评估大脑年龄作为大脑健康的特定标志物
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.
bioRxiv : the preprint server for biology
|November 28, 2024
概括
预测阿尔茨海默病痴呆症结果的直接模型优于一般的大脑年龄模型. 虽然微调的大脑年龄模型与直接模型的性能相匹配,但它们无法超越它们,这突显了有针对性的方法的价值.
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:
- 在大型数据集上训练的大脑年龄模型作为一般大脑健康标志物.
- 大型语言模型在针对特定应用程序进行微调时表现出成功.
- 直接模型在预测特定结果方面往往优于代用模型.
研究的目的:
- 为了比较大规模大脑年龄模型与特定健康结果的直接模型的预测性能.
- 在阿尔茨海默病 (AD) 痴呆症预测的背景下调查大脑年龄模型的有效性.
主要方法:
- 利用了三大洲1,848名参与者的解剖T1扫描.
- 与直接模型相比,非微调大脑年龄模型的预测准确性.
- 评估微调大脑年龄模型与直接模型对比,考虑预训练数据尺度差异.
主要成果:
- 直接模型在预测AD痴呆症方面表现优于非微调大脑年龄模型.
- 精心调整的大脑年龄模型的性能与直接模型相比.
- 尽管有大量的预训练数据 (N=53,542),但微调的大脑年龄模型的表现并没有超过在较小数据集上训练的直接模型 (N=50).
结论:
- 大脑年龄仍然是大脑整体健康的重要指标.
- 对于预测AD痴呆症等特定的神经结果,直接模型具有显著的价值.
- 提取特定大脑健康标志物的小规模,有针对性的方法仍然至关重要,即使大规模的大脑年龄模型的出现.
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