Characterizing Alzheimer’s Disease Severity via Resting-Awake EEG Amplitude Modulation Analysis

Abstract

Changes in electroencephalography (EEG) amplitude modulations have recently been linked with early-stage Alzheimer’s disease (AD). Existing tools available to perform such analysis (e.g., detrended fluctuation analysis), however, provide limited gains in discriminability power over traditional spectral based EEG analysis. In this paper, we explore the use of an innovative EEG amplitude modulation analysis technique based on spectro-temporal signal processing. More specifically, full-band EEG signals are first decomposed into the five well-known frequency bands and the envelopes are then extracted via a Hilbert transform. Each of the five envelopes are further decomposed into four so-called modulation bands, which were chosen to coincide with the delta, theta, alpha and beta frequency bands. Experiments on a resting-awake EEG dataset collected from 76 participants (27 healthy controls, 27 diagnosed with mild-AD, and 22 with moderate-AD) showed significant differences in amplitude modulations between the three groups. Most notably, i) delta modulation of the beta frequency band disappeared with an increase in disease severity (from mild to moderate AD), ii) delta modulation of the theta band appeared with an increase in severity, and iii) delta modulation of the beta frequency band showed to be a reliable discriminant feature between healthy controls and mild-AD patients. Taken together, it is hoped that the developed tool can be used to assist clinicians not only with early detection of Alzheimer’s disease, but also to monitor its progression.

Corticothalamic network dysfunction and behavioral deficits in a mouse model of Alzheimer's disease

“…During cognitive processing, including visual and auditory stimuli, theta synchronization/coupling helps in recruitment of various neural network and information co-ordination among distinct neuroanatomical structures (Kawasaki, et al, 2014). EEG studies in AD patients have also shown altered delta and theta power (Fraga, et al, 2013,Huang, et al, 2000,Jeong, 2004,van Deursen, et al, 2008), although they also exhibit a decrease in alpha and beta frequency band power (Fraga, et al, 2013,Jeong, 2004). In our APP mice, we did see a trend for decreased alpha frequency, but the difference between genotypes did not reach statistical significance.…”

Power spectral density and coherence analysis of Alzheimer’s EEG

“…It is considered as an integrated appearance of different brain functions, such as depth of anesthesia (Shalbaf et al 2014), Parkinson's disease ), brain death , even under manual acupuncture (Pei et al 2014;Yi et al 2013). Despite it has been around for decades, using EEG as cognitive biomarker to detect and assess AD in individuals is a relatively new effort (Baker et al 2008;Czigler et al 2008;Fraga et al 2013;Hidasi et al 2007;Jelles et al 2008). According to the rather widely held view, the development of AD is associated with the slowing of the EEG (Czigler et al 2008;Dauwels et al 2010a, b;Moretti et al 2009), reduction of complexity in EEG ) and perturbations in EEG synchrony (Dauwels et al 2010a, b;Wang et al 2014).…”

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

“…These features have been used as indicators of cortical connectivity [31]. As we are interested inter-hemispheric connectivity, coherence features (both magnitude and phase) were computed only for the 8 Lastly, amplitude modulation features have been recently proposed and shown to reliably detect AD, as well as monitor disease progression [5], [32], [33]. To compute the features, the EEG full-band signal is first decomposed into the five conventional sub-bands: delta, theta, alpha, beta, and gamma.…”