Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Poza, Jesús; Hornero, Roberto; Abásolo, Daniel; Fernández, Alberto; Escudero, Javier

doi:10.1109/iembs.2007.4353766

Cited by 20 publications

(16 citation statements)

References 23 publications

(46 reference statements)

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“…Previous work in Alzheimer’s disease models and patients has demonstrated reductions in relative spectral power in high beta-gamma bands ( Poza et al , 2007 ), despite methodological differences between electrophysiological studies ( Dauwels et al , 2010 ). For example, in a network analysis using EEG, Petersen et al (2001) found small-worldness of networks in the gamma band correlated with cognitive performance in prodromal Alzheimer’s disease.…”

Section: Discussionmentioning

confidence: 99%

Neurophysiological signatures of Alzheimer’s disease and frontotemporal lobar degeneration: pathology versus phenotype

Sami

Williams

Hughes

et al. 2018

Brain

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Section: Discussionmentioning

confidence: 99%

Neurophysiological signatures of Alzheimer’s disease and frontotemporal lobar degeneration: pathology versus phenotype

Sami

Williams

Hughes

et al. 2018

Brain

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“…This work has been developed on the basis of the results obtained in a pilot study where we tested several entropies using the averaged relative power over all sensors as a probability distribution. 30 Our findings suggested both a slowing of MEG rhythms and a loss of irregularity in AD. Further work should be developed to obtain a temporal evolution of the quantifiers, to assess the influence of the entropic index and to perform a spatial analysis.…”

Section: Introductionmentioning

confidence: 89%

Regional Analysis of Spontaneous MEG Rhythms in Patients with Alzheimer’s Disease Using Spectral Entropies

et al. 2007

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Alzheimer's disease (AD) is the most common form of dementia. Ageing is the greatest known risk factor for this disorder. Therefore, the prevalence of AD is expected to increase in western countries due to the rise in life expectancy. Nowadays, a low diagnosis accuracy is reached, but an early and accurate identification of AD should be attempted. In this sense, only a few studies have focused on the magnetoencephalographic (MEG) AD patterns. This work represents a new effort to explore the ability of three entropies from information theory to discriminate between spontaneous MEG rhythms from 20 AD patients and 21 controls. The Shannon (SSE), Tsallis (TSE), and Rényi (RSE) spectral entropies were calculated from the time-frequency distribution of the power spectral density (PSD). The entropies provided statistically significant lower values for AD patients than for controls in all brain regions (p < 0.0005). This fact suggests a significant loss of irregularity in AD patients' MEG activity. Maximal accuracy of 87.8% was achieved by both the TSE and RSE (90.0%, sensitivity; 85.7%, specificity). The statistically significant results obtained by both the extensive (SSE and RSE) and non-extensive (TSE) spectral entropies suggest that AD could disturb long and short-range interactions causing an abnormal brain function.

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“…Similar measures have been previously constructed to characterize Alzheimer’s disease [58], [61] and depression [60] and, in the former, were correlated with clinical measures that characterize the severity of dementia. In epilepsy, an epileptogenicity index has been defined to quantify the degree of epileptogenicity using high frequency (rapid discharges) recorded with depth electrodes [62].…”

Section: Methodsmentioning

confidence: 99%

Altered Resting State Brain Dynamics in Temporal Lobe Epilepsy Can Be Observed in Spectral Power, Functional Connectivity and Graph Theory Metrics

et al. 2013

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Despite a wealth of EEG epilepsy data that accumulated for over half a century, our ability to understand brain dynamics associated with epilepsy remains limited. Using EEG data from 15 controls and 9 left temporal lobe epilepsy (LTLE) patients, in this study we characterize how the dynamics of the healthy brain differ from the “dynamically balanced” state of the brain of epilepsy patients treated with anti-epileptic drugs in the context of resting state. We show that such differences can be observed in band power, synchronization and network measures, as well as deviations from the small world network (SWN) architecture of the healthy brain. The θ (4–7 Hz) and high α (10–13 Hz) bands showed the biggest deviations from healthy controls across various measures. In particular, patients demonstrated significantly higher power and synchronization than controls in the θ band, but lower synchronization and power in the high α band. Furthermore, differences between controls and patients in graph theory metrics revealed deviations from a SWN architecture. In the θ band epilepsy patients showed deviations toward an orderly network, while in the high α band they deviated toward a random network. These findings show that, despite the focal nature of LTLE, the epileptic brain differs in its global network characteristics from the healthy brain. To our knowledge, this is the only study to encompass power, connectivity and graph theory metrics to investigate the reorganization of resting state functional networks in LTLE patients.

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Analysis of Spontaneous MEG Activity in Patients with Alzheimer's Disease using Spectral Entropies

Cited by 20 publications

References 23 publications

Neurophysiological signatures of Alzheimer’s disease and frontotemporal lobar degeneration: pathology versus phenotype

Neurophysiological signatures of Alzheimer’s disease and frontotemporal lobar degeneration: pathology versus phenotype

Regional Analysis of Spontaneous MEG Rhythms in Patients with Alzheimer’s Disease Using Spectral Entropies

Altered Resting State Brain Dynamics in Temporal Lobe Epilepsy Can Be Observed in Spectral Power, Functional Connectivity and Graph Theory Metrics

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