Luke Van Popering,1 Amirhessam Tahmassebihttps://orcid.org/0000-0003-4677-6907,1 Uwe Meyer-Baese,1 Martin Dyrba,2 Jorge Munilla,3 Andres Ortiz,3 Anke Meyer-Baese1
1Florida State Univ. (United States) 2German Ctr. for Neurodegenerative Disease (Germany) 3Univ. de Málaga (Spain)
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Normal and aberrant cognitive functions are the result of the dynamic interplay between large-scale neural circuits. Describing the nature of these interactions has been a challenging task yet important for neurodegenerative disease evolution. The origin of Alzheimer’s disease lies in the hippocampus and subsequently diffuses to the temporal, parietal and prefrontal cortices. Determining the sources of dementia is crucial to the prediction of the disease evolution and choice of treatment. State-of-the-art method for determining dementia progression are network diffusion models derived from the heat equation without diffusion sources. We propose a different research avenue based on epidemic modeling to localize the disease sources. These models may better characterize the empirical spread of dementia through brain regions. We explore an estimation algorithm based on a susceptible-infected (SI) epidemic algorithm and a network diffusion model for comparison purposes emulating the disease evolution from sources (susceptible) to non-recovered (atrophy, infected) areas. The goal is to identify the probable disease diffusion sources, which we accomplish via a ranking heuristic based upon steady-state convergence times. Graph centrality measures are employed to provide a baseline for further comparison. Our results applied on structural brain networks in dementia suggest that epidemic models are able to accurately describe the different node roles in controlling trajectories of brain networks comparably to the existing diffusion approach.
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Luke Van Popering, Amirhessam Tahmassebi, Uwe Meyer-Baese, Martin Dyrba, Jorge Munilla, Andres Ortiz, Anke Meyer-Baese, "Identifying the diffusion source of dementia spreading in structural brain networks," Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116000A (15 February 2021); https://doi.org/10.1117/12.2582200