Characteristics and variability of structural networks derived from diffusion tensor imaging. Neuroimage 2012 Jul 16;61(4):1153-64
Date
03/28/2012Pubmed ID
22450298Pubmed Central ID
PMC3500617DOI
10.1016/j.neuroimage.2012.03.036Scopus ID
2-s2.0-84861329768 (requires institutional sign-in at Scopus site) 99 CitationsAbstract
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.
Author List
Cheng H, Wang Y, Sheng J, Kronenberger WG, Mathews VP, Hummer TA, Saykin AJAuthors
Vincent Mathews MD Chair, Professor in the Radiology department at Medical College of WisconsinYang Wang MD Professor in the Radiology department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
BrainBrain Mapping
Diffusion Tensor Imaging
Humans
Image Interpretation, Computer-Assisted
Male
Nerve Net
Young Adult
