Robust assessment of EEG connectivity patterns in mild cognitive impairment and Alzheimer's disease

The prevalence of dementia, including Alzheimer's disease (AD), is on the rise globally with screening and intervention of particular importance and benefit to those with limited access to healthcare. Electroencephalogram (EEG) is an inexpensive, scalable, and portable brain imaging technology that could deliver AD screening to those without local tertiary healthcare infrastructure. We study EEG recordings of sporadic mild cognitive impairment (MCI) and prodromal familial, early-onset, AD subjects for the same working memory tasks using high- and low-density EEG, respectively. A challenge in detecting electrophysiological changes with EEG is that noise and volume conduction effects are common and disruptive to functional connectivity analysis. It is known that the imaginary part of coherency (iCOH) can mitigate against volume conduction when generating functional connectivity networks. We aim to expose topological differences in these connectivity networks with a global network measure, eigenvector alignment (EA), that is shown to be robust to targeted alterations in the connectivity network; emulating the erasure of true instantaneous activity (zero or π-phase) by iCOH. The assessment of alignments establishes the relationship between EEG channels from the similarity of their connectivity patterns. Significant alignments, versus random null models, are found to be consistent across frequency ranges (delta, theta, alpha, and beta) for the working memory tasks - aided by the relative consistency of iCOH connectivities - in order to reveal network structure. For high-density EEG recordings, stark differences in the control and sporadic MCI results are observed with the control group demonstrating far more consistent alignments. These differences are also detected by comparing the significant correlation and iCOH connectivities, again in reference to random null models, where only EA suggests a notable difference in network topology when comparing subjects with sporadic MCI and prodromal familial AD. The consistency of alignments, across frequency ranges, provides a measure of confidence in EA's detection of topological structure, an important aspect that marks this approach as a promising direction for developing a reliable test for early onset AD