ArticleNeuroImage. Clinical2019
Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review.Frontiers in aging neuroscience · 2023Pooled it
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- AI-driven magnetoencephalography biomarkers in dementia risk prediction: current evidence, challenges and future perspectives.Frontiers in dementia · 2026Review
- AI-powered model for accurate prediction of MCI-to-AD progression.Acta pharmaceutica Sinica. B · 2025Article
- Research progress in predicting the conversion from mild cognitive impairment to Alzheimer's disease via multimodal MRI and artificial intelligence.Frontiers in neurology · 2025Review
- High-power transient 12-30 Hz beta event features as early biomarkers of Alzheimer's disease conversion: An MEG study.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- Machine Learning and Graph Signal Processing Applied to Healthcare: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Neurophysiological consequences of synapse loss in progressive supranuclear palsy.Brain : a journal of neurology · 2023Article
- Article
- Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.Frontiers in neurorobotics · 2023Article
- The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.Biomedicines · 2022Review
- Oscillatory Activity of the Hippocampus in Prodromal Alzheimer's Disease: A Source-Space Magnetoencephalography Study.Journal of Alzheimer's disease : JAD · 2022Article
- Artificial Intelligence for Alzheimer's Disease: Promise or Challenge?Diagnostics (Basel, Switzerland) · 2021Review
- A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression With MEG Brain Networks.IEEE transactions on bio-medical engineering · 2021Article
- Electrophysiological Signature and the Prediction of Deep Brain Stimulation Withdrawal and Insertion Effects.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Many neuroimaging studies focus on a frequency-specific or a multi-frequency network analysis showing that functional brain networks are disrupted in patients with Alzheimer's disease (AD). Although those studies enriched our knowledge of the impact of AD in brain's functionality, our goal is to test the effectiveness of combining neuroimaging with network neuroscience to predict with high accuracy subjects with mild cognitive impairment (MCI) that will convert to AD. In this study, eyes-closed resting-state magnetoencephalography (MEG) recordings from 27 stable MCI (sMCI) and 27 progressive MCI (pMCI) from two scan sessions (baseline and follow-up after approximately 3 years) were projected via beamforming onto an atlas-based set of regions of interest (ROIs). Dynamic functional connectivity networks were constructed independently for the five classical frequency bands while a multivariate phase-based coupling metric was adopted. Thus, computing the distance between the fluctuation of functional strength of every pair of ROIs between the two conditions with dynamic time wrapping (DTW), a large set of features was extracted. A machine learning algorithm revealed 30 DTW-based features in the five frequency bands that can distinguish the sMCI from pMCI with absolute accuracy (100%). Further analysis of the selected links revealed that most of the connected ROIs were part of the default mode network (DMN), the cingulo-opercular (CO), the fronto-parietal and the sensorimotor network. Overall, our dynamic network multi-frequency analysis approach provides an effective framework of constructing a sensitive MEG-based connectome biomarker for the prediction of conversion from MCI to Alzheimer's disease.
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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.