ArticleFrontiers in aging neuroscience2026
Multimodal neuroimaging discrimination of Alzheimer's disease, mild cognitive impairment, and late-life depression using electroencephalography and functional near-infrared spectroscopy: integrating electrophysiological and hemodynamic biomarkers.
Article in Frontiers in aging neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Feature fusion and WOA-GWO optimization for Alzheimer's disease detection with sparse EEG channels.Frontiers in computational neuroscience · 2026Article
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Abstract
Objective: To identify electrophysiological and hemodynamic characteristics of the cerebral cortex during the resting-state that could help differentiate Alzheimer's disease (AD), mild cognitive impairment (MCI), and late-life depression (LLD) and integrate these characteristics into a diagnostic model. Methods: We recorded oxygenated hemoglobin concentration (HbO) signals detected by functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex, partial parietal cortex, and temporal lobe cortex, as well as electrophysiological signals detected by electroencephalography (EEG). The recording time was 30 min. Then, we used machine learning modeling with the support vector machine (SVM) algorithm to evaluate the diagnostic performances of EEG-based, fNIRS-based, and EEG plus fNIRS-based models for distinguishing AD, MCI, and LLD. Results: We investigated the differential neural signatures of patients with AD ( Conclusion: These findings highlight distinct EEG spectral patterns in patients with LLD compared to those with AD or MCI, particularly in alpha and high-gamma oscillations. These differences could be potential biomarkers for differentiating these conditions. Combining EEG and fNIRS analyses may further elucidate the neurophysiological mechanisms underlying these disorders.
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