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Related Experiment Video

Updated: Sep 13, 2025

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

Ziyang Li1, Hong Wang1, Lei Li1

  • 1Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China.

Biomimetics (Basel, Switzerland)
|July 25, 2025
PubMed
Summary

Early Alzheimer's disease (AD) detection in healthy individuals is challenging. Task-related electroencephalography (EEG) combined with interpretable deep learning identified early AD risk signatures by analyzing brain activity patterns.

Keywords:
Alzheimer’s disease riskbiomarkerearly screeninginterpretable deep learningtask-related EEG

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Early detection of Alzheimer's disease (AD) in cognitively healthy individuals is a significant preclinical challenge.
  • Electroencephalography (EEG) shows promise for detecting AD risk, but task-related EEG studies are less common than resting-state EEG.
  • Interpretable deep learning offers a pathway to identify subtle AD-related patterns in complex neurophysiological data.

Purpose of the Study:

  • To investigate the utility of task-related EEG in identifying early Alzheimer's disease risk signatures in cognitively healthy individuals.
  • To apply an interpretable deep learning framework, Interpretable Convolutional Neural Network (InterpretableCNN), for AD risk feature identification.
  • To enhance the transparency of EEG-based AD risk assessment through interpretable AI.

Main Methods:

  • EEG data were collected during three distinct cognitive task conditions from participants.
  • Samples were classified based on Alzheimer's disease risk factors, including APOE genotype and polygenic risk scores.
  • A 100-fold leave-p%-subjects-out cross-validation (LPSO-CV) was employed to assess model performance and generalizability.
  • The InterpretableCNN model was used to analyze EEG features and identify patterns associated with AD risk.

Main Results:

  • The InterpretableCNN model achieved an ROC AUC of 60.84% and a Kappa value of 0.22, indicating fair agreement in identifying AD risk.
  • Model interpretation consistently highlighted theta and alpha brainwave activity in the parietal and temporal regions as key indicators.
  • These identified brain regions and activity patterns align with known areas affected by Alzheimer's disease pathology.

Conclusions:

  • Task-related EEG, when analyzed with interpretable deep learning, can effectively reveal early Alzheimer's disease risk signatures in healthy individuals.
  • The InterpretableCNN framework provides transparency in identifying crucial EEG features, making it a valuable tool for preclinical AD screening.
  • This approach offers a promising non-invasive method for early detection and risk assessment of Alzheimer's disease.