慢性中风中的深度学习衍生的MRI区域大脑年龄 (ENIGMA):一个多队列,回顾性,观察性研究
Gilsoon Park1, Mahir H Khan2, Justin W Andrushko3
1Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
The Lancet. Digital health
|January 23, 2026
概括
脑中风病变在受影响的半球加速大脑衰老,并在相反的半球减缓它,这表明大脑有补偿性机制. 区域大脑年龄是中风恢复和神经可塑性的潜在生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 放射学 放射学是一门学科.
- 医疗成像医学成像
背景情况:
- 脑卒中会导致慢性脑部变化,影响运动功能.
- 大脑预测年龄差异 (PAD) 是中风后感觉运动和认知功能的生物标志物.
- 之前的研究将全球大脑PAD与较差的运动功能联系起来;局部损伤影响尚不清楚.
研究的目的:
- 研究焦点中风病变负荷,两个半球的区域大脑PAD和慢性中风的运动结果之间的关联.
- 确定中风后运动障碍的关键预测因素.
主要方法:
- 慢性中风患者的回顾性分析 (ENIGMA中风恢复工作组).
- 使用英国生物库数据和图形卷积网络训练区域大脑年龄模型.
- 用线性混合效应模型,机器学习和结构方程建模评估损伤负荷,区域大脑PAD,以及它们与运动结果的关系.
主要成果:
- 较大的病变大小与较高的脑部区域PAD (加速衰老) 相相关.
- 突出网络中的损伤负载双边影响区域大脑PAD.
- 运动结果的首要预测因素包括皮质脊髓道的损伤负荷,突出网络的损伤负荷和逆伤前侧对称网络大脑PAD.
结论:
- 脑卒中病变加速脑衰老和减缓脑衰老,这表明有补偿性神经机制.
- 区域大脑年龄评估可能表明神经可塑性,并指导中风运动恢复干预措施.
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