AI and Neurological Rare & Neuroimmune Diseases ── Basic research ── 2026-10-17

Two public EEG datasets compare cognitively normal people with patients with Alzheimer's disease, Parkinson's disease and frontotemporal dementia. SHAP identifies features that reproduce in both datasets. The representative one, the θ/α ratio, shows lower temporal stability and broader empirical distributions than CN in most patient groups. This may reflect disrupted oscillatory organization and offers a basis for future longitudinal studies. It is a retrospective secondary analysis, and only the abstract was checked. — Image abstract — the whole article on one page (click to enlarge)

1 Basic research papers. Lead: An Interpretable Framework for Identifying EEG Spectral Biomarkers of Pathological Brain Aging.

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Neurodegeneration and neuroimmunology research and AI

An Interpretable Framework for Identifying EEG Spectral Biomarkers of Pathological Brain Aging.
Diagnostics (Basel, Switzerland) · 2026-09-27 · Evidence: Retrospective observational (secondary analysis of public datasets)
Abstract
Background : Normal aging involves gradual declines in brain function, whereas neurodegenerative disorders show accelerated deterioration. Electroencephalography (EEG) captures alterations in brain oscillatory activity, making it promising for detecting neurodegenerative disorders. However, existing approaches often emphasize classification performance over spectral biomarker identification, limiting clinical translation. Methods : We propose an EEG-based framework integrating explainable machine learning, analyses of inter-subject heterogeneity and temporal stability, and probabilistic modeling to identify reproducible spectral biomarkers of pathological brain aging. Two independent public EEG datasets comprising cognitively normal (CN) individuals and patients with Alzheimer's disease (AD), Parkinson's disease (PD), and frontotemporal dementia (FTD) were analyzed. Spectral features from canonical frequency bands were analyzed using SHAP to identify reproducible biomarkers in two datasets. The identified biomarker was subsequently characterized through inter-subject heterogeneity, temporal stability, and empirical distributions to assess disease-related alterations in oscillatory activity. Results : Several spectral features showed consistent disease-related associations across classification tasks. The θ/α ratio emerged as a representative biomarker, showing reduced temporal stability and broader empirical distributions in most neurodegenerative groups than in CN. Conclusions : Reduced temporal stability of reproducible EEG spectral biomarkers may represent an observable signature of disrupted oscillatory brain organization during pathological aging. The proposed framework characterizes a dimension of EEG spectral biomarkers that static summaries discard, offering an interpretable basis on which future longitudinal studies may build diagnostic and monitoring tools.
Journal IF-equivalent: 4.0 (OpenAlex 2-year mean citedness, value as of 2026-10-16, retrieved 2026-10-17; not the official Clarivate IF)
Reference: Prieur-Coloma Y, Prado P, El-Deredy W, Weinstein A. An Interpretable Framework for Identifying EEG Spectral Biomarkers of Pathological Brain Aging. Diagnostics. 2026 Sep 27;16(19):3143. doi:10.3390/diagnostics16193143.
Checked: Abstract only

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