Castillo-Barnes Diego, Su Li, Ramírez Javier, Salas-Gonzalez Diego, Martinez-Murcia Francisco J, Illan Ignacio A, Segovia Fermin, Ortiz Andres, Cruchaga Carlos, Farlow Martin R, Xiong Chengjie, Graff-Radford Neil R, Schofield Peter R, Masters Colin L, Salloway Stephen, Jucker Mathias, Mori Hiroshi, Levin Johannes, Gorriz Juan M
Department of Signal Theory, Telematics and Communications, University of Granada, Granada (Spain).
Department of Psychiatry, University of Cambridge, Cambridge (UK).
Inf Fusion. 2020 Jun;58:153-167. doi: 10.1016/j.inffus.2020.01.001. Epub 2020 Jan 7.
Despite subjects with Dominantly-Inherited Alzheimer's Disease (DIAD) represent less than 1% of all Alzheimer's Disease (AD) cases, the Dominantly Inherited Alzheimer Network (DIAN) initiative constitutes a strong impact in the understanding of AD disease course with special emphasis on the presyptomatic disease phase. Until now, the 3 genes involved in DIAD pathogenesis (PSEN1, PSEN2 and APP) have been commonly merged into one group (Mutation Carriers, MC) and studied using conventional statistical analysis. Comparisons between groups using null-hypothesis testing or longitudinal regression procedures, such as the linear-mixed-effects models, have been assessed in the extant literature. Within this context, the work presented here performs a comparison between different groups of subjects by considering the 3 genes, either jointly or separately, and using tools based on Machine Learning (ML). This involves a feature selection step which makes use of ANOVA followed by Principal Component Analysis (PCA) to determine which features would be realiable for further comparison purposes. Then, the selected predictors are classified using a Support-Vector-Machine (SVM) in a nested k-Fold cross-validation resulting in maximum classification rates of 72-74% using PiB PET features, specially when comparing asymptomatic Non-Carriers (NC) subjects with asymptomatic PSEN1 Mutation-Carriers (PSEN1-MC). Results obtained from these experiments led to the idea that PSEN1-MC might be considered as a mixture of two different subgroups including: a first group whose patterns were very close to NC subjects, and a second group much more different in terms of imaging patterns. Thus, using a k-Means clustering algorithm it was determined both subgroups and a new classification scenario was conducted to validate this process. The comparison between each subgroup . NC subjects resulted in classification rates around 80% underscoring the importance of considering DIAN as an heterogeneous entity.
尽管显性遗传阿尔茨海默病（DIAD）患者在所有阿尔茨海默病（AD）病例中占比不到1%，但显性遗传阿尔茨海默病网络（DIAN）计划对理解AD病程产生了重大影响，特别强调症状前疾病阶段。到目前为止，参与DIAD发病机制的3个基因（PSEN1、PSEN2和APP）通常被合并为一组（突变携带者，MC），并使用传统统计分析进行研究。现有文献中评估了使用零假设检验或纵向回归程序（如线性混合效应模型）在组间进行的比较。在此背景下，本文提出的工作通过联合或单独考虑这3个基因，并使用基于机器学习（ML）的工具，对不同组的受试者进行比较。这涉及一个特征选择步骤，该步骤利用方差分析（ANOVA），然后进行主成分分析（PCA），以确定哪些特征对于进一步比较目的是可靠的。然后，使用支持向量机（SVM）在嵌套k折交叉验证中对选定的预测因子进行分类，使用PiB PET特征时，最大分类率为72-74%，特别是在比较无症状非携带者（NC）受试者和无症状PSEN1突变携带者（PSEN1-MC）时。从这些实验中获得的结果引发了这样一种想法，即PSEN1-MC可能被视为两个不同亚组的混合体，包括：第一组其模式与NC受试者非常接近，第二组在成像模式方面差异更大。因此，使用k均值聚类算法确定了这两个亚组，并进行了新的分类方案以验证这一过程。每个亚组与NC受试者之间的比较得出的分类率约为80%，突出了将DIAN视为一个异质性实体的重要性。
