School of Life Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing 100081, P.R. China.
ACS Chem Neurosci. 2021 Nov 17;12(22):4209-4223. doi: 10.1021/acschemneuro.1c00472. Epub 2021 Nov 1.
The neuroimaging method of multimodal magnetic resonance imaging (MRI) can identify the changes in brain structure and function caused by Alzheimer's disease (AD) at different stages, and it is a practical method to study the mechanism of AD progression. This paper reviews the studies of methods and biomarkers for predicting AD progression based on multimodal MRI. First, different approaches for predicting AD progression are analyzed and summarized, including machine learning, deep learning, regression, and other MRI analysis methods. Then, the effective biomarkers of AD progression under structural magnetic resonance imaging, diffusion tensor imaging, functional magnetic resonance imaging, and arterial spin labeling modes of MRI are summarized. It is believed that the brain changes shown on MRI may be related to the cognitive decline in different prodrome stages of AD, which is conducive to the further realization of early intervention and prevention of AD. Finally, the deficiencies of the existing studies are analyzed in terms of data set size, data heterogeneity, processing methods, and research depth. More importantly, future research directions are proposed, including enriching data sets, simplifying biomarkers, utilizing multimodal magnetic resonance, etc. In the future, the study of AD progression by multimodal MRI will still be a challenge but also a significant research hotspot.
多模态磁共振成像（MRI）的神经影像学方法可以识别不同阶段阿尔茨海默病（AD）引起的大脑结构和功能变化，是研究 AD 进展机制的实用方法。本文综述了基于多模态 MRI 预测 AD 进展的方法和生物标志物的研究。首先，分析和总结了预测 AD 进展的不同方法，包括机器学习、深度学习、回归和其他 MRI 分析方法。然后，总结了 MRI 结构磁共振成像、弥散张量成像、功能磁共振成像和动脉自旋标记模式下 AD 进展的有效生物标志物。可以认为，MRI 上显示的脑变化可能与 AD 不同前驱期的认知下降有关，这有助于进一步实现 AD 的早期干预和预防。最后，从数据集大小、数据异质性、处理方法和研究深度等方面分析了现有研究的不足。更重要的是，提出了未来的研究方向，包括丰富数据集、简化生物标志物、利用多模态磁共振等。未来，多模态 MRI 对 AD 进展的研究仍将是一个挑战，但也是一个重要的研究热点。
