Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach
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...
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MDPI AG
2025-07-01
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| Reeks: | Biomimetics |
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| Online toegang: | https://www.mdpi.com/2313-7673/10/7/468 |
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| _version_ | 1839616407108583424 |
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| author | Ziyang Li Hong Wang Lei Li |
| author_facet | Ziyang Li Hong Wang Lei Li |
| author_sort | Ziyang Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b943fdfb0fba480e91ff3bed53bcdfb9 |
| institution | Matheson Library |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-b943fdfb0fba480e91ff3bed53bcdfb92025-07-25T13:16:15ZengMDPI AGBiomimetics2313-76732025-07-0110746810.3390/biomimetics10070468Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning ApproachZiyang Li0Hong Wang1Lei Li2Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, ChinaDepartment of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, ChinaDepartment of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, ChinaThe 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.https://www.mdpi.com/2313-7673/10/7/468task-related EEGAlzheimer’s disease riskinterpretable deep learningearly screeningbiomarker |
| spellingShingle | Ziyang Li Hong Wang Lei Li Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach Biomimetics task-related EEG Alzheimer’s disease risk interpretable deep learning early screening biomarker |
| title | Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach |
| title_full | Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach |
| title_fullStr | Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach |
| title_full_unstemmed | Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach |
| title_short | Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach |
| title_sort | task related eeg as a biomarker for preclinical alzheimer s disease an explainable deep learning approach |
| topic | task-related EEG Alzheimer’s disease risk interpretable deep learning early screening biomarker |
| url | https://www.mdpi.com/2313-7673/10/7/468 |
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