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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Network properties of healthy and Alzheimer brains

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Author(s):
Coninck, Jose C. P. [1] ; Ferrari, Fabiano A. S. [2] ; Reis, Adriane S. [3] ; Iarosz, Kelly C. [4] ; Caldas, Ibere L. [4] ; Batista, Antonio M. [4, 5] ; Viana, Ricardo L. [3]
Total Authors: 7
Affiliation:
[1] Technol Univ Parana, Dept Stat, BR-80230901 Curitiba, Parana - Brazil
[2] Fed Univ Valleys Jequitinhonha & Mucuri, Inst Engn Sci & Technol, BR-39447790 Janauba - Brazil
[3] Univ Fed Parana, Dept Phys, BR-80060000 Curitiba, Parana - Brazil
[4] Univ Sao Paulo, Inst Phys, BR-05508900 Sao Paulo, SP - Brazil
[5] Univ Estadual Ponta Grossa, Dept Math & Stat, BR-84030900 Ponta Grossa, Parana - Brazil
Total Affiliations: 5
Document type: Journal article
Source: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 547, JUN 1 2020.
Web of Science Citations: 0
Abstract

The application of graph theory in diffusion weighted resonance magnetic images have allowed the description of the brain as a complex network, often called structural network. For many years, the small-world properties of brain networks have been studied and reported. However, few studies have gone beyond of clustering and characteristic path length. In this work, we compare the structural connection network of a healthy brain and a brain affected by Alzheimer's disease with artificial small-world networks. Based on statistical analysis, we demonstrate how artificial networks can be constructed using Newman-Watts procedure. The network quantifiers of both structural matrices are identified inside a probabilistic valley. Despite of similarities between structural connection matrices and artificial small-world networks, increased assortativity can be found in the Alzheimer brain. Due to limited experimental data, we cannot define a direct link between Alzheimer's disease and assortativity. Nevertheless, we intend to call attention for an important network quantifier that has been neglected. Our results indicate that network quantifiers can be helpful to identify abnormalities in real structural connections, for instance Alzheimer's disease that disrupts the communication among neurons. One of our main results is to show that the network indicators of the Alzheimer brain are almost identical with the small-world network, except the assortativity. (C) 2020 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 18/03211-6 - Non linear dynamics
Grantee:Iberê Luiz Caldas
Support type: Research Projects - Thematic Grants
FAPESP's process: 15/07311-7 - Dynamic behaviour of neural networks
Grantee:Kelly Cristiane Iarosz
Support type: Scholarships in Brazil - Post-Doctorate