Quantitative EEG markers relate to Alzheimer’s disease severity in the Prospective Dementia Registry Austria (PRODEM)

Garn, Heinrich; Waser, Markus; Deistler, Manfred; Benke, Thomas; Dal‐Bianco, Peter; Ransmayr, Gerhard; Schmidt, Helena; Sanin, Guenter; Santer, Peter; Caravias, Georg; Seiler, Stephan; Grossegger, Dieter; Fruehwirt, Wolfgang; Schmidt, Reinhold

doi:10.1016/j.clinph.2014.07.005

Cited by 48 publications

(46 citation statements)

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“…Refuting our expectation, no significant baseline associations were found between EEG slowing and cognition (see Table 1), which is contrary to the findings in the majority of studies indicating a slowing of EEG in individuals with AD and MCI (e.g., Baker, Akrofi, Schiffer, & Boyle, 2008;van der Hiele et al, 2007;Moretti et al, 2004;Riekkinen, Buzsaki, Riekkinen, Soininen, & Partanen, 1991; but see Onishi et al, 2005 for contrary results). The missing association is further contrary to the correlative association between power and cognitive performance reported by previous studies (Alexander et al, 2006;Claus et al, 2000;Garn et al, 2015;van der Hiele et al, 2007). Our results are in line with the findings reported by Moretti et al (2004): The authors failed to find a correlative link between total power and cognitive performance, although they reported a significantly increased delta and significantly decreased alpha power in participants with AD in comparison with healthy controls.…”

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confidence: 89%

Global EEG coherence as a marker for cognition in older adults at risk for dementia

et al. 2019

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Quantitative electroencephalography (EEG) provides useful information about neurophysiological health of the aging brain. Current studies investigating EEG coherence and power for specific brain areas and frequency bands have yielded inconsistent results. This study assessed EEG coherence and power indices at rest measured over the whole skull and for a wide frequency range as global EEG markers for cognition in a sample at risk for dementia. Since global markers are more reliable and less error‐prone than region‐ and frequency‐specific indices they might help to overcome previous inconsistencies. Global EEG coherence (1–30 Hz) and an EEG slowing score were assessed. The EEG slowing score was calculated by low‐frequency power (1–8 Hz) divided by high‐frequency power (9–30 Hz). In addition, the prognostic value of the two EEG indices for cognition and cognitive decline was assessed in a 5‐year follow‐up pilot study. Baseline global coherence correlated positively with cognition at baseline, but not with cognitive decline or with cognition at the 5‐year follow‐up. The EEG slowing ratio showed no significant association, neither with cognition at baseline or follow‐up, nor with cognitive decline over a period of 5 years. The results indicate that the resting state global EEG coherence might be a useful and easy to assess electrophysiological correlate for neurocognitive health in older adults at risk for dementia. Because of the small statistical power for the follow‐up analyses, the prognostic value of global coherence could not be determined in the present study. Future studies should assess its prognostic value with larger sample sizes.

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Section: Of 13contrasting

confidence: 89%

Global EEG coherence as a marker for cognition in older adults at risk for dementia

et al. 2019

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“…The strong points of our paper are the following: 79 patients with probable AD is the largest study compared to other similar papers [9,[11][12][13][14][15] that dealt with complexity markers and AD. Furthermore, previous papers [9,[11][12][13] that studied qEEG markers and AD patients involved comparing AD patients with normal controls or other dementia-related diseases such as MCI while this study focused on qEEG markers and AD patients with varying disease severity.…”

Section: Discussionmentioning

confidence: 95%

Quantitative EEG Markers of Entropy and Auto Mutual Information in Relation to MMSE Scores of Probable Alzheimer’s Disease Patients

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Waser

et al. 2017

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Abstract:Analysis of nonlinear quantitative EEG (qEEG) markers describing complexity of signal in relation to severity of Alzheimer's disease (AD) was the focal point of this study. In this study, 79 patients diagnosed with probable AD were recruited from the multi-centric Prospective Dementia Database Austria (PRODEM). EEG recordings were done with the subjects seated in an upright position in a resting state with their eyes closed. Models of linear regressions explaining disease severity, expressed in Mini Mental State Examination (MMSE) scores, were analyzed by the nonlinear qEEG markers of auto mutual information (AMI), Shannon entropy (ShE), Tsallis entropy (TsE), multiscale entropy (MsE), or spectral entropy (SpE), with age, duration of illness, and years of education as co-predictors. Linear regression models with AMI were significant for all electrode sites and clusters, where R 2 is 0.46 at the electrode site C3, 0.43 at Cz, F3, and central region, and 0.42 at the left region. MsE also had significant models at C3 with R 2 > 0.40 at scales τ = 5 and τ = 6. ShE and TsE also have significant models at T7 and F7 with R 2 > 0.30. Reductions in complexity, calculated by AMI, SpE, and MsE, were observed as the MMSE score decreased.

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“…Electroencephalography (EEG) is cost-effective and available as the standard equipment in most primary, secondary, and tertiary neurological and psychiatric referral centers and in neurological practice. Significant correlations between various features of quantitative EEG (QEEG) and the severity of AD have already been demonstrated (Garn et al 2014, 2015). A slowing of the frequency spectrum to variable extent is a characteristic feature of degenerative dementias, such as AD or DLB (Li et al 2016; Jeong 2004; Caso et al 2012; Lindau et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Differential diagnosis between patients with probable Alzheimer’s disease, Parkinson’s disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features

et al. 2017

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The objective of this work was to develop and evaluate a classifier for differentiating probable Alzheimer’s disease (AD) from Parkinson’s disease dementia (PDD) or dementia with Lewy bodies (DLB) and from frontotemporal dementia, behavioral variant (bvFTD) based on quantitative electroencephalography (QEEG). We compared 25 QEEG features in 61 dementia patients (20 patients with probable AD, 20 patients with PDD or probable DLB (DLBPD), and 21 patients with bvFTD). Support vector machine classifiers were trained to distinguish among the three groups. Out of the 25 features, 23 turned out to be significantly different between AD and DLBPD, 17 for AD versus bvFTD, and 12 for bvFTD versus DLBPD. Using leave-one-out cross validation, the classification achieved an accuracy, sensitivity, and specificity of 100% using only the QEEG features Granger causality and the ratio of theta and beta1 band powers. These results indicate that classifiers trained with selected QEEG features can provide a valuable input in distinguishing among AD, DLB or PDD, and bvFTD patients. In this study with 61 patients, no misclassifications occurred. Therefore, further studies should investigate the potential of this method to be applied not only on group level but also in diagnostic support for individual subjects.

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Quantitative EEG markers relate to Alzheimer’s disease severity in the Prospective Dementia Registry Austria (PRODEM)

Cited by 48 publications

References 43 publications

Global EEG coherence as a marker for cognition in older adults at risk for dementia

Global EEG coherence as a marker for cognition in older adults at risk for dementia

Quantitative EEG Markers of Entropy and Auto Mutual Information in Relation to MMSE Scores of Probable Alzheimer’s Disease Patients

Differential diagnosis between patients with probable Alzheimer’s disease, Parkinson’s disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features

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