Next Article in Journal
Weyl Prior and Bayesian Statistics
Next Article in Special Issue
Limitations to Estimating Mutual Information in Large Neural Populations
Previous Article in Journal
Tsallis Entropy, Likelihood, and the Robust Seismic Inversion
Open AccessArticle

Early Detection of Alzheimer’s Disease: Detecting Asymmetries with a Return Random Walk Link Predictor

1
Polytechnic School, Catholic University of Murcia, 30107 Murcia, Spain
2
Department of Computer Science and AI, University of Alicante, 03690 Alicante, Spain
3
Department of Computer Science, University of York, York YO10 5GH, UK
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(4), 465; https://doi.org/10.3390/e22040465
Received: 2 March 2020 / Revised: 4 April 2020 / Accepted: 17 April 2020 / Published: 19 April 2020
(This article belongs to the Special Issue Information Theory in Computational Neuroscience)
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. View Full-Text
Keywords: Alzheimer’s disease; neural embedding; random walk; link prediction; brain asymmetries; fMRI networks; directed graphs Alzheimer’s disease; neural embedding; random walk; link prediction; brain asymmetries; fMRI networks; directed graphs
Show Figures

Figure 1

MDPI and ACS Style

Curado, M.; Escolano, F.; Lozano, M.A.; Hancock, E.R. Early Detection of Alzheimer’s Disease: Detecting Asymmetries with a Return Random Walk Link Predictor. Entropy 2020, 22, 465.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop