Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment

Koenig, Thomas; Prichep, Leslie S.; Dierks, Thomas; Hubl, Daniela; Wahlund, Lars Olof; John, E. Roy; Jelić, Vesna

doi:10.1016/j.neurobiolaging.2004.03.008

Cited by 475 publications

(426 citation statements)

References 30 publications

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“…Besides GFS, we analyzed five alternative phase synchrony measures: Hilbert and wavelet based phase synchrony, phase coherence, evolution map approach (EMA), and instantaneous period approach (IPA). The p-value of the latter is low (p = 0.020; uncorrected), in agreement with the results of (Koenig et al, 2005), but this effect is non-significant after statistical post-correction.…”

Section: Discussionsupporting

confidence: 86%

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: Discussionsupporting

confidence: 86%

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

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

371

384

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“…Finally, MCIs showed a higher synchronization in the gamma band between right anterior and central areas, which was related to a poorer performance in attention, memory, and executive function, while in the control group, this increase was localized in more posterior regions and was directly correlated with a greater hippocampal volume and higher scores in MMSE, Immediate and Delayed Recall, and Semantic Fluency. Most of these findings, and especially the decrease in synchronization in alpha and beta bands, are in agreement with those found in MCI and AD studies (Berendse et al 2000;Jelic et al 2000;Koenig et al 2005;Stam et al 2003;Moretti et al 2008;Gómez et al 2009), suggesting that MCI could be considered as the beginning of the "disconnection syndrome" (Delbeuck et al 2003). …”

Section: Control Group Vs MCI Groupsupporting

confidence: 88%

“…At a structural level, anatomical links in AD patients are disrupted, and this has been associated with a neurofibrillary pathology (Braak and Braak 1991). At a functional level, a decrease in the brain interactions has been shown in several studies (Stam et al 2006Locatelli et al 1998Berendse et al 2000;Stam and van Dijk 2002;see Jeong 2004 for a review; Koenig et al 2005;SanzArigita et al 2010). The concept of functional connectivity has emerged to evaluate functional relations between brain regions.…”

Section: Introductionmentioning

confidence: 99%

Synchronization during an internally directed cognitive state in healthy aging and mild cognitive impairment: a MEG study

López

Garcés

Cuesta

et al. 2014

AGE

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Mild cognitive impairment (MCI) is a stage between healthy aging and dementia. It is known that in this condition the connectivity patterns are altered in the resting state and during cognitive tasks, where an extra effort seems to be necessary to overcome cognitive decline. We aimed to determine the functional connectivity AGE (2014) pattern required to deal with an internally directed cognitive state (IDICS) in healthy aging and MCI. This task differs from the most commonly employed ones in neurophysiology, since inhibition from external stimuli is needed, allowing the study of this control mechanism. To this end, magnetoencephalographic (MEG) signals were acquired from 32 healthy individuals and 38 MCI patients, both in resting state and while performing a subtraction task of two levels of difficulty. Functional connectivity was assessed with phase locking value (PLV) in five frequency bands. Compared to controls, MCIs showed higher PLV values in delta, theta, and gamma bands and an opposite pattern in alpha, beta, and gamma bands in resting state. These changes were associated with poorer neuropsychological performance. During the task, this group exhibited a hypersynchronization in delta, theta, beta, and gamma bands, which was also related to a lower cognitive performance, suggesting an abnormal functioning in this group. Contrary to controls, MCIs presented a lack of synchronization in the alpha band which may denote an inhibition deficit. Additionally, the magnitude of connectivity changes rose with the task difficulty in controls but not in MCIs, in line with the compensation-related utilization of neural circuits hypothesis (CRUNCH) model.

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“…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%

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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Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment

Cited by 475 publications

References 30 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

Synchronization during an internally directed cognitive state in healthy aging and mild cognitive impairment: a MEG study

Power spectral density and coherence analysis of Alzheimer’s EEG

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