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Rich Club Network Analysis Shows Distinct Patterns of Disruption in Frontotemporal Dementia and Alzheimer’s Disease

  • Madelaine Daianu
  • Neda Jahanshad
  • Julio E. Villalon-Reina
  • Mario F. Mendez
  • George Bartzokis
  • Elvira E. Jimenez
  • Aditi Joshi
  • Joseph Barsuglia
  • Paul M. Thompson
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Diffusion imaging and brain connectivity analyses can reveal the underlying organizational patterns of the human brain, described as complex networks of densely interlinked regions. Here, we analyzed 1.5-Tesla whole-brain diffusion-weighted images from 64 participants—15 patients with behavioral variant frontotemporal (bvFTD) dementia, 19 with early-onset Alzheimer’s disease (EOAD), and 30 healthy elderly controls. Based on whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We examined how bvFTD and EOAD disrupt the weighted ‘rich club’—a network property where high-degree network nodes are more interconnected than expected by chance. bvFTD disrupts both the nodal and global organization of the network in both low- and high-degree regions of the brain. EOAD targets the global connectivity of the brain, mainly affecting the fiber density of high-degree (highly connected) regions that form the rich club network. These rich club analyses suggest distinct patterns of disruptions among different forms of dementia.

Keywords

Alzheimer’s disease Connectivity DWI Frontotemporal dementia Network Rich club 

Notes

Acknowledgments

Algorithm development and image analysis for this study was funded, in part, by grants to PT from the NIBIB (R01 EB008281, R01 EB008432) and by the NIA, NIBIB, NIMH, the National Library of Medicine, and the National Center for Research Resources (AG016570, AG040060, EB01651, MH097268, LM05639, RR019771 to PT). Data collection and sharing for this project was funded by NIH Grant 5R01AG034499-05. This work was also supported in part by a Consortium grant (U54 EB020403) from the NIH Institutes contributing to the Big Data to Knowledge (BD2K) Initiative, including the NIBIB and NCI.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Madelaine Daianu
    • 1
  • Neda Jahanshad
    • 1
  • Julio E. Villalon-Reina
    • 1
  • Mario F. Mendez
    • 2
  • George Bartzokis
    • 2
  • Elvira E. Jimenez
    • 2
  • Aditi Joshi
    • 2
  • Joseph Barsuglia
    • 2
  • Paul M. Thompson
    • 1
  1. 1.Imaging Genetics Center, Institute for Neuroimaging & InformaticsUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Alzheimer’s Disease Research Center, Department of NeurologyUCLA School of MedicineLos AngelesUSA

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