Format

Send to

Choose Destination
See comment in PubMed Commons below
Neurobiol Aging. 2015 Jan;36(1):401-12. doi: 10.1016/j.neurobiolaging.2014.06.019. Epub 2014 Jun 21.

Weighted brain networks in disease: centrality and entropy in human immunodeficiency virus and aging.

Author information

  • 1Department of Neurology, Washington University in St Louis, School of Medicine, St. Louis, MO, USA.
  • 2Department of Radiology, Washington University in St Louis, School of Medicine, St. Louis, MO, USA; Hope Center for Neurologic Diseases, Washington University in St Louis, School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St Louis, School of Medicine, St. Louis, MO, USA.
  • 3Department of Neurology, Washington University in St Louis, School of Medicine, St. Louis, MO, USA; Department of Radiology, Washington University in St Louis, School of Medicine, St. Louis, MO, USA; Hope Center for Neurologic Diseases, Washington University in St Louis, School of Medicine, St. Louis, MO, USA; Knight Alzheimer's Disease Research Center, Washington University in St Louis, School of Medicine, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO, USA. Electronic address: bances@wustl.edu.

Abstract

Graph theory models can produce simple, biologically informative metrics of the topology of resting-state functional connectivity (FC) networks. However, typical graph theory approaches model FC relationships between regions (nodes) as unweighted edges, complicating their interpretability in studies of disease or aging. We extended existing techniques and constructed fully connected weighted graphs for groups of age-matched human immunodeficiency virus (HIV) positive (n = 67) and HIV negative (n = 77) individuals. We compared test-retest reliability of weighted versus unweighted metrics in an independent study of healthy individuals (n = 22) and found weighted measures to be more stable. We quantified 2 measures of node centrality (closeness centrality and eigenvector centrality) to capture the relative importance of individual nodes. We also quantified 1 measure of graph entropy (diversity) to measure the variability in connection strength (edge weights) at each node. HIV was primarily associated with differences in measures of centrality, and age was primarily associated with differences in diversity. HIV and age were associated with divergent measures when evaluated at the whole graph level, within individual functional networks, and at the level of individual nodes. Graph models may allow us to distinguish previously indistinguishable effects related to HIV and age on FC.

Copyright © 2015 Elsevier Inc. All rights reserved.

KEYWORDS:

Aging fc-MRI; Centrality; Graph theory; HIV; Neurodegeneration

[PubMed - indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science Icon for PubMed Central
    Loading ...
    Write to the Help Desk