Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach

• Published in: Biomimetics (Basel, Switzerland) Vol. 10; no. 7; p. 468
• Main Authors: Li, Ziyang, Wang, Hong, Li, Lei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16.07.2025; MDPI
• Subjects: Advertising executives; Alzheimer's disease; Alzheimer’s disease risk; Apolipoprotein E; Apolipoproteins; Asymptomatic; biomarker; Biomarkers; Brain research; Classification; Cognitive ability; Deep learning; Dementia; Diagnosis; Diseases; early screening; EEG; Electroencephalography; Genotypes; Health risk assessment; interpretable deep learning; Medical research; Medicine, Experimental; Middle age; Neural networks; Neurodegeneration; Neurodegenerative diseases; Neurophysiology; task-related EEG; interpretable deep learning; task-related EEG; early screening; biomarker; Alzheimer’s disease risk
• ISSN: 2313-7673; 2313-7673
• DOI: 10.3390/biomimetics10070468

The early detection of Alzheimer’s disease (AD) in cognitively healthy individuals remains a major preclinical challenge. EEG is a promising tool that has shown effectiveness in detecting AD risk. Task-related EEG has been rarely used in Alzheimer’s disease research, as most studies have focused on resting-state EEG. An interpretable deep learning framework—Interpretable Convolutional Neural Network (InterpretableCNN)—was utilized to identify AD-related EEG features. EEG data were recorded during three cognitive task conditions, and samples were labeled based on APOE genotype and polygenic risk scores. A 100-fold leave-p%-subjects-out cross-validation (LPSO-CV) was used to evaluate model performance and generalizability. The model achieved an ROC AUC of 60.84% across the tasks and subjects, with a Kappa value of 0.22, indicating fair agreement. Interpretation revealed a consistent focus on theta and alpha activity in the parietal and temporal regions—areas commonly associated with AD pathology. Task-related EEG combined with interpretable deep learning can reveal early AD risk signatures in healthy individuals. InterpretableCNN enhances transparency in feature identification, offering a valuable tool for preclinical screening.