Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology

Uhlhaas, Peter J.; Singer, Wolf

doi:10.1016/j.neuron.2006.09.020

Cited by 1,814 publications

(1,257 citation statements)

References 144 publications

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“…In particular, a common hypothesis, proposed about three decades ago, is that basal forebrain neurons may be severely affected in AD and result in a cerebral cholinergic deficit, underlying memory loss and other cognitive symptoms (Jeong et al, 2001). In other words, our report supports the view that the cognitive disturbances associated with AD may not solely be due to the loss of neurons, but also due to impairments in the temporal coordination of distributed neuronal activity (Uhlhaas & Singer, 2006).…”

Section: Resultssupporting

confidence: 81%

“…In addition, the EEG signals of AD patients are generally less coherent than in age-matched control patients; see (Jeong, 2004;Uhlhaas & Singer, 2006) for an in-depth review. It is noteworthy, however, that those two effects are not always easily detectable: there tends to be large variability among AD patients.…”

Section: Introductionmentioning

confidence: 99%

“…A considerable amount of research has recently been devoted to detecting fluctuations in EEG synchrony; more specifically, a large variety of measures has been proposed to quantify EEG synchrony, stemming from a wide spectrum of disciplines such as physics, information theory, statistics, and signal processing; we refer to (Pereda et al, 2005;Uhlhaas & Singer, 2006;Pereda et al, 2005;Breakspear, 2004;Kamiński et al, 2005;Stam, 2005) for recent reviews on EEG synchrony measures.…”

Section: Introductionmentioning

confidence: 99%

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

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

361

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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: Resultssupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

361

380

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“…Although the role of altered gamma activity in ASD is still under debate, it is suggested to relate to cognitive dysfunctions and may therefore offer an important entry to study neurophysiological correlates of autistic behavior [9]. It should be noted that altered gamma activity changes are not specific to ASD and have been related to alterations in common defects in GABAergic pathways observed across different developmental disorders such as schizophrenia and epilepsy [10].…”

Section: Discussionmentioning

confidence: 99%

Increased power of resting-state gamma oscillations in autism spectrum disorder detected by routine electroencephalography

Diessen

Senders

Jansen

et al. 2014

Eur Arch Psychiatry Clin Neurosci

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Experimental studies suggest that increased resting-state power of gamma oscillations is associated with autism spectrum disorder (ASD). To extend the clinical applicability of this finding, we retrospectively investigated routine electroencephalography (EEG) recordings of 19 patients with ASD and 19 age-and gender-matched controls. Relative resting-state condition gamma spectral power was variable, but on average significantly increased in children with ASD. This effect remained when excluding electrodes associated with myogenic gamma activity. These findings further indicate that increased resting-state gamma activity characterizes a subset of ASD and may also be detected by routine EEG as a clinically accessible and well-tolerated investigation.

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“…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. It has been extensively reported that EEG coherence of AD patients is characterized by a pattern of statistically significant decrease among cortical regions in the alpha frequency band (Adler et al 2003;Hogan et al 2003;Jeong 2004;Jiang 2005;Koenig et al 2005;Locatelli et al 1998;Sankari et al 2011;Uhlhaas and Singer, 2006). Knott et al (2000) have further demonstrated that the significantly decreased EEG coherence located in temporo-parietal brain areas can be taken as a discriminant variable between AD patients and the normal.…”

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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Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology

Cited by 1,814 publications

References 144 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

Increased power of resting-state gamma oscillations in autism spectrum disorder detected by routine electroencephalography

Power spectral density and coherence analysis of Alzheimer’s EEG

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