Functional disorganization of small-world brain networks in mild Alzheimer's Disease and amnestic Mild Cognitive Impairment: an EEG study using Relative Wavelet Entropy (RWE)

Abstract

Previous neuroscientific findings have linked Alzheimer's Disease (AD) with less efficient information processing and brain network disorganization. However, pathological alterations of the brain networks during the preclinical phase of amnestic Mild Cognitive Impairment (aMCI) remain largely unknown. The present study aimed at comparing patterns of the detection of functional disorganization in MCI relative to Mild Dementia (MD). Participants consisted of 23 cognitively healthy adults, 17 aMCI and 24 mild AD patients who underwent electroencephalographic (EEG) data acquisition during a resting-state condition. Synchronization analysis through the Orthogonal Discrete Wavelet Transform (ODWT), and directional brain network analysis were applied on the EEG data. This computational model was performed for networks that have the same number of edges (N = 500, 600, 700, 800 edges) across all participants and groups (fixed density values). All groups exhibited a small-world (SW) brain architecture. However, we found a significant reduction in the SW brain architecture in both aMCI and MD patients relative to the group of Healthy controls. This functional disorganization was also correlated with the participant's generic cognitive status. The deterioration of the network's organization was caused mainly by deficient local information processing as quantified by the mean cluster coefficient value. Functional hubs were identified through the normalized betweenness centrality metric. Analysis of the local characteristics showed relative hub preservation even with statistically significant reduced strength. Compensatory phenomena were also evident through the formation of additional hubs on left frontal and parietal regions. Our results indicate a declined functional network organization even during the prodromal phase. Degeneration is evident even in the preclinical phase and coexists with transient network reorganization due to compensation.

Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms

“…Within this context, wavelet entropy (WE) has demonstrated very interesting results because it combines entropy and wavelet decomposition to increase its robustness to non-stationarities, noise and artifacts [7]. Given that physiological signals are often non-stationary, WE has proven to be widely successful in quantifying clinically-relevant events from electroencephalograms (EEG) [7,8], electrocardiograms (ECG) [9], intracranial pressure recordings [10] and evoked related potentials [11]. In the present work, a new application of WE to detect automatically the onset of the most common cardiac arrhythmia will be introduced.…”

A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity

“…It is still not possible to predict whether the MCI condition is going to degenerate into dementia or not, so there is still no chance of early intervention. Cross-sectional studies in the literature report that subjects with dementia exhibited a reduced efficiency of the electrical connectivity compared to MCI subjects, who, in turn, exhibited a weaker connectivity, compared to healthy subjects [2,7,13,19]. This paper addressed the importance of follow-up studies on MCI subjects with the aim of detecting any early sign of cortical electrical connectivity deterioration.…”

“…These findings endorse that cognitive decline seems to reflect the underlying cortical "disconnection" phenomenon caused by neuronal degeneration in MCI, worsened subjects indeed showed increased λ and decreased CC, GE and SW, thus decreased efficiency of the underlying complex network. MCI, when prodromal to dementia, is indeed considered a disconnection disorder because it induces a deterioration of network organization [2,7,13,19]. Frantzidis et al [19] provided evidence that MCI and Mild Dementia (MD) patients exhibited a loss of optimal brain network organization, in comparison with healthy controls, probably due to the reduction of local information processing caused by neuronal death.…”

“…Ahmadou and Adeli [18] introduced an enhanced probabilistic neural network classifier to discriminate between MCI and healthy subjects (98.4% accuracy). MCI condition, whenever it is the early symptom of a pathology that degenerates into dementia, appears associated with a progressive reduction in functional connectivity due to neuronal death [2,7,13,19,20]. An EEG-based model that quantifies brain electrical connectivity should be able to indirectly measure such changes.…”

Genetic variants in Alzheimer disease — molecular and brain network approaches

“…1c). These changes, which may be due to amyloid accumulation in the hub regions of the brain 123 , can be visualized with several imaging methods, including functional MRI (fMRI) 121,124 , magnetoencephalography 125 , EEG 124 and structural imaging 122,126 . Importantly, although the presence of short paths in networks has been associated with cognitive function in healthy cohorts 112,127 , specific cognitive deficits in AD have not been conclusively linked to this metric.…”