Department of Functional Anatomy, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan.
Sci Rep. 2022 Mar 11;12(1):4284. doi: 10.1038/s41598-022-08231-y.
The intervention at the stage of mild cognitive impairment (MCI) is promising for preventing Alzheimer's disease (AD). This study aims to search for the optimal machine learning (ML) model to classify early and late MCI (EMCI and LMCI) subtypes using multimodal MRI data. First, the tract-based spatial statistics (TBSS) analyses showed LMCI-related white matter changes in the Corpus Callosum. The ROI-based tractography addressed the connected cortical areas by affected callosal fibers. We then prepared two feature subsets for ML by measuring resting-state functional connectivity (TBSS-RSFC method) and graph theory metrics (TBSS-Graph method) in these cortical areas, respectively. We also prepared feature subsets of diffusion parameters in the regions of LMCI-related white matter alterations detected by TBSS analyses. Using these feature subsets, we trained and tested multiple ML models for EMCI/LMCI classification with cross-validation. Our results showed the ensemble ML model (AdaBoost) with feature subset of diffusion parameters achieved better performance of mean accuracy 70%. The useful brain regions for classification were those, including frontal, parietal lobe, Corpus Callosum, cingulate regions, insula, and thalamus regions. Our findings indicated the optimal ML model using diffusion parameters might be effective to distinguish LMCI from EMCI subjects at the prodromal stage of AD.
干预轻度认知障碍（MCI）阶段有望预防阿尔茨海默病（AD）。本研究旨在寻找最佳机器学习（ML）模型，使用多模态 MRI 数据对早期和晚期 MCI（EMCI 和 LMCI）亚型进行分类。首先，基于束的空间统计学（TBSS）分析显示 LMCI 相关的胼胝体白质变化。基于 ROI 的束追踪通过受影响的胼胝体纤维解决了受影响的皮质区域的连通性。然后，我们通过分别测量这些皮质区域的静息状态功能连接（TBSS-RSFC 方法）和图论度量（TBSS-Graph 方法），为 ML 准备了两个特征子集。我们还在 TBSS 分析检测到的与 LMCI 相关的白质改变区域中准备了扩散参数的特征子集。使用这些特征子集，我们使用交叉验证对 EMCI/LMCI 分类进行了多个 ML 模型的训练和测试。我们的结果表明，具有扩散参数特征子集的集成 ML 模型（AdaBoost）的平均准确率为 70%，性能更好。用于分类的有用大脑区域包括额叶、顶叶、胼胝体、扣带回区域、脑岛和丘脑区域。我们的研究结果表明，使用扩散参数的最佳 ML 模型可能有效区分 AD 前驱期的 LMCI 和 EMCI 受试者。
