Abstract
Alzheimer’s disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n = 77) and VaD (n = 77) and healthy elderly normal controls (NC) (n = 77). We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria.
Highlights
Alzheimer’s disease (AD) is a debilitating neuro-degenerative disease, and is one of the most common forms of dementia among the elderly population [1] with a significant socio-economic burden for societies in developed countries
Both AD and Vascular dementia (VaD) groups showed increased delta power when compared to normal controls (NC), whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC
In an effort to meet the needs of clinical EEG community, this study proposes a model for extracting EEG spectral features in groups of dementia patients, which might be used as biomarkers for other diseases involving encephalopathy
Summary
Alzheimer’s disease (AD) is a debilitating neuro-degenerative disease, and is one of the most common forms of dementia among the elderly population [1] with a significant socio-economic burden for societies in developed countries. Vascular dementia (VaD) may result either from ischemic or hemorrhagic cerebrovascular disease (CVD), or from cardiovascular or circulatory disturbances that injure brain regions relevant to memory, cognition, and behavior [4]. VaD is the second most common form of dementia after AD, affecting approximately 20% of the dementia cases worldwide [5]. While VaD can be assessed with the use of imaging techniques at early stages of the disease, the similarities between symptoms between the different conditions can lead to diagnostic uncertainty. Autopsy assessment studies in dementia report that VaD was present in 24–28% of AD cases [6, 7]. Relevant features that are not immediately visible, such as power modulations, connectivity changes, or sparse small amplitude phenomena, may be overlooked
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