ArticleFrontiers in computational neuroscience2021
Machine Learning to Predict Brain Amyloid Pathology in Pre-dementia Alzheimer's Disease Using QEEG Features and Genetic Algorithm Heuristic.
Article in Frontiers in computational neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Diagnostic utility of electrophysiological markers for early and differential diagnosis of Alzheimer's, Frontotemporal, and Lewy Body dementias: A systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- Digital twins support cross-modal and cross-centric classification of mild cognitive impairment.Communications medicine · 2026Article
- A Multimodal Multi-Stage Deep Learning Model for the Diagnosis of Alzheimer's Disease Using EEG Measurements.Neurology international · 2025Article
- Digital twins and non-invasive recordings enable early diagnosis of Alzheimer's disease.Alzheimer's research & therapy · 2025Observational
- Event-related potential markers of subjective cognitive decline and mild cognitive impairment during a sustained visuo-attentive task.NeuroImage. Clinical · 2025Article
- The role of quantitative EEG biomarkers in Alzheimer's disease and mild cognitive impairment: applications and insights.Frontiers in aging neuroscience · 2025Review
- Quantitative electroencephalography (qEEG), apolipoprotein A-I (APOA-I), and apolipoprotein epsilon 4 (APOE ɛ4) alleles for the diagnosis of mild cognitive impairment and Alzheimer's disease.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- Neurophysiological markers of early cognitive decline in older adults: a mini-review of electroencephalography studies for precursors of dementia.Frontiers in aging neuroscience · 2024Review
- Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach.Frontiers in aging neuroscience · 2024Article
- Contribution of Scalp Regions to Machine Learning-Based Classification of Dementia Utilizing Resting-State qEEG Signals.Neuropsychiatric disease and treatment · 2024Article
- Review
- PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer's disease.Scientific reports · 2023Article
- CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG.Neuropsychiatric disease and treatment · 2023Article
- Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms.Journal of biomedical physics & engineering · 2022Article
- Characteristics of Attention-Deficit/Hyperactivity Disorder Subtypes in Children Classified Using Quantitative Electroencephalography.Neuropsychiatric disease and treatment · 2022Article
- Prediction model for potential depression using sex and age-reflected quantitative EEG biomarkers.Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of positron emission tomography (PET) as the initial or sole biomarker of β-amyloid (Aβ) brain pathology may inhibit Alzheimer's disease (AD) drug development and clinical use due to cost, access, and tolerability. We developed a qEEG-ML algorithm to predict Aβ pathology among subjective cognitive decline (SCD) and mild cognitive impairment (MCI) patients, and validated it using Aβ PET. We compared QEEG data between patients with MCI and those with SCD with and without PET-confirmed beta-amyloid plaque. We compared resting-state eyes-closed electroencephalograms (EEG) patterns between the amyloid positive and negative groups using relative power measures from 19 channels (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2), divided into eight frequency bands, delta (1-4 Hz), theta (4-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-12 Hz), beta 1 (12-15 Hz), beta 2 (15-20 Hz), beta 3 (20-30 Hz), and gamma (30-45 Hz) calculated by FFT and denoised by iSyncBrain
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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.