Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer’s disease

Madelaine Daianu; Neda Jahanshad; Talia M. Nir; Cassandra D. Leonardo; Clifford R. Jack; Michael W. Weiner; Matt A. Bernstein; Paul M. Thompson

doi:10.1007/978-3-319-11182-7_6

Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer’s disease

Madelaine Daianu, Neda Jahanshad, Talia M. Nir, Cassandra D. Leonardo, Clifford R. Jack, Michael W. Weiner, Matt A. Bernstein, Paul M. Thompson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Scopus citations

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 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.

Original language	English (US)
Title of host publication	Computational Diffusion MRI - MICCAI Workshop 2014
Editors	Torben Schneider, Marco Reisert, Lauren O’Donnell, Yogesh Rathi, Gemma Nedjati-Gilani
Publisher	springer berlin
Pages	55-64
Number of pages	10
ISBN (Electronic)	9783319111810
DOIs	https://doi.org/10.1007/978-3-319-11182-7_6
State	Published - 2014
Event	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 - Boston, United States Duration: Sep 18 2014 → Sep 18 2014

Publication series

Name	Mathematics and Visualization
Volume	39
ISSN (Print)	1612-3786
ISSN (Electronic)	2197-666X

Other

Other	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
Country/Territory	United States
City	Boston
Period	9/18/14 → 9/18/14

ASJC Scopus subject areas

Modeling and Simulation
Geometry and Topology
Computer Graphics and Computer-Aided Design
Applied Mathematics

Access to Document

10.1007/978-3-319-11182-7_6

Cite this

Daianu, M., Jahanshad, N., Nir, T. M., Leonardo, C. D., Jack, C. R., Weiner, M. W., Bernstein, M. A., & Thompson, P. M. (2014). Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer’s disease. In T. Schneider, M. Reisert, L. O’Donnell, Y. Rathi, & G. Nedjati-Gilani (Eds.), Computational Diffusion MRI - MICCAI Workshop 2014 (pp. 55-64). (Mathematics and Visualization; Vol. 39). springer berlin. https://doi.org/10.1007/978-3-319-11182-7_6

Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer’s disease. / Daianu, Madelaine; Jahanshad, Neda; Nir, Talia M. et al.
Computational Diffusion MRI - MICCAI Workshop 2014. ed. / Torben Schneider; Marco Reisert; Lauren O’Donnell; Yogesh Rathi; Gemma Nedjati-Gilani. springer berlin, 2014. p. 55-64 (Mathematics and Visualization; Vol. 39).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Daianu, M, Jahanshad, N, Nir, TM, Leonardo, CD, Jack, CR, Weiner, MW, Bernstein, MA & Thompson, PM 2014, Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer’s disease. in T Schneider, M Reisert, L O’Donnell, Y Rathi & G Nedjati-Gilani (eds), Computational Diffusion MRI - MICCAI Workshop 2014. Mathematics and Visualization, vol. 39, springer berlin, pp. 55-64, 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, Boston, United States, 9/18/14. https://doi.org/10.1007/978-3-319-11182-7_6

Daianu M, Jahanshad N, Nir TM, Leonardo CD, Jack CR, Weiner MW et al. Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer’s disease. In Schneider T, Reisert M, O’Donnell L, Rathi Y, Nedjati-Gilani G, editors, Computational Diffusion MRI - MICCAI Workshop 2014. springer berlin. 2014. p. 55-64. (Mathematics and Visualization). doi: 10.1007/978-3-319-11182-7_6

Daianu, Madelaine ; Jahanshad, Neda ; Nir, Talia M. et al. / Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer’s disease. Computational Diffusion MRI - MICCAI Workshop 2014. editor / Torben Schneider ; Marco Reisert ; Lauren O’Donnell ; Yogesh Rathi ; Gemma Nedjati-Gilani. springer berlin, 2014. pp. 55-64 (Mathematics and Visualization).

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Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer’s disease

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