ds-FCRN:三维双流完全卷积的残余网络和基于变压器的全球本地特征学习,用于大脑年龄预测
Yutong Wu1, Chen Zhang1, Xiangge Ma1
1Department of Biomedical Engineering, College of Chemistry and Life Sciences, Beijing University of Technology, Beijing, 100124, China.
Brain structure & function
|January 18, 2025
概括
这项研究引入了一个深度学习模型,用于使用MRI扫描来准确预测大脑年龄. 该模型确定了影响大脑衰老的关键大脑区域和生活方式因素,提供了对认知健康的见解.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 老年学是指老年学的学科.
背景情况:
- 衰老与大脑缩和认知能力下降有关.
- 使用神经成像来预测大脑年龄是研究大脑衰老的新方法.
- 现有的模型缺乏高准确性和可解释性.
研究的目的:
- 开发一个高度准确和可解释的深度学习模型来预测大脑年龄.
- 使用MRI数据的灰质密度图进行预测.
- 识别影响大脑衰老的大脑区域和生活方式因素.
主要方法:
- 提出了一种创新的3D双流完全卷积残余网络 (ds-FCRN) 与特征学习的变压器相结合.
- 使用了来自16377名健康参与者的T1MRI数据 (UKB数据库).
- 采用Shapley值来解释模型,并识别有影响力的大脑区域.
主要成果:
- 在对3276名受试者的测试组中,大脑年龄预测的平均绝对误差为2.2年.
- 在同一数据集上,3D ds-FCRN 模型的性能优于现有方法.
- 确定叶对预测最有意义,并将额叶衰老与生活方式因素联系起来.
结论:
- 开发的3D ds-FCRN模型在大脑年龄预测方面提供了高准确性和透明度.
- 沙普利值为影响大脑衰老的因素提供了有价值的脑区域层面的见解.
- 这种方法增强了对与年龄相关的大脑变化和潜在干预措施的理解.
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