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Article

Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer’s Disease

1
Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain
2
Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
3
IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, 47011 Valladolid, Spain
4
Department of Neurology, Río Hortega University Hospital, 47012 Valladolid, Spain
5
Department of Clinical Neurophysiology, Río Hortega University Hospital, 47012 Valladolid, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Geert Verdoolaege
Entropy 2021, 23(5), 500; https://doi.org/10.3390/e23050500
Received: 1 March 2021 / Revised: 10 April 2021 / Accepted: 19 April 2021 / Published: 22 April 2021
(This article belongs to the Special Issue Entropy in Brain Networks)
Alzheimer’s disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity. View Full-Text
Keywords: Alzheimer’s disease (AD); mild cognitive impairment (MCI); electroencephalogram (EEG); functional neural network; network weights distribution; Shannon entropy (SE) Alzheimer’s disease (AD); mild cognitive impairment (MCI); electroencephalogram (EEG); functional neural network; network weights distribution; Shannon entropy (SE)
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MDPI and ACS Style

Revilla-Vallejo, M.; Poza, J.; Gomez-Pilar, J.; Hornero, R.; Tola-Arribas, M.Á.; Cano, M.; Gómez, C. Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer’s Disease. Entropy 2021, 23, 500. https://doi.org/10.3390/e23050500

AMA Style

Revilla-Vallejo M, Poza J, Gomez-Pilar J, Hornero R, Tola-Arribas MÁ, Cano M, Gómez C. Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer’s Disease. Entropy. 2021; 23(5):500. https://doi.org/10.3390/e23050500

Chicago/Turabian Style

Revilla-Vallejo, Marcos, Jesús Poza, Javier Gomez-Pilar, Roberto Hornero, Miguel Á. Tola-Arribas, Mónica Cano, and Carlos Gómez. 2021. "Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer’s Disease" Entropy 23, no. 5: 500. https://doi.org/10.3390/e23050500

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