, left corresponds to the left hemisphere, right corresponds to the right hemisphere, up is front and bottom is posterior.
Fig. 3
a) Degree distribution for WBN1 (blue) and WBN2 (red). The histogram of weights are displayed in b) for WBN1 and c) for WBN2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 4
Comparison of node betweenness centrality between WBN1 and BBN1 (a) and WBN1 and WBN2 (b). The order of nodes is based on the order of betweenness centrality in WBN1.
Fig. 5
Scatter plots of the nodal mean strength (nodal strength divided by nodal degree) and nodal betweenness centrality (a, b); nodal mean strengths and nodal degree (c, d) for WBN1 and WBN2.
Fig. 6
Thresholding effects for three global metrics: clustering coefficient (a), maximized modularity (b), and efficiency (c), plotted as functions of total degree after thresholding.
Fig. 7
Intra-subject variability characterized by correlation of network edge weights between two scans (a, b), and scatter plots of global metrics between two scans (c, d) for both weighting schemes. The correlation coefficients of the global metrics across subjects are listed in (c) and (d).
Fig. 8
Inter-subject variability for WS 1 characterized by correlation of network edge weights between subjects (a); and coefficient of variation of the edge weights (b).
Fig. 9
Mean values and standard deviation of local network metrics for each node across 59 subjects with WS 1: (a) strength; (b) betweenness centrality; (c) path length; (d) clustering coefficient.
Fig. 10
Plot of the Intra-class correlation coefficient for different network metrics as a function of threshold of fiber counts in constructing the weighted network with weighting scheme 1 (A) and weighting scheme 2 (B).
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