Integrative and interpretable framework to unveil the neurophysiological fingerprint of Alzheimer’s disease and mild cognitive impairment: A machine learning-SHAP approach

Topic: Amyloid, tau and blood-test diagnosis

Dementia and mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) are neurological pathologies associated with disruptions in brain electromagnetic activity, typically studied using magnetoencephalography (MEG) and electroencephalography (EEG). To quantify diverse brain properties, different families of parameters can be computed from MEG and EEG (i.e., spectral, non-linear, morphological, functional connectivity, or network structure and organisation). However, studying these characteristics separately overlooks the complex nature of brain activity. Integrative frameworks can be useful to unveil the intricate neurophysiological fingerprint, as well as to characterise pathological conditions comprehensively. To that purpose, data fusion methodologies are crucial, despite their interpretational challenges. In this study, Machine Learning (ML) models were trained to discriminate between groups of severity, whereas the SHapley Additive eXplanations (SHAP) algorithm was afterwards utilised to assess the relevance of the input characteristics into the output classification. Three databases were analysed: MEG (55 healthy controls, HC, 42 MCI patients, and 86 AD patients), EEG1 (51 HC, 52 MCI, and 100 AD), and EEG2 (45 HC, 69 MCI, and 82 AD). The best results for the three-class classification problem were obtained by Gradient Boosting for the MEG database: 3-class Cohen’s kappa coefficient of 0.5452 and accuracy of 72.63 %. Afterwards, using SHAP on Gradient Boosting, it has been shown that spectral features were identified as highly relevant across all databases. Furthermore, morphology measures presented high relevance for the MEG database, whereas EEG1 and EEG2 databases showed functional connectivity and multiplex organisation measures, respectively, as relevant subgroups of parameters. Finally, commonly relevant features across databases were selected using SHAP to generate the neurophysiological fingerprints of AD and MCI. This study highlights the relevance of different MEG and EEG parameters in characterising neurological pathologies. The proposed framework, based on MEG and EEG, can be used to generate interpretable, robust, and accurate neurophysiological fingerprints of AD and MCI.

Key findings

• Dementia and mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) are neurological pathologies associated with disruptions in brain electromagnetic activity, typically studied using magnetoencephalography (MEG) and electroencephalography (EEG).
• The best results for the three-class classification problem were obtained by Gradient Boosting for the MEG database: 3-class Cohen’s kappa coefficient of 0.5452 and accuracy of 72.63 %.
• Afterwards, using SHAP on Gradient Boosting, it has been shown that spectral features were identified as highly relevant across all databases.

Why it matters

This study trained machine learning models on brain electromagnetic data from MEG and EEG to tell apart healthy controls, people with mild cognitive impairment and people with Alzheimer's disease, with the best result reaching about 72.63% accuracy. It is a diagnostic classification study and does not test any treatment, cure or prevention.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.1016/j.bbe.2025.05.011
• Original source →
• Free full text: Journal of Applied Biomedicine →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{pablo2025-0d3ba250,
  author  = {Pablo, Víctor Gutiérrez-de and Herrero-Tudela, María and Sandonís-Fernández, Marina and Poza, Jesús and Maturana-Candelas, Aarón and Rodríguez-González, Víctor and Tola-Arribas, Miguel Ángel and Cano, Mónica and others},
  title   = {{Integrative and interpretable framework to unveil the neurophysiological fingerprint of Alzheimer’s disease and mild cognitive impairment: A machine learning-SHAP approach}},
  journal = {Journal of Applied Biomedicine},
  year    = {2025},
  doi     = {10.1016/j.bbe.2025.05.011},
  url     = {https://doi.org/10.1016/j.bbe.2025.05.011}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

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• Predicting Cognitive Impairment and Dementia: A Machine Learning Approach Journal of Alzheimer's disease : JAD · 2020-01-01 · usefulness score 49/100
• A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2020-07-31 · usefulness score 46/100
• A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort Alzheimer's & dementia (New York, N. Y.) · 2019-12-18 · usefulness score 58/100
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