ArticleFrontiers in neuroscience2016
Classification of Single Normal and Alzheimer's Disease Individuals from Cortical Sources of Resting State EEG Rhythms.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Functional and effective EEG connectivity patterns in Alzheimer's disease and mild cognitive impairment: a systematic review.Frontiers in aging neuroscience · 2025Pooled it
- Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment.Disease markers · 2018Pooled it
- Decoding the mechanisms of amyloid-β in synaptic toxicity.Journal of Alzheimer's disease : JAD · 2026Review
- Quantitative EEG signatures of power and functional connectivity alterations in Alzheimer's disease and frontotemporal dementia.Scientific reports · 2026Article
- Resting-state EEG biomarkers of general and domain-specific cognitive impairment and plasma P-tau217 in patients with Alzheimer's disease and mild cognitive impairment.Frontiers in neurology · 2026Article
- The correlation between physical exercise and cognitive function in older adults with cognitive impairment mediated by inhibitory control and possible cognitive processing mechanisms.Frontiers in public health · 2026Article
- Wavelet Entropy Analysis of EEG Signals During Wake and Sleep in Patients with Alzheimer's Disease: A Pilot Study.Neuropsychiatric disease and treatment · 2026Article
- Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Prognostic value of quantitative and visual electroencephalography in disorders of consciousness: a retrospective study.Frontiers in neuroscience · 2025Article
- Exploring the Relationship Between CAIDE Dementia Risk and EEG Signal Activity in a Healthy Population.Brain sciences · 2024Article
- Changes of brain functional network in Alzheimer's disease and frontotemporal dementia: a graph-theoretic analysis.BMC neuroscience · 2024Article
- Age-Related Aspects of Sex Differences in Event-Related Brain Oscillatory Responses: A Turkish Study.Brain sciences · 2024Article
- Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis.Sensors (Basel, Switzerland) · 2024Article
- Screening for Mild Cognitive Impairment with Speech Interaction Based on Virtual Reality and Wearable Devices.Brain sciences · 2023Article
- Effects of Robot-Assisted Activity Using a Communication Robot on Neurological Activity in Older Adults with and without Cognitive Decline.Journal of clinical medicine · 2023Article
- Resting-state electroencephalographic characteristics related to mild cognitive impairments.Frontiers in psychiatry · 2023Article
- The Aging Patterns of Brain Structure, Function, and Energy Metabolism.Advances in experimental medicine and biology · 2023Article
- Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Article
- The sleep EEG envelope is a novel, neuronal firing-based human biomarker.Scientific reports · 2022Article
- Frontotemporal EEG as potential biomarker for early MCI: a case-control study.BMC psychiatry · 2022Article
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23 authors.
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Abstract
Previous studies have shown abnormal power and functional connectivity of resting state electroencephalographic (EEG) rhythms in groups of Alzheimer's disease (AD) compared to healthy elderly (Nold) subjects. Here we tested the best classification rate of 120 AD patients and 100 matched Nold subjects using EEG markers based on cortical sources of power and functional connectivity of these rhythms. EEG data were recorded during resting state eyes-closed condition. Exact low-resolution brain electromagnetic tomography (eLORETA) estimated the power and functional connectivity of cortical sources in frontal, central, parietal, occipital, temporal, and limbic regions. Delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz) were the frequency bands of interest. The classification rates of interest were those with an area under the receiver operating characteristic curve (AUROC) higher than 0.7 as a threshold for a moderate classification rate (i.e., 70%). Results showed that the following EEG markers overcame this threshold: (i) central, parietal, occipital, temporal, and limbic delta/alpha 1 current density; (ii) central, parietal, occipital temporal, and limbic delta/alpha 2 current density; (iii) frontal theta/alpha 1 current density; (iv) occipital delta/alpha 1 inter-hemispherical connectivity; (v) occipital-temporal theta/alpha 1 right and left intra-hemispherical connectivity; and (vi) parietal-limbic alpha 1 right intra-hemispherical connectivity. Occipital delta/alpha 1 current density showed the best classification rate (sensitivity of 73.3%, specificity of 78%, accuracy of 75.5%, and AUROC of 82%). These results suggest that EEG source markers can classify Nold and AD individuals with a moderate classification rate higher than 80%.
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