Enriched resting-state EEG prediction of cognitive decline in prodromal Alzheimer’s disease: a machine-learning approach

Clinical Neurophysiology 2026 Open access

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Authors: Claudio Babiloni, Susanna Lopez, Giuseppe Noce, Claudio Del Percio, Roberta Lizio, Dharmendra Jakhar, Mina De Bartolo, Raffaele Ferri, Filippo Carducci, Valentina Catania, Andrea Soricelli, Marco Salvatore
+25 more Dario Arnaldi, Francesco Famà, Andrea Brugnolo, Matteo Pardini, Franco Giubilei, Fabrizio Stocchi, Laura Vacca, Chiara Coletti, Fabrizia D’Antonio, Giuseppe Bruno, Bahar Güntekin, Lütfü Hanoğlu, Harun Yırıkoğulları, Görsev Yener, Giacomo Maria Russo, Moira Marizzoni, Giovanni B. Frisoni, Rossella Rotondo, Tiziana D’Alessandro, Nicole Dalia Cilia, Maria Francesca De Pandis, Adolfo Santoro, Simone Marziali, Claudio De Stefano, Francesco Fontanella
DOI: 10.1016/j.clinph.2026.2111860
Cited by: 2
References: 92

Abstract

OBJECTIVE: We evaluated the accuracy of standard machine learning (ML) algorithms in predicting 1-year cognitive decline in Alzheimer's disease patients with mild cognitive impairment (ADMCI) using resting-state electroencephalographic (rsEEG) biomarkers enriched with APOE genotype, sex, age, and educational attainment data. METHODS: The study analyzed datasets from 63 ADMCI patients obtained from an international archive. The ML algorithms included Simple Logistic Regression, Model Trees, Logistic Regression, K-nearest neighbor, and Support Vector Machine. Input features comprised lobar rsEEG source activities across delta (

Fields of Study & Subjects

EEG and Brain-Computer Interfaces Functional Brain Connectivity Studies Dementia and Cognitive Impairment Research

Publication details

Journal / source
Clinical Neurophysiology
Publisher
Elsevier BV
Volume
186
Pages
2111860–2111860
Publication date
25 March 2026
Language
EN