Slowing and Loss of Complexity in Alzheimer′s EEG: Two Sides of the Same Coin?

Abstract

Medical studies have shown that EEG of Alzheimer's disease (AD) patients is “slower” (i.e., contains more low-frequency power) and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1) EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI) and control subjects; (2) EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony), classification rates of 83% (MCI) and 98% (MiAD) are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone.

MEG spectral analysis in subtypes of mild cognitive impairment

“…Nonetheless, overall our findings seem to confirm the hypothesis of a spectral pattern more proximate to the typical AD profile, mainly in a-md-MCIs. Previous EEG (Babiloni et al 2004;Dauwels et al 2011;Huang et al 2000;Jeong 2004) and MEG studies (Berendse et al 2000;Fernandez et al 2002Fernandez et al , 2003Fernandez et al , 2006b) of AD found an increased power in the low-frequency bands (delta and theta), accompanied by a decreased power in the high-frequency range (alpha, beta, and gamma). Such pattern of spectral changes is a consistent finding that correlates with cognitive performance and functional status (Fernandez et al 2002;Prichep et al 1994;van Deursen et al 2008).…”

Complexity Measures for Quantifying Changes in Electroencephalogram in Alzheimer’s Disease

“…A more detailed study is necessary to evaluate the methods using a much larger and diverse EEG datasets. This includes using the methods to differentiate between normal, MCI, and AD subjects [57,63,73].…”

“…AD causes a reduction in neuronal activity of the brain [52] resulting in decreased capability of the brain to process information [53][54][55] and this may be reflected in the EEG signals [52]. EEG complexity can potentially be a good biomarker for AD diagnosis [38] as AD patients have a significant reduction in EEG complexity [38,40,41,52,56,57]. Several studies have investigated EEG complexity as a potential AD biomarker using whole EEG record with the objective of achieving a high performance.…”

Towards automated EEG-Based Alzheimer's disease diagnosis using relevance vector machines

“…The most distinctive of these effects include: i) noticeable shift occurring in the EEG power spectrum (e.g., slowing of the EEG) [8]- [10]; ii) reduced inter-hemispherical spectral coherence [11]- [13]; iii) decreased EEG "complexity," likely caused by the reduction in nonlinear connections between cortical regions or even neuronal death [7]; and more recently, iv) a change in neuromodulatory behaviour measured via EEG amplitude modulation analysis [5], [14]. Many of these changes have been shown to be related [15] and diagnostic accuracies in line with more advanced neuroimaging techniques (e.g., fMRI) have been achieved [16].…”