ArticleBrain sciences2018
A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronization in People with Alzheimer’s Disease and Healthy Controls
Article in Brain sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Alzheimer's disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study.Frontiers in aging neuroscience · 2024Article
- The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook.Brain sciences · 2023Review
- Spatial-temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram.Human brain mapping · 2022Article
- Open Eyes Increase Neural Oscillation and Enhance Effective Brain Connectivity of the Default Mode Network: Resting-State Electroencephalogram Research.Frontiers in neuroscience · 2022Article
- Nonlinear System Identification of Neural Systems from Neurophysiological Signals.Neuroscience · 2021Review
- A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals.BioMed research international · 2021Review
- Up-regulation of DMN Connectivity in Mild Cognitive Impairment Via Network-based Cognitive Training.Current Alzheimer research · 2018Article
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Authors and funding
16 authors.
Funding
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Abstract
backgroundThe incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD.
methods20 patients with probable AD, 20 matched healthy controls (HC) and 4 patients with probable fronto temporal dementia (FTD) were included. All had detailed neuropsychology along with structural, resting state fMRI and EEG. EEG data were analyzed using the Error Reduction Ratio-causality (ERR-causality) test that can capture both linear and nonlinear interactions between different EEG recording areas. The 95% confidence intervals of EEG levels of bi-centroparietal synchronization were estimated for eyes open (EO) and eyes closed (EC) states.
resultsIn the EC state, AD patients and HC had very similar levels of bi-centro parietal synchronization; but in the EO resting state, patients with AD had significantly higher levels of synchronization (AD = 0.44; interquartile range (IQR) 0.41 vs. HC = 0.15; IQR 0.17,
conclusionIn this pilot work, resting state quantitative EEG shows significant differences between healthy controls and patients with AD. This approach has the potential to develop into a useful non-invasive and economical diagnostic aid in AD.
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