Determining disease evolution driver nodes in dementia networks Conference Paper


Authors: Tahmassebi, A.; Amani, A. M.; Pinker-Domenig, K.; Meyer-Baese, A.
Title: Determining disease evolution driver nodes in dementia networks
Conference Title: Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
Abstract: Imaging connectomics emerged as an important field in modern neuroimaging to represent the interaction of structural and functional brain areas. Static graph networks are the mathematical structure to capture these interactions modeled by Pearson correlations between the representative area signals. Dynamical functional resting state networks seen in most fMRI experiments can not be represented by the classic correlation graph network. The changes in brain connectivity observed in many neuro-degenerative diseases in longitudinal data series suggest that more sophisticated graph networks to capture the dynamical properties of the brain networks are required. Furthermore, certain brain areas seem to act as "disease epicenters" being responsible for the spread of neuro-degenerative diseases. To mathematically describe these aspects, we propose a novel framework based on pinning controllability applied to dynamic graphs and seek to determine the changes in the "driver nodes" during the course of the disease. In contrast to other current research in pinning controllability, we aim to identify the best driver nodes describing disease evolution with respect to connectivity changes and location of the best driver nodes in functional F-18-Fluorodeoxyglucose Positron Emission Tomography ((18)FDG-PET) and structural Magnetic Resonance Imaging (MRI) connectivity graphs in healthy controls (CN), and patients with mild cognitive impairment (MCI), and Alzheimer's disease (AD). We present the theoretical framework for determining the best driver nodes in connectivity graphs and their relation to disease evolution in dementia. We revolutionize the current graph analysis in brain networks and apply the concept of dynamic graph theory in connection with pinning controllability to reveal differences in the location of "disease epicenters" that play an important role in the temporal evolution of dementia. The described research will constitute a leap in biomedical research related to novel disease prediction trajectories and precision dementia therapies.
Keywords: dementia; graph theory; complex networks; synchronization; dynamic graph; pinning-controllability; pinning observability; competitive neural-networks; different time-scales; stability analysis; disease epicenters
Journal Title Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume: 10578
Conference Dates: 2018 Feb 11-13
Conference Location: Houston, TX
ISBN: 1605-7422
Publisher: SPIE  
Date Published: 2018-01-01
Start Page: 10578 29
Language: English
ACCESSION: WOS:000450869300069
PROVIDER: wos
DOI: 10.1117/12.2293865
Notes: Proceedings Paper -- SPIE Conference on Medical Imaging - Biomedical Applications in Molecular, Structural, and Functional Imaging -- FEB 11-13, 2018 -- Houston, TX -- SPIE, DECTRIS Ltd -- 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA -- 1057829 -- Source: Wos
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