Early Detection of Alzheimer’s Disease: Detecting Asymmetries with a Return Random Walk Link Predictor
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Título: | Early Detection of Alzheimer’s Disease: Detecting Asymmetries with a Return Random Walk Link Predictor |
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Autor/es: | Curado, Manuel | Escolano, Francisco | Lozano, Miguel Angel | Hancock, Edwin R. |
Grupo/s de investigación o GITE: | Laboratorio de Investigación en Visión Móvil (MVRLab) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Palabras clave: | Alzheimer’s disease | Neural embedding | Random walk | Link prediction | Brain asymmetries | fMRI networks | Directed graphs |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | 19-abr-2020 |
Editor: | MDPI |
Cita bibliográfica: | Curado M, Escolano F, Lozano MA, Hancock ER. Early Detection of Alzheimer’s Disease: Detecting Asymmetries with a Return Random Walk Link Predictor. Entropy. 2020; 22(4):465. doi:10.3390/e22040465 |
Resumen: | 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. |
URI: | http://hdl.handle.net/10045/105847 |
ISSN: | 1099-4300 |
DOI: | 10.3390/e22040465 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/e22040465 |
Aparece en las colecciones: | INV - MVRLab - Artículos de Revistas |
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