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

Abstract

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.

Wireless electroencephalogram monitoring system for deciphering neurological disorders using brain connectivity patterns

“…Whereas a disordered network with both reduced, and increased connectivity are associated with dysconnectivity or dysconnection. Functional connectivity studies in AD have shown a reduction in connectivity strength in cortical regions especially between distant electrodes in inter and intra‐hemispheres (Afshari & Jalili, 2017; Babiloni et al, 2016; Das & Puthankattil, 2020; Gurja et al, 2021; Jeong et al, 2001; Mammone et al, 2017; Musaeus et al, 2019; Pijnenburg et al, 2004; Revilla‐Vallejo et al, 2021; Sankari et al, 2011; Smailovic et al, 2020; Yan et al, 2021). Reduction in information transfer in AD patients across distant electrodes in the right hemisphere and between hemispheres was observed even without frequency band based analysis (Jeong et al, 2001).…”

Entropy in Brain Networks

“…Due to the complexity of the problem, multidisciplinarity is clear in most of these works. This is the case of the study carried out by Revilla-Vallejo and colleagues [ 7 ]. They propose a new methodology based on analyzing the distribution of the weights of the brain network from EEG after source reconstruction to improve understanding of the neural mechanisms associated with the progression of Alzheimer disease (AD).…”