The E.E.G. in presenile dementia.

Gordon, E.; Sim, Moira

doi:10.1136/jnnp.30.3.285

Cited by 107 publications

(23 citation statements)

References 13 publications

(13 reference statements)

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“…As reported in earlier studies by other authors most patients with Alzheimer's disease show different EEG abnormalities, mainly in the form of slow waves [24][25][26]. In our study we have registered pathological EEG changes such as reduced background activity and an increase in the amount of theta and delta waves in 82% of our patients.…”

Section: Discussionsupporting

confidence: 64%

The value of quantitative EEG in differential diagnosis of Alzheimer's disease and subcortical vascular dementia

Gaweł

Zalewska

Szmidt-Salkowska

et al. 2009

Journal of the Neurological Sciences

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Section: Discussionsupporting

confidence: 64%

The value of quantitative EEG in differential diagnosis of Alzheimer's disease and subcortical vascular dementia

Gaweł

Zalewska

Szmidt-Salkowska

et al. 2009

Journal of the Neurological Sciences

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“…Generally, AD patients show a characteristic increase in slow activity and a reduction in alpha and beta activities (Gordon and Sim, 1967;Letemendia and Pampiglione, 1958). However, the neuropathology initially confined to the hippocampus and entorhinal cortex in the early stage of AD progresses to the neocortex and other brain regions in the later stages of AD (Selkoe, 1994).…”

Section: Discussionmentioning

confidence: 99%

“…The EEG in AD patients shows slowing of the posterior dominant rhythm, an increase in diffuse slow activity (Brenner et al, 1988;Liddle, 1958), and a reduction in alpha (Gordon and Sim, 1967;Letemendia and Pampiglione, 1958) and beta activities (Letemendia and Pampiglione, 1958;Wiener and Schuster, 1956). It is thought that the earliest changes in AD are an increase in theta activity and a decrease in beta activity, which are followed by a decrease in alpha and an increase in delta activity (Coben et al, 1983;Hier et al, 1991;Pentitila et al 1985).…”

mentioning

confidence: 93%

Quantitative EEG Findings in Different Stages of Alzheimer’s Disease

Kwak¹

2006

Journal of Clinical Neurophysiology

113

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Although quantitative EEG (q-EEG) has been used in Alzheimer's disease (AD), q-EEG changes in AD are complex because of the progressive nature of this disease. The topographical spectral power and occipital peak frequency (OPF) were compared among elderly controls, patients with mild cognitive impairment (MCI), and patients with four stages of AD. In AD patients, except those with a Clinical Dementia Rating Scale (CDR) score of 0.5, OPF was lower than that of elderly controls. Compared with elderly controls, the left anterior alpha spectral power was reduced in CDR 0.5; both posterior theta spectral powers were increased and all alpha spectral powers were reduced in CDR 1; all alpha and beta spectral powers were reduced and theta spectral power was increased in CDR 2; and all alpha and beta spectral powers were reduced and all delta and theta spectral powers were increased in CDR 3. Patients with MCI exhibited a reduction in both centrotemporal, posterior delta and left anterior, centrotemporal theta fields. The Mini-Mental State Examination (MMSE) score was related to left OPF, right posterior delta and left anterior theta spectral power, in that order. This study suggests that q-EEG in MCI shows nonoverlapping features between controls and AD patients, and AD patients show dynamic changes as the disease progresses. Finally, the left OPF is the parameter most significantly correlated with MMSE score.

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“…Visual resting state EEG analyses in AD patients have revealed a slow dominant posterior rhythm and an increase in widespread delta and theta activity combined with a reduction in alpha and beta (Berger, 1937;Brenner, Reynolds, & Ulrich, 1988;Gordon & Sim, 1967;Letemendia & Pampiglione, 1958;Liddell, 1958;Rae-Grant et al, 1987;Soininen, Partanen, Helkala, & Riekkinen, 1982;Weiner & Schuster, 1956). These abnormal EEG patterns have shown correlations with the cognitive status as well (Brenner et al, 1988;Gordon & Sim, 1967;Johannsen, Jakobsen, Bruhn, & Gjedde, 1999;Kaszniak, Garron, Fox, Bergen, & Huckman, 1979;Liddell, 1958;Merskey et al, 1980;Mundy-Castle, Hurst, Beerstecher, & Prinsloo, 1954;Obrist, Busse, Eisdorfer, & Kleemeier, 1962;Rae-Grant et al, 1987;Roberts, McGeorge, & Caird, 1978;Weiner & Schuster, 1956). Computerized resting state EEG studies have confirmed these early studies; they used the spectral power in predefined frequency bands to quantify EEG rhythmicity, synchrony-measures such as coherence to quantify EEG connectivity, and measures from information theory to quantify EEG complexity (see Jeong, 2004 andDauwels, Vialatte, &Cichocki, 2010 for extensive reviews).…”

Section: Introductionmentioning

confidence: 99%

Neuroimaging markers of global cognition in early Alzheimer's disease: A magnetic resonance imaging–electroencephalography study

et al. 2018

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IntroductionMagnetic resonance imaging (MRI) and electroencephalography (EEG) are a promising means to an objectified assessment of cognitive impairment in Alzheimer's disease (AD). Individually, however, these modalities tend to lack precision in both AD diagnosis and AD staging. A joint MRI–EEG approach that combines structural with functional information has the potential to overcome these limitations.Materials and MethodsThis cross‐sectional study systematically investigated the link between MRI and EEG markers and the global cognitive status in early AD. We hypothesized that the joint modalities would identify cognitive deficits with higher accuracy than the individual modalities. In a cohort of 111 AD patients, we combined MRI measures of cortical thickness and regional brain volume with EEG measures of rhythmic activity, information processing and functional coupling in a generalized multiple regression model. Machine learning classification was used to evaluate the markers’ utility in accurately separating the subjects according to their cognitive score.ResultsWe found that joint measures of temporal volume, cortical thickness, and EEG slowing were well associated with the cognitive status and explained 38.2% of ifs variation. The inclusion of the covariates age, sex, and education considerably improved the model. The joint markers separated the subjects with an accuracy of 84.7%, which was considerably higher than by using individual modalities.ConclusionsThese results suggest that including joint MRI–EEG markers may be beneficial in the diagnostic workup, thus allowing for adequate treatment. Further studies in larger populations, with a longitudinal design and validated against functional‐metabolic imaging are warranted to confirm the results.

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The E.E.G. in presenile dementia.

Cited by 107 publications

References 13 publications

The value of quantitative EEG in differential diagnosis of Alzheimer's disease and subcortical vascular dementia

The value of quantitative EEG in differential diagnosis of Alzheimer's disease and subcortical vascular dementia

Quantitative EEG Findings in Different Stages of Alzheimer’s Disease

Neuroimaging markers of global cognition in early Alzheimer's disease: A magnetic resonance imaging–electroencephalography study

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