Disturbed fluctuations of resting state EEG synchronization in Alzheimer's disease

Stam, Cornelis J.; Montez, Teresa; Jones, Bethany F.; Rombouts, Serge A.R.B.; Made, Y. van der; Pijnenburg, Yolande A.L.; Scheltens, Philip

doi:10.1016/j.clinph.2004.09.022

Cited by 219 publications

(172 citation statements)

References 42 publications

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“…Section 2.8), is believed to be more sensitive than coherence to detect changes in AD patients (Stam et al, 2003). Using state space based synchrony methods, significant loss of EEG synchrony was found in MCI (Stam et al, 2003) and AD patients (Jeong, 2004;Stam et al, 2003Stam et al, , 2005Babiloni et al, 2006;Pijnenburg et al, 2004;Yagyu et al, 1997;Wan et al, 2008), which evolved consistently from MCI to AD stage (Stam et al, 2003). We report here a low (uncorrected) p-value for the indices N k (p = 0.029; uncorrected) and S k (p = 0.019; uncorrected), and the S-estimator (p = 0.042; uncorrected); however, after post-correction, those effects are not statistically significant.…”

Section: Discussionmentioning

confidence: 99%

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Dauwels

Vialatte

Musha³

et al. 2010

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It is well known that EEG signals of Alzheimer's disease (AD) patients are generally less synchronous than in age-matched control subjects. However, this effect is not always easily detectable. This is especially the case for patients in the pre-symptomatic phase, commonly referred to as Mild Cognitive Impairment (MCI), during which neuronal degeneration is occurring prior to the clinical symptoms appearance. In this paper, various synchrony measures are studied in the context of AD diagnosis, including the correlation coefficient, mean-square and phase coherence, Granger causality, phase synchrony indices, information-theoretic divergence measures, state space based measures, and the recently proposed stochastic event synchrony measures. Experiments with EEG data show that many of those measures are strongly correlated (or anti-correlated) with the correlation coefficient, and hence, provide little complementary information about EEG synchrony. Measures that are only weakly correlated with the correlation coefficient include the phase synchrony indices, Granger causality measures, and stochastic-event synchrony measures. In addition, those three families of synchrony measures are mutually uncorrelated, and therefore, they each seem to capture a specific kind of interdependence. For the data set at hand, only two synchrony measures are able to convincingly distinguish AD patients from age-matched control patients, i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony. Those two measures are used as features to distinguish MCI patients from age-matched control subjects, yielding a leave-one-out classification rate of 83%. The classification performance may be further improved by adding complementary features from EEG; this approach may eventually lead to a reliable EEG-based diagnostic tool for MCI and AD.

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Section: Discussionmentioning

confidence: 99%

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

366

384

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“…It is generally accepted that there are alterations of EEG rhythms in the AD brain: (1) EEG slowing; that is, a shift of the power spectrum toward the lower frequency bands (delta and theta band) along with a decrease of oscillations in the higher frequency bands (alpha and beta); (2) decreased complexity, which is strongly related to the slowing of EEG; and (3) decreased synchrony, from which the functional network can be extracted (Dauwels et al 2010a(Dauwels et al , b, 2011(Dauwels et al , 2013Stam et al 2003Stam et al , 2005Stam et al , 2007. Notably, AD has been described as a disconnection syndrome, where functional interactions in the brain are strongly affected by anatomical abnormalities among different cortical areas (Jelles et al 1999) and altered cholinergic coupling interactions among cortical neurons (Jeong 2004).…”

Section: Discussionmentioning

confidence: 99%

“…Relative PSD and functional connectivity extracted from coherence were calculated to quantify the abnormalities of spectrum and synchrony in AD patients. The PSD of EEG has been widely applied to show the power of different brain areas (Akin and Kiymik 2000), and the coherence measures the synchrony between two cortical regions of the brain in frequency domain (Dauwels et al 2010a, b;Koenig et al 2005;Pereda et al 2005;Stam et al 2003Stam et al , 2005.…”

Section: Discussionmentioning

confidence: 99%

“…However, it is extensively reported that AD is considered as a 'disconnection syndrome', characterized by widespread degeneration of synapses and the death of neurons (Cook and Leuchter 1996;Delbeuck et al 2003;Tijms et al 2013). EEG coherence is a promising approach to evaluate functional cortical connections between different cortical areas of the brain (Dauwels et al 2010a, b;Koenig et al 2005;Pereda et al 2005;Stam et al 2003Stam et al , 2005. The higher the coherence is, the higher the linear synchrony is, which indicates a strong functional linkage (Dauwels et al 2010a, b;Pereda et al 2005), and even the information transmission, synergies and collaborative activity between different brain areas.…”

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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“…Some abnormalities in moderate and severe AD patients' EEG and MEG background activity have been observed [11][12][13]. AD patients show a decrease of coherence values at the α and β bands in EEG [14,15], together with a slowing of spontaneous electrical brain activity.…”

Section: Introductionmentioning

confidence: 98%

Evaluation of spectral ratio measures from spontaneous MEG recordings in patients with Alzheimer's disease

Poza

Hornero

Abásolo

et al. 2008

Computer Methods and Programs in Biomedicine

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Disturbed fluctuations of resting state EEG synchronization in Alzheimer's disease

Cited by 219 publications

References 42 publications

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Power spectral density and coherence analysis of Alzheimer’s EEG

Evaluation of spectral ratio measures from spontaneous MEG recordings in patients with Alzheimer's disease

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