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.

Keywords: Alzheimer Disease; Relative Wavelet Entropy; amnestic Mild Cognitive Impairment; electroencephalography; graph analysis.

Figures

Figure 1

Visualization of the proposed analysis framework: there are five main analysis steps (A–E). Firstly, a randomization, bootstrap technique* is employed during step “A” to select (N = 75) multiple, artifact free data epochs. This bootstrap technique is implemented through a generator of random numbers which produces choices of data epochs. Each epoch lasts for 20 s. For each data segment and for each electrode the Orthogonal Discrete Wavelet Transform (ODWT) is applied through an iterative, recursive decomposition scheme (Step “B”). The ODWT framework results in the estimation of the wavelet coefficients for each frequency band and for each epoch. The computations are performed on 128 ms intervals, resulting in one (1) wavelet coefficient for the slow (delta, theta rhythms), two (2) coefficients for the alpha, four (4) for beta and eight (8) for gamma (Step “C”). These coefficients are then squared in order to express the rhythm's energy. So, the relative energy contribution of each frequency band is then computed by dividing the energy of each rhythm by the total EEG energy during Step “D.” The Relative Wavelet Entropy (RWE) is then computed for each electrode pair. The RWE provides a directed metric of the co-operative degree among two electrodes. Then, synchronization matrices based on the RWE values are formed. These matrices are then thresholded and directed, non-weighted networks are formed. These networks are employed toward the estimation of both global (small-world, characteristic path length, mean cluster coefficient) and local (relative betweenness centrality) characteristics.

Figure 2

(Top) Visualization of grand average brain graphs for each one of the three study groups (Healthy, MCI, MD) and for each edge density value (500, 600, 700, 800). (Bottom) visualization of the strongest (1%) network connections and their (edge) strengths, depicted through a colorbar.

Figure 3

Visualization of the statistically significant network parameters results (Small-World, Characteristic Path Length, Cluster Coefficient). Results refer to network differences among the three groups (Healthy, aMCI, MD) and are dependent on the density parameter (N = 500, 600, 700, 800). More specifically, statistical analysis demonstrated a significant group by density interaction. Both group and density main effects were further analyzed in order to highlight how global network characteristics differ among the three groups and how these parameters are affected by the density of the graph.

Figure 4

Visualization of the linear correlation among the network architecture (Small-World) with the generic neuropsychological tests. The correlation degree was estimated through the Pearson coefficient and was greater for the MoCA (r = 0.470) and lower for the MMSE (r = 0.367). Both correlations would be characterized of medium strength and may indicate that deficient generic cognition may be attributed to disturbances of the resting-state brain networks.

Figure 5

Visualization of the functional hubs identified for each one of the three groups (Healthy, aMCI and MD). The hub identification was based on the normalized relative betweenness centrality value. According to a previous study, a node is defined as a hub when its centrality value is greater or equal to 1.5 (Seo et al., 2013). This threshold is a strict one. Therefore, the analysis was performed on the lower density range (N = 500). A small density value is more likely to result in a greater number of functional hubs. The hubs (names and locations) are visualized in a sensor level for each group. The hub strength is also reported through its relative betweenness centrality (bi) value.