EEG microstate complexity for aiding early diagnosis of Alzheimer's disease

Abstract

The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful tool for imaging microstates, and EEG microstate analysis can potentially give insight into altered brain dynamics underpinning cognitive impairment in disorders such as Alzheimer's disease (AD). Since EEG is non-invasive and relatively inexpensive, EEG microstates have the potential to be useful clinical tools for aiding early diagnosis of AD. In this study, EEG was collected from two independent cohorts of probable AD and cognitively healthy control participants, and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. The microstate associated with the frontoparietal working-memory/attention network was altered in AD due to parietal inactivation. Using a novel measure of complexity, we found microstate transitioning was slower and less complex in AD. When combined with a spectral EEG measure, microstate complexity could classify AD with sensitivity and specificity > 80%, which was tested on an independent cohort, and could predict progression from MCI to AD in a small preliminary test cohort of 11 participants. EEG microstates therefore have potential to be a non-invasive functional biomarker of AD.

Figure 1

Microstate topographies for the four classes. (Top) Globally clustered maps for HOA cohort, for classes A-D from left to right. (Bottom) As above, but for the AD cohort. Black circles mark the electrode locations.

Figure 2

Cortical source generators underpinning alterations to microstate class D in AD. (A) Absolute value of the eLORETA solution to the instantaneous topography given by taking the difference between the global class D maps for HOA and AD. (B) t-statistic for voxel-wise comparisons of the subject-wise class D maps for HOA versus AD. Red indicates absolute value of current density is larger for HOA than AD, whilst blue is AD > HOA. (C) Voxels with t-values such that P  < .05. Red voxels indicate HOA > AD, and blue voxels are AD > HOA.

Figure 3

Microstate and complexity statistics are significantly altered in AD. (A) Mean duration of microstates. (B) Microstate LZC. (C) Ω-complexity. (D) Time series LZC. Descriptions of classical complexity measures C-D are given in Supplementary Material 3. Stars denote effect size of Mann–Whitney U test: *P < .05, **P < .01, ***P < .001. Points next to boxplots show values for each participant.

Figure 4

Separation of AD, HOA, and MCI using a SVM θRP + C classifier. The SVM predictor classifies points within the pale red region as AD and points within the gray region as HOA. (A) Training data set overlaid on 64 channel model. (B) RSM data set overlaid on 19 channel classifier model. (C) MCI data set overlaid on 64 channel model. All models were trained on the data set shown in A. Blue circles in A show the support vectors for training in the 64 channel model.