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

Czigler, Balázs; Csikós, Dóra; Hidasi, Zoltán; Gaál, Zsófia Anna; Csibri, Éva; Kiss, Éva; Salacz, Pál; Molnár, Márk

doi:10.1016/j.ijpsycho.2007.11.002

Cited by 142 publications

(103 citation statements)

References 31 publications

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“…Our findings are in accordance with other studies showing spectral ''slowing'' in AD (Czigler et al 2008;Dauwels et al 2010aDauwels et al , b, 2011van der Hiele et al 2007). For instance, Jeong (2004) have observed a slowing of the resting EEG in AD patients, reflected by a higher power in the theta frequency band.…”

Section: Discussionsupporting

confidence: 94%

“…Nowadays, quantitative studies have shown that AD causes EEG signals to slow down. Czigler et al (2008) have found a significant decrease of the alpha band and increase of the theta power in the AD patients. Gianotti et al (2007) and Jeong (2004) have further proved that AD patients show an increase in low frequencies bands (delta and theta band) power with a simultaneous decrease in high frequencies (alpha and beta) power in AD patients along with the development of the disease.…”

Section: Introductionmentioning

confidence: 86%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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Power spectral density and coherence analysis of Alzheimer’s EEG

et al. 2014

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In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pairwise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

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Section: Discussionsupporting

confidence: 94%

Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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Power spectral density and coherence analysis of Alzheimer’s EEG

et al. 2014

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“…Many studies have shown that Alzheimer's disease (AD) causes EEG signals to slow down (see, e.g., [5,10,[18][19][20][21][22][23][24]28]): AD is associated with an increase of power in low frequencies (delta and theta band, 0.5-8Hz) and a decrease of power in higher frequencies (alpha and beta, 8-30Hz, and gamma, 30-100Hz). To quantify the changes in spectral power, one has applied Fourier transforms [5,10,[18][19][20][21][22]28] and sparsified time-frequency maps ("bump models") [23,24].…”

Section: Slowing Of Eegmentioning

confidence: 99%

On the Early Diagnosis of Alzheimer’s Disease from EEG Signals: A Mini-Review

Dauwels

Vialatte

Cichocki

2010

Advances in Cognitive Neurodynamics (II)

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“…Induced responses represent a stimulus-or task-related increase in frequency-specific power recorded electrophysiologically (Kilner et al, 2005). It had been shown that the modulation of frequencyspecific dynamics, reflecting functional integration within or between neuronal sources, can be used to characterise neuronal coupling and address questions about the underlying mechanisms in health and disease; for example, task-related changes (Kilner et al, 2002(Kilner et al, , 2004Leocani et al, 1997), learning-related changes (Gerloff and Andres, 2002) and functional deficits (Czigler et al, 2008;Patino et al, 2006;Raethjen et al, 2007). The machinery presented in this paper contributes to this endeavour by allowing one to make inferences and quantify changes in either linear or non-linear coupling, induced experientially or associated with pathophysiology.…”

Section: Introductionmentioning

confidence: 99%

Dynamic causal modelling of induced responses

2008

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This paper describes a dynamic causal model (DCM) for induced or spectral responses as measured with the electroencephalogram (EEG) or the magnetoencephalogram (MEG). We model the time-varying power, over a range of frequencies, as the response of a distributed system of coupled electromagnetic sources to a spectral perturbation. The model parameters encode the frequency response to exogenous input and coupling among sources and different frequencies. The Bayesian inversion of this model, given data enables inferences about the parameters of a particular model and allows us to compare different models, or hypotheses. One key aspect of the model is that it differentiates between linear and non-linear coupling; which correspond to within and betweenfrequency coupling respectively. To establish the face validity of our approach, we generate synthetic data and test the identifiability of various parameters to ensure they can be estimated accurately, under different levels of noise. We then apply our model to EEG data from a faceperception experiment, to ask whether there is evidence for non-linear coupling between early visual cortex and fusiform areas.

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

Cited by 142 publications

References 31 publications

Power spectral density and coherence analysis of Alzheimer’s EEG

Power spectral density and coherence analysis of Alzheimer’s EEG

On the Early Diagnosis of Alzheimer’s Disease from EEG Signals: A Mini-Review

Dynamic causal modelling of induced responses

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