ReviewEntropy (Basel, Switzerland)2020
Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer's Disease: A Review.
Review in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
77 citing papers in PubMed, 148 citations in OpenAlex.
- Impact of Recording Conditions and Feature Fusion on Dementia Classification Using Spectral, Entropy, and Complexity EEG Features.Entropy (Basel, Switzerland) · 2026Article
- Aberrant Brain Entropy in Alzheimer's Disease: Evidence From a Resting-State fMRI Study.Brain and behavior · 2026Article
- EEG Brain Rhythms During Resting-State Wakefulness and Sleep in Elderly Expert Meditators.Journal of sleep research · 2026Article
- Decoding the mechanisms of amyloid-β in synaptic toxicity.Journal of Alzheimer's disease : JAD · 2026Review
- Article
- EENet-RLA: An Explainable Prediction Learning Framework for Alzheimer's Disease Classification from EEG Signals.Brain topography · 2026Article
- Integrating attractor dynamics and connectivity features for EEG-based dementia classification.Scientific reports · 2026Article
- Binaural Beat Stimulation Enhances Cognitive Function in Alzheimer's Disease via Temporal Lobe Activation: An sLORETA Study.Biomedicines · 2026Article
- Interpretable Network-Level Biomarker Discovery for Alzheimer's Stage Assessment Using Resting-State fNIRS Complexity Graphs.Brain sciences · 2026Article
- Wearable EEG devices in the detection of mild cognitive impairment: a systematic review.NPJ digital medicine · 2026Article
- EFD in Comparison with EWT for Synthetic and EEG Signal Decomposition and Classification of Alzheimer's Disease and Mild Cognitive Impairment.Annals of biomedical engineering · 2026Article
- Divergence of cortical neurophysiology across different neurodegenerative disorders compared to healthy ageing.Progress in neurobiology · 2026Article
- Multi-dimensional EEG analysis reveals distinct neurophysiological patterns in Alzheimer's and frontotemporal dementia.Journal of neuroengineering and rehabilitation · 2026Article
- Dynamics of brain connectivity across the Alzheimer's disease spectrum through magnetoencephalography.Scientific reports · 2026Article
- EEG features in late-onset epilepsy: possible correlation with cognitive impairment.Brain communications · 2026Article
- Restoring oscillatory dynamics in Alzheimer's disease: A laminar whole-brain model of serotonergic psychedelic effects.Network neuroscience (Cambridge, Mass.) · 2026Article
- Interpretable self-supervised transformers for resting-state EEG analysis in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- The application of traditional machine learning and deep learning with EEG for mild cognitive impairment: a bibliometric analysis.Frontiers in aging neuroscience · 2026Article
- Disrupted Brain Hierarchical Organization in Alzheimer's Disease Progression.medRxiv : the preprint server for health sciences · 2025Article
- EEG biomarkers for Alzheimer's disease: A novel automated pipeline for detecting and monitoring disease progression.Journal of Alzheimer's disease : JAD · 2025Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Alzheimer's disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000-2019 was performed in the Web of Science and PubMed databases, resulting in 126 identified studies. Compared to healthy individuals, the signals from AD patients have less complexity and more predictable oscillations, which are found mainly in the left parietal, occipital, right frontal, and temporal regions. This complexity is considered a potential biomarker for accurately responding to the functional lesion in AD. The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD. We proposed further studies in the signal complexities of AD patients, including investigating the reliability of complexity algorithms and the spatial patterns of signal complexity. In conclusion, the current review helps to better understand the complexity of abnormalities in the AD brain and provide useful information for AD diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.