Zhang Lei, Binns Malcolm, Chow Ricky, Rabi Rahel, Anderson Nicole D, Lu Jing, Freedman Morris, Alain Claude
Rotman Research Institute, Baycrest Academy for Research and Education, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada.
Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5T 3M7, Canada.
Geroscience. 2025 Jul 22. doi: 10.1007/s11357-025-01799-z.
Early detection of amnestic mild cognitive impairment (aMCI) is crucial for timely interventions. This study combines scalp recordings of lateralized auditory, visual, and somatosensory stimuli with a flexible and interpretable support vector machine learning pipeline to differentiate individuals diagnosed with aMCI from healthy controls. Event-related potentials (ERPs) and functional connectivity (FC) matrices from each modality successfully predicted aMCI. Reduced ERP amplitude in aMCI contributed to classification. The analysis of FC using phase-locking value revealed higher FC in aMCI than controls in frontal regions, which predicted worse cognitive performance, and lower FC in posterior regions from delta to alpha frequency. We observe optimal classification accuracy (96.1%), sensitivity (97.7%), and specificity (94.3%) when combining information from all sensory conditions rather than when using information from a single modality. The results highlight the clinical potential of sensory-evoked potentials in detecting aMCI, with optimal classification using both amplitude and oscillatory-based FC measures from multiple modalities.
遗忘型轻度认知障碍（aMCI）的早期检测对于及时干预至关重要。本研究将听觉、视觉和体感刺激的头皮记录与灵活且可解释的支持向量机学习管道相结合，以区分被诊断为aMCI的个体与健康对照。来自每种模态的事件相关电位（ERP）和功能连接（FC）矩阵成功预测了aMCI。aMCI中ERP振幅降低有助于分类。使用锁相值对FC的分析显示，aMCI在额叶区域的FC高于对照组，这预测了较差的认知表现，而后部区域从δ到α频率的FC较低。我们观察到，当结合所有感觉条件的信息时，而不是使用单一模态的信息时，分类准确率（96.1%）、敏感性（97.7%）和特异性（94.3%）最佳。结果突出了感觉诱发电位在检测aMCI方面的临床潜力，通过使用来自多种模态的振幅和基于振荡的FC测量进行最佳分类。
