生物标志物 生物标志物
Ava Farnan1, Annabel Sorby-Adams1, Jennifer Guo1
1Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
概括
便携式低场MRI (LF-MRI) 与机器学习相结合,准确量化了阿尔茨海默病 (AD) 患者的大脑变化. 这种可访问的神经成像方法可以在门诊环境中有效监测疾病.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 神经学 神经学
背景情况:
- 磁共振成像 (MRI) 对于监测阿尔茨海默病 (AD) 进展至关重要.
- 便携式低场核磁共振 (LF-MRI) 提供了治疗点的评估和改善了患者的访问.
- 开发了一种机器学习 (ML) 管道,用于使用LF-MRI量化AD患者监测的大脑形态测量.
研究的目的:
- 评估使用LF-MRI与ML用于阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 患者的大脑形态测量量的可行性.
- 为了比较从LF-MRI获得的大脑体积测量与传统高场MRI (HF-MRI).
- 评估LF-MRI在区分AD/MCI患者和患有血管并发症 (VC) 患者之间的潜力.
主要方法:
- 从门诊记忆障碍诊所招募了患有MCI,AD和VC的患者.
- 获得的LF-MRI (0.064 T) 和HF-MRI (1.5-3 T) 扫描.
- 使用基于FreeSurfer的管道 (WMH-SynthSeg) 处理了T2 FLAIR序列,以量化不同区域的大脑体积.
主要成果:
- 在所有区域中,在高频MRI和轻频MRI衍生的大脑体积之间观察到高相关性 (p <0.05).
- 皮质,白质,侧腔室,海马体和杏仁体显示出最高的相关性.
- 与VC队列相比,MCI和AD队列表现出显著的区域缩,特别是在皮质,海马和杏仁体 (p < 0.001).
结论:
- 在MCI或AD患者的临床评估中,可以采用LF-MRI.
- 应用于LF-MRI数据的ML算法产生与HF-MRI可比的脑体积.
- LF-MRI作为一种可访问的神经成像工具,用于监测认知障碍和区分患者子组.
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