Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer’s Disease

Abstract

Since age is the most significant risk factor for the development of Alzheimer's disease (AD), it is important to understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on information derived from resting state electroencephalogram (EEG) recordings, aiming to detect brain network disruption. This paper proposes a novel brain functional connectivity imaging method, particularly targeting the contribution of nonlinear dynamics of functional connectivity, on distinguishing participants with AD from healthy controls (HC). We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity between any two EEG channels without fitting a full model. This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption in AD from a new perspective and to gain some insight into the dynamic behaviour of brain networks in two age groups (above and below 70) with normal cognitive function. Although linear and stationary connectivity dominates the classification contributions, quantitative results have demonstrated that nonlinear and dynamic connectivity can significantly improve the classification

qEEG Analysis in the Diagnosis of Alzheimer’s Disease; a Comparison of Functional Connectivity and Spectral Analysis

“…A first limitation arises from the duration of the epochs that were available in the dataset (12s per subject). Longer epochs would have provided more EEG samples for phase synchronization analysis as well as an opportunity to evaluate the dynamic changes of connectivity over time as it had been previously done in Zhao et al [30]. Another limitation involved the number of participants.…”

“…Volume conduction, which refers to the leakage of electrical potentials to the neighboring electrodes, can complicate the interpretation of connectivity metrics. Therefore, the use of bipolar derivations in computation of functional connectivity is highly recommended as it was demonstrated in a recent study in the field of AD detection [30].…”

“…Although it resulted in identifying higher connectivity distributed in the brains of AD group, it could not localize the effect. Indeed, the effect of Alzheimer's disease tends to be more prominent in some areas of the brain than others [30,31], hence justifying a motive to pursue a further analysis with the Homotopic Pair Connectivity. Similar to the band division performed to retrieve the PSD in a spectral analysis, in functional connectivity studies involving AD subjects, electrode pairs can be singled-out and evaluated separately, instead of combining them all together [36].…”

“…Homotopic Pair Connectivity was computed by focusing on certain pairs of bipolar derivations that were homotopic in the Left and Right brain hemispheres (mirror areas of the brain hemispheres). Based on previous classifications [30,31], four pairs that were, in part, shown most affected by Alzheimer's disease were selected. These pairs are demonstrated in Figure 2.…”

Brain functional and effective connectivity based on electroencephalography recordings: A review

“…Visualization is indispensable in brain connectivity analysis and highly promoting to interpret brain activity and intercommunications. Zhao et al (2020) suggest that the imaging and study of brain functional connectivity can effectively revolutionize our understanding of brain degradation or dysfunction in a user friendly and systematic way. Besides, some researchers believe that connectivity results are supposed to be presented using appropriate visualizations that would in reality be interpreted by neurophysiologists (Haufe, Nikulin, Müller, & Nolte, 2013).…”