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Algebraic Connectivity of Brain Networks Shows Patterns of Segregation Leading to Reduced Network Robustness in Alzheimer’s Disease

Conference paper
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Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Measures of network topology and connectivity aid the understanding of network breakdown as the brain degenerates in Alzheimer’s disease (AD). We analyzed 3-Tesla diffusion-weighted images from 202 patients scanned by the Alzheimer’s Disease Neuroimaging Initiative—50 healthy controls, 72 with early- and 38 with late-stage mild cognitive impairment (eMCI/lMCI) and 42 with AD. Using whole-brain tractography, we reconstructed structural connectivity networks representing connections between pairs of cortical regions. We examined, for the first time in this context, the network’s Laplacian matrix and its Fiedler value, describing the network’s algebraic connectivity, and the Fiedler vector, used to partition a graph. We assessed algebraic connectivity and four additional supporting metrics, revealing a decrease in network robustness and increasing disarray among nodes as dementia progressed. Network components became more disconnected and segregated, and their modularity increased. These measures are sensitive to diagnostic group differences, and may help understand the complex changes in AD.

Keywords

Mini Mental State Examination Diffusion Weighted Imaging Brain Network Laplacian Matrix Mini Mental State Examination Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 ADNI (NIH Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through contributions from the following: Abbott; Alzheimer's Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. This research was also supported by NIH grants P30 AG010129 and K01 AG030514 from the National Institute of General Medical Sciences. 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

  1. 1.Imaging Genetics Center, Institute for Neuroimaging & InformaticsUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of RadiologyMayo ClinicRochesterUSA
  3. 3.Department of Radiology, Medicine, and PsychiatryUniversity of CaliforniaSan FranciscoUSA

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