EEG synchronization likelihood in mild cognitive impairment and Alzheimer's disease during a working memory task

Pijnenburg, Yolande A.L.; Made, Y. van der; Walsum, A.M. van Cappellen van; Knol, Dirk L.; Scheltens, Philip; Stam, Cornelis J.

doi:10.1016/j.clinph.2003.12.029

Cited by 232 publications

(221 citation statements)

References 39 publications

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“…These findings are in agreement with many studies which employed cognitive tasks in the MCI population (Pijnenburg et al 2004;Jiang 2005;Jiang and Zheng 2006;Bajo et al 2010). In the alpha band, however, MCIs were desynchronized during the task.…”

Section: Control Group Vs MCI Groupsupporting

confidence: 92%

“…However, other resting state studies have failed to find functional connectivity differences between MCI and healthy controls (Stam et al 2003;Jiang 2005;Zheng et al 2007;Tao and Tian 2005). By contrast, most task studies found an increased synchronization in MCIs when compared to healthy people during a cognitive task (i.e., memory task) (Pijnenburg et al 2004;Jiang 2005;Zheng et al 2007;Bajo et al 2010). For instance, Jiang and Zheng (2006) reported a higher inter-and intrahemispheric EEG coherence in MCI in a working memory task.…”

Section: Introductionmentioning

confidence: 87%

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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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Section: Control Group Vs MCI Groupsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 87%

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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“…Section 2.2) as a measure for EEG synchrony. A similar effect was observed in (Pijnenburg et al, 2004) by means of the synchronization likelihood measure, which is state-space based measure similar to the indices S k , H k , and N k . In particular, in that study, one observed an increase of EEG synchrony during working memory task, but there was no significant effect in rest condition.…”

Section: Discussionsupporting

confidence: 70%

“…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

NeuroImage

371

384

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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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“…For instance, Pijnenburg et al (2004) have studied the EEG synchronization likelihood in AD during a working memory task and have found a decrease of beta band synchronization both in a resting condition and during a working memory task. Nonlinear forecasting and entropy maps have been used to characterize drug effects on brain dynamics in AD (Pezard et al, 1998) and mutual information analysis to assess information transmission between different cortical areas in AD (Jeong et al, 2001b).…”

Section: Introductionmentioning

confidence: 99%

Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy

Abásolo

Hornero

Espino

et al. 2005

Clinical Neurophysiology

220

159

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EEG synchronization likelihood in mild cognitive impairment and Alzheimer's disease during a working memory task

Cited by 232 publications

References 39 publications

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

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

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

Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy

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