TY - GEN
T1 - Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer’s disease
AU - Daianu, Madelaine
AU - Jahanshad, Neda
AU - Nir, Talia M.
AU - Leonardo, Cassandra D.
AU - Jack, Clifford R.
AU - Weiner, Michael W.
AU - Bernstein, Matt A.
AU - Thompson, Paul M.
N1 - Funding Information:
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.
Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - 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 earlyand 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.
AB - 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 earlyand 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.
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U2 - 10.1007/978-3-319-11182-7_6
DO - 10.1007/978-3-319-11182-7_6
M3 - Conference contribution
AN - SCOPUS:84929492795
T3 - Mathematics and Visualization
SP - 55
EP - 64
BT - Computational Diffusion MRI - MICCAI Workshop 2014
A2 - Schneider, Torben
A2 - Reisert, Marco
A2 - O’Donnell, Lauren
A2 - Rathi, Yogesh
A2 - Nedjati-Gilani, Gemma
PB - springer berlin
T2 - MICCAI Workshop on Computational Diffusion MRI, CDMRI 2014 held under the auspices of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014
Y2 - 18 September 2014 through 18 September 2014
ER -