Characteristics and variability of structural networks derived from diffusion tensor imaging

Hu Cheng, Yang Wang, Jinhua Sheng, William Kronenberger, Vincent Mathews, Tom A. Hummer, Andrew Saykin

Research output: Contribution to journalArticle

68 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1153-1164
Number of pages12
JournalNeuroImage
Volume61
Issue number4
DOIs
StatePublished - Jul 16 2012

Fingerprint

Diffusion Tensor Imaging
Weights and Measures
Reproducibility of Results
Cluster Analysis
Brain

Keywords

  • DTI
  • Structural network
  • Variability

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

Characteristics and variability of structural networks derived from diffusion tensor imaging. / Cheng, Hu; Wang, Yang; Sheng, Jinhua; Kronenberger, William; Mathews, Vincent; Hummer, Tom A.; Saykin, Andrew.

In: NeuroImage, Vol. 61, No. 4, 16.07.2012, p. 1153-1164.

Research output: Contribution to journalArticle

@article{dbf9e330d68b4912a87d0556f83cb4e5,
title = "Characteristics and variability of structural networks derived from diffusion tensor imaging",
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.",
keywords = "DTI, Structural network, Variability",
author = "Hu Cheng and Yang Wang and Jinhua Sheng and William Kronenberger and Vincent Mathews and Hummer, {Tom A.} and Andrew Saykin",
year = "2012",
month = "7",
day = "16",
doi = "10.1016/j.neuroimage.2012.03.036",
language = "English",
volume = "61",
pages = "1153--1164",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
number = "4",

}

TY - JOUR

T1 - Characteristics and variability of structural networks derived from diffusion tensor imaging

AU - Cheng, Hu

AU - Wang, Yang

AU - Sheng, Jinhua

AU - Kronenberger, William

AU - Mathews, Vincent

AU - Hummer, Tom A.

AU - Saykin, Andrew

PY - 2012/7/16

Y1 - 2012/7/16

N2 - 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.

AB - 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.

KW - DTI

KW - Structural network

KW - Variability

UR - http://www.scopus.com/inward/record.url?scp=84861329768&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84861329768&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2012.03.036

DO - 10.1016/j.neuroimage.2012.03.036

M3 - Article

VL - 61

SP - 1153

EP - 1164

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 4

ER -