School of Life Science, Beijing Institute of Technology, Beijing, China.
Daniel Felix Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO, USA.
J Alzheimers Dis. 2019;68(3):1013-1027. doi: 10.3233/JAD-181049.
Alzheimer's disease (AD) is one of the most common progressive and irreversible neurodegenerative diseases. The study of the pathological mechanism of AD and early-stage diagnosis is essential and important. Subjective cognitive decline (SCD), the first at-risk stage of AD occurring prior to amnestic mild cognitive impairment (aMCI), is of great research value and has gained our interest. To investigate the entire pathological development of AD pathology efficiently, we proposed a machine learning classification method based on a multimodal support vector machine (SVM) to investigate the structural and functional connectivity patterns of the three stages of AD (SCD, aMCI, and AD). Our experiments achieved an accuracy of 98.58% in the AD group, 97.76% in the aMCI group, and 80.24% in the SCD group. Moreover, in our experiments, we identified the most discriminating brain regions, which were mainly located in the default mode network and subcortical structures (SCS). Notably, with the development of AD pathology, SCS regions have become increasingly important, and structural connectivity has shown more discriminative power than functional connectivity. The current study may shed new light on the pathological mechanism of AD and suggests that whole-brain connectivity may provide potential effective biomarkers for the early-stage diagnosis of AD.
阿尔茨海默病（AD）是最常见的进行性和不可逆转的神经退行性疾病之一。研究 AD 的病理机制和早期诊断至关重要。主观认知下降（SCD）是 AD 发生于遗忘型轻度认知障碍（aMCI）之前的第一个风险阶段，具有重要的研究价值，引起了我们的兴趣。为了有效地研究 AD 病理的整个发展过程，我们提出了一种基于多模态支持向量机（SVM）的机器学习分类方法，以研究 AD 的三个阶段（SCD、aMCI 和 AD）的结构和功能连接模式。我们的实验在 AD 组中达到了 98.58%的准确率，在 aMCI 组中达到了 97.76%的准确率，在 SCD 组中达到了 80.24%的准确率。此外，在我们的实验中，我们确定了最具区分力的脑区，这些脑区主要位于默认模式网络和皮质下结构（SCS）中。值得注意的是，随着 AD 病理的发展，SCS 区域变得越来越重要，结构连接比功能连接具有更强的判别能力。本研究可能为 AD 的病理机制提供新的见解，并表明全脑连接可能为 AD 的早期诊断提供潜在的有效生物标志物。
