Curado, Manuel, Escolano, Francisco, Lozano, Miguel A. et al. (1 more author) (2020) Early Detection of Alzheimer’s Disease:Detecting Asymmetries with a Return Random Walk Link Predictor. Entropy. 465. ISSN 1099-4300
Abstract
Alzheimer’s disease has been extensively studied using undirected graphs to represent the correlations of BOLD signals in different anatomical regions through functional magnetic resonance imaging (fMRI). However, there has been relatively little analysis of this kind of data using directed graphs, which potentially offer the potential to capture asymmetries in the interactions between different anatomical brain regions. The detection of these asymmetries is relevant to detect the disease in an early stage. For this reason, in this paper, we analyze data extracted from fMRI images using the net4Lap algorithm to infer a directed graph from the available BOLD signals, and then seek to determine asymmetries between the left and right hemispheres of the brain using a directed version of the Return Random Walk (RRW). Experimental evaluation of this method reveals that it leads to the identification of anatomical brain regions known to be implicated in the early development of Alzheimer’s disease in clinical studies.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 by the authors |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 22 Apr 2020 10:20 |
Last Modified: | 16 Oct 2024 16:33 |
Published Version: | https://doi.org/10.3390/e22040465 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.3390/e22040465 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:159743 |