从轻度认知障碍转化为阿尔茨海默病的结构性,静态和动态功能性MRI预测器:上海记忆研究和ADNI的跨队列验证
Zhihan Chen1,2, Keliang Chen3, Yuxin Li1,4
1Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Human brain mapping
|November 22, 2023
概括
预测阿尔茨海默病从轻度认知障碍 (MCI) 的进展至关重要. 这项研究开发了一个新的框架,使用结构性和功能性MRI来识别大脑变化,在预测MCI转换方面达到高准确性.
科学领域:
- 神经成像是一种神经成像.
- 神经学 神经学
- 生物标志物发现发现
背景情况:
- 轻度认知障碍 (MCI) 是阿尔茨海默病 (AD) 的前体,但转化机制尚未完全理解.
- 使用成像生物标志物预测MCI转化是具有挑战性的,因为队列限制和可重现性问题.
研究的目的:
- 开发和验证一种新的框架,用于使用多模态MRI方法预测MCI转换.
- 研究结构性,静态功能性和动态功能性大脑特征在MCI进展中的作用.
主要方法:
- 在两个独立的队列中,MCI转换器 (MCI_C) 和非转换器 (MCI_NC) 之间的结构MRI (sMRI) 和静止状态fMRI (rs-fMRI) 特性进行了比较.
- 使用支持矢量机 (SVM) 进行预测模型构建,并通过内部 (上海记忆研究) 和外部 (ADNI) 队列验证.
- 集成的静态和动态fMRI功能来评估它们的联合预测能力.
主要成果:
- MCI转换器显示缩,区域性过度活跃,网络间的低连接性,以及中间叶,后壁皮质皮质和皮质的动态变化.
- 所有基于MRI的预测模型都实现了AUC>0.7,多模体MRI模型表现出色 (AUC>0.85).
- 结合静态和动态fMRI特征,比单独预测更好的预测性能,突出显示大脑动态的重要性.
结论:
- 拟议的框架提供了一种可靠的方法,用于跨队列MCI转换预测.
- 大脑结构和动态功能变化是MCI向AD进展的关键指标.
- 这项研究提供了MCI转换机制的见解,并支持先进的MRI生物标志物的临床实用性.
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