注意差距:大脑年龄是否改善了阿尔茨海默病的预测?
Trevor Wei Kiat Tan1,2,3,4,5, Kim-Ngan Nguyen1, Chen Zhang1,2,3,4
1Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Human brain mapping
|August 18, 2025
概括
大脑年龄模型虽然对大脑的整体健康有用,但对于预测阿尔茨海默氏症,痴呆症等特定结果可能不是最佳的. 直接预测模型往往优于大脑年龄模型,特别是当大脑年龄模型没有特别微调时.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 计算神经科学是一种神经科学.
背景情况:
- 大脑年龄模型预测时间年龄,并作为大脑健康的标志物.
- 直接对结果 (直接模型) 进行训练的机器学习模型往往优于以替代目标进行训练的模型.
- 大脑年龄模型与直接模型对特定健康结果预测的有效性尚不清楚.
研究的目的:
- 将大规模大脑年龄模型的预测性能与阿尔茨海默病 (AD) 痴呆症预测的直接模型进行比较.
- 从大脑年龄模型中调查不同表示的实用性 (尺度与高维的中间表示).
- 评估微调大脑年龄模型对特定健康结果预测的影响.
主要方法:
- 利用了三大洲1,848名参与者的解剖T1扫描.
- 与预先训练的大型脑年龄模型 (N=53,542) 与直接预测模型进行比较.
- 使用尺度大脑年龄差距和更高维度的中间表示来评估性能,有或没有微调.
主要成果:
- 一个单个标尺大脑年龄差距显示了AD痴呆症的预测性能不佳.
- 来自大脑年龄模型的更高维度中间表示改善了预测,但仍被直接模型所超越.
- 使用中间表示的微调大脑年龄模型是必要的,以匹配直接模型的性能.
结论:
- 大脑年龄是大脑整体健康的重要标志物.
- 预测AD痴呆症等特定的健康结果可能是次优的,使用时间年龄作为大脑年龄模型的预训目标.
- 直接预测模型或微调的大脑年龄模型对于特定的健康结果预测更有效.
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