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Proceedings Paper

Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia
Author(s): Amirhessam Tahmassebi; Katja Pinker-Domenig; Georg Wengert; Marc Lobbes; Andreas Stadlbauer; Francisco J. Romero; Diego P. Morales; Encarnacion Castillo; Antonio Garcia; Guillermo Botella; Anke Meyer-Bäse
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Paper Abstract

Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links.

In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system’s eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

Paper Details

Date Published: 16 May 2017
PDF: 8 pages
Proc. SPIE 10216, Smart Biomedical and Physiological Sensor Technology XIV, 1021609 (16 May 2017); doi: 10.1117/12.2263555
Show Author Affiliations
Amirhessam Tahmassebi, Florida State Univ. (United States)
Katja Pinker-Domenig, Florida State Univ. (United States)
Medical Univ. of Vienna (Austria)
Memorial Sloan-Kettering Cancer Ctr. (United States)
Georg Wengert, Medical Univ. of Vienna (Austria)
Marc Lobbes, Maastricht Univ. (Netherlands)
Andreas Stadlbauer, Univ. of Erlangen-Nürnberg (Germany)
Francisco J. Romero, Univ. de Granada (Spain)
Diego P. Morales, Univ. de Granada (Spain)
Encarnacion Castillo, Univ. de Granada (Spain)
Antonio Garcia, Univ. de Granada (Spain)
Guillermo Botella, Univ. Complutense de Madrid (Spain)
Anke Meyer-Bäse, Florida State Univ. (United States)

Published in SPIE Proceedings Vol. 10216:
Smart Biomedical and Physiological Sensor Technology XIV
Brian M. Cullum; Douglas Kiehl; Eric S. McLamore, Editor(s)

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