ArticleFrontiers in human neuroscience2021
Multimodal Prediction of Alzheimer's Disease Severity Level Based on Resting-State EEG and Structural MRI.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The EEG analysis and identification of Alzheimer's disease: a review.Frontiers in aging neuroscience · 2025Pooled it
- Electroencephalography for early Alzheimer's disease diagnosis: from advanced feature engineering to interpretable ai and clinical translation.Frontiers in psychiatry · 2026Review
- A Novel CNN-Based Framework for Alzheimer's Disease Detection Using EEG Spectrogram Representations.Journal of personalized medicine · 2025Article
- The role of quantitative EEG biomarkers in Alzheimer's disease and mild cognitive impairment: applications and insights.Frontiers in aging neuroscience · 2025Review
- Multimodal diagnosis of Alzheimer's disease based on resting-state electroencephalography and structural magnetic resonance imaging.Frontiers in physiology · 2025Article
- Adaptive spatiotemporal encoding network for cognitive assessment using resting state EEG.NPJ digital medicine · 2024Article
- Multimodal Identification of Molecular Factors Linked to Severe Diabetic Foot Ulcers Using Artificial Intelligence.International journal of molecular sciences · 2024Article
- Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia.Brain sciences · 2024Article
- Article
- EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs.Sensors (Basel, Switzerland) · 2023Article
- Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay.Scientific reports · 2023Article
- Quantifying time perception during virtual reality gameplay using a multimodal biosensor-instrumented headset: a feasibility study.Frontiers in neuroergonomics · 2023Article
- Antidepressant effect of bright light therapy on patients with Alzheimer's disease and their caregivers.Frontiers in pharmacology · 2023Article
- Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning-Based Alzheimer's Disease Diagnosis.Computational intelligence and neuroscience · 2023Article
Corrections and comments
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6 authors.
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Abstract
While several biomarkers have been developed for the detection of Alzheimer's disease (AD), not many are available for the prediction of disease severity, particularly for patients in the mild stages of AD. In this paper, we explore the multimodal prediction of Mini-Mental State Examination (MMSE) scores using resting-state electroencephalography (EEG) and structural magnetic resonance imaging (MRI) scans. Analyses were carried out on a dataset comprised of EEG and MRI data collected from 89 patients diagnosed with minimal-mild AD. Three feature selection algorithms were assessed alongside four machine learning algorithms. Results showed that while MRI features alone outperformed EEG features, when both modalities were combined, improved results were achieved. The top-selected EEG features conveyed information about amplitude modulation rate-of-change, whereas top-MRI features comprised information about cortical area and white matter volume. Overall, a root mean square error between predicted MMSE values and true MMSE scores of 1.682 was achieved with a multimodal system and a random forest regression model.
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