区域大脑衰老模式揭示了神经退行症的疾病特定途径
Pál Vakli1, Béla Weiss1,2, Attila Keresztes1,3
1Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary.
medRxiv : the preprint server for health sciences
|November 26, 2025
概括
BrainAgeMap是一个新的AI工具,从MRI扫描中创建详细的大脑衰老地图. 它识别了神经系统疾病 (如阿尔茨海默氏症) 中的特定衰老模式,有助于诊断和治疗.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 大脑衰老的异质性是神经系统疾病的关键.
- 当前的机器学习 (大脑年龄) 使用全球指标,缺乏具体性.
- 需要详细的,可解释的工具来绘制大脑衰老模式.
研究的目的:
- 介绍BrainAgeMap,一个可解释的深度学习框架.
- 生成大脑预测年龄差异 (大脑-PAD) 的语音智能地图.
- 评估BrainAgeMap的临床,预后和神经生物学实用性.
主要方法:
- 开发了一个深度学习框架BrainAgeMap.
- 使用了T1权重磁共振成像 (MRI) 扫描.
- 使用阿尔茨海默病 (AD),前性痴呆症,精神分裂症和轻度认知障碍 (MCI) 队列的数据进行验证.
主要成果:
- 确定了加速大脑衰老的疾病特异性模式.
- 在轻度认知障碍 (MCI) 进展中显示出预后价值.
- 链接区域大脑-PAD与TAU病理 (PET) 在AD和MCI/AD的记忆功能.
结论:
- BrainAgeMap提供细粒度,可解释的大脑衰老地图.
- 在识别疾病特异性神经退行途径方面具有实用性.
- 提供了早期诊断,患者分层和监测干预的潜力.
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