Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Academy for Engineering & Technology, Fudan University, Shanghai, China.
Hum Brain Mapp. 2024 Jan;45(1):e26529. doi: 10.1002/hbm.26529. Epub 2023 Nov 22.
Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.
轻度认知障碍（MCI）是阿尔茨海默病（AD）的关键前驱阶段，其转化的机制尚未完全阐明。由于缺乏纵向队列和各种特定研究的成像指标的可重复性，构建和跨队列验证用于预测 MCI 转化的成像生物标志物目前具有很大的挑战性。我们提出了一种新的跨队列 MCI 转化预测框架，涉及比较 MCI 转化者（MCI_C）和非转化者（MCI_NC）之间结构磁共振成像（sMRI）和静息态功能磁共振成像（fMRI）的结构、静态和动态功能脑特征，并使用支持向量机构建预测模型。从两个独立的队列中选择了 218 名具有 3 年随访结果的 MCI 患者：上海记忆研究队列用于内部交叉验证，以及阿尔茨海默病神经影像学倡议（ADNI）队列用于外部验证。与 MCI_NC 相比，MCI_C 的主要特征是萎缩、区域过度活跃和网络间连接减少，以及以区域和连接不稳定为特征的动态改变，涉及内侧颞叶（MTL）、后顶叶皮层（PPC）和枕叶。所有基于影像学的预测模型在两个队列中的曲线下面积（AUC）均大于 0.7，多模态 MRI 模型的表现最好，AUC 大于 0.85。值得注意的是，静息态和动态 fMRI 的结合比静息态或动态 fMRI 单独使用的表现更好，这支持了动态特征的贡献。这项跨队列验证研究为涉及大脑动力学的 MCI 转化机制提供了新的见解，并为未来结构和功能 MRI 生物标志物在临床中的应用铺平了道路。
