Li Ziyang, Wang Hong, Li Lei
Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China.
Biomimetics (Basel). 2025 Jul 16;10(7):468. 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.
在认知健康个体中早期检测阿尔茨海默病（AD）仍然是一项重大的临床前挑战。脑电图（EEG）是一种很有前景的工具，已显示出在检测AD风险方面的有效性。与任务相关的EEG在阿尔茨海默病研究中很少被使用，因为大多数研究都集中在静息态EEG上。一个可解释的深度学习框架——可解释卷积神经网络（InterpretableCNN）——被用于识别与AD相关的EEG特征。在三种认知任务条件下记录EEG数据，并根据载脂蛋白E（APOE）基因型和多基因风险评分对样本进行标记。使用100倍留p%受试者交叉验证（LPSO-CV）来评估模型性能和泛化能力。该模型在所有任务和受试者中的受试者工作特征曲线下面积（ROC AUC）为60.84%，卡帕值为0.22，表明一致性一般。解释显示，该模型一致聚焦于顶叶和颞叶区域的θ波和α波活动，这些区域通常与AD病理学相关。与任务相关的EEG结合可解释的深度学习能够揭示健康个体中的早期AD风险特征。InterpretableCNN提高了特征识别的透明度，为临床前筛查提供了一个有价值的工具。
