Quantitative EEG in early Alzheimer's disease patients - Power spectrum and complexity features

Institute for Psychology, Hungarian Academy of Sciences, Budapest, Hungary.
International Journal of Psychophysiology (Impact Factor: 2.88). 05/2008; 68(1):75-80. DOI: 10.1016/j.ijpsycho.2007.11.002
Source: PubMed


The goal of this study was to investigate the EEG signs of early stage Alzheimer's disease (AD) by conventional analyses and by methods quantifying linear and nonlinear EEG-complexity. The EEG was recorded in 12 mild AD patients and in an age-matched healthy control group (24 subjects) in both eyes open and eyes closed conditions. Frequency spectra, Omega-complexity and Synchronization likelihood were calculated on the data. In the patients a significant decrease of the relative alpha and increase of the theta power were found. Remarkably increased Omega-complexity and lower Synchronization likelihood were observed in AD in the 0.5-25 Hz frequency ranges. It is concluded that both spectral- and EEG-complexity changes can be found already in the early stage of AD in a wide frequency range. Application of conventional EEG analysis methods in combination with quantification of EEG-complexity may improve the chances of early diagnosis of AD.

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Available from: Zsófia Anna Gaál, Feb 03, 2014
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    • "Our finding of a strong negative correlation between MMSE score and diffusivity lends support to this conclusion. These data are in agreement with a number of EEG studies which have found signal changes in Alzheimer's disease patients [Czigler et al., 2008; Gallego-Jutgl a et al., 2014; Jeong, 2004], as well as a recent DTI study looking at abnormalities in short-range fibers in Alzheimer's disease [Gao et al., 2014] that found that the abnormalities contribute to lower cognitive efficiency and higher compensatory brain activation. Another aspect to point out in the attempt to understand the role of superficial white matter in Alzheimer's disease is that the superficial white matter contains a large numbers of interstitial neurons. "
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    ABSTRACT: White matter abnormalities have been shown in the large deep fibers of Alzheimer's disease patients. However, the late myelinating superficial white matter comprised of intracortical myelin and short-range association fibers has not received much attention. To investigate this area, we extracted a surface corresponding to the superficial white matter beneath the cortex and then applied a cortical pattern-matching approach which allowed us to register and subsequently sample diffusivity along thousands of points at the interface between the gray matter and white matter in 44 patients with Alzheimer's disease (Age: 71.02 ± 5.84, 16M/28F) and 47 healthy controls (Age 69.23 ± 4.45, 19M/28F). In patients we found an overall increase in the axial and radial diffusivity across most of the superficial white matter (P < 0.001) with increases in diffusivity of more than 20% in the bilateral parahippocampal regions and the temporal and frontal lobes. Furthermore, diffusivity correlated with the cognitive deficits measured by the Mini-Mental State Examination scores (P < 0.001). The superficial white matter has a unique microstructure and is critical for the integration of multimodal information during brain maturation and aging. Here we show that there are major abnormalities in patients and the deterioration of these fibers relates to clinical symptoms in Alzheimer's disease. Hum Brain Mapp, 2016.
    Full-text · Article · Jan 2016 · Human Brain Mapping
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    • "As the name suggests, spectral power based features measure the power present in the five conventional EEG frequency bands: 0.1–4 Hz (delta), 4–8 Hz (theta), 8–12 Hz (alpha), 12–30 Hz (beta) and, 30–100+ Hz (gamma) (Sörnmo and Laguna, 2005), with some studies further partitioning a band into low (e.g., alpha1: 8–10 Hz) and high (e.g., alpha2: 10–12 Hz) parts. Several studies have shown that changes in EEG power spectra due to AD are reflected as an increase in delta and theta band powers, together with a decrease in alpha and beta band powers, thus suggesting a “slowing” of the EEG signal (Coben et al., 1983, 1985; Penttilä et al., 1985; Soininen et al., 1989; Czigler et al., 2008; Moretti et al., 2009; Babiloni et al., 2010). More recently, other features have been proposed, such as the subband spectral peaks (the most prominent peak inside a frequency band) (Raicher et al., 2008) and the ratio of different bands (e.g., theta/gamma by Moretti et al., 2009, 2011). "
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    ABSTRACT: Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system "semi-automated." Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.
    Full-text · Article · Mar 2014 · Frontiers in Aging Neuroscience
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    • "Linear and nonlinear analyses of EEG-complexity were also performed in the present study since these methods may be expected to reveal so far unexplored features of time-series data and are related to the degree of EEG-synchronization. Opening the eyes causes the increase of Omega-complexity [12] [8]. Dimensional complexity was found to increase with aging [1]. "
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    ABSTRACT: Task-dependent changes of nonlinear-linear synchronization features and graph theoretical properties of the delta and theta frequencies were analyzed in the present EEG study that were related to episodic memory maintenance processes. Synchronization was found to increase with respect to both the delta and theta bands within the frontal and parietal areas and also between these regions. Results of graph theoretical analysis indicated a task-related shift towards small-world network topology in the theta band.
    Full-text · Article · Dec 2011 · International journal of psychophysiology: official journal of the International Organization of Psychophysiology
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