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对跨学科队列的大脑年龄差距的分析和预测模型架构的分析

Lara Dular¹, Žiga Špiclin¹, For The Alzheimer's Disease Neuroimaging Initiative¹

¹University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, 1000 Ljubljana, Slovenia.

|September 28, 2024

概括

使用深度学习模型预测的大脑年龄差距 (BAG) 在各种神经疾病中显示出显著的差异. 阿尔茨海默氏症痴呆症和多发性硬化症始终显示出最大的BAG,这表明这些疾病中的大脑衰老加速.

科学领域:

神经成像是一种神经成像.
人工智能的人工智能
生物标志物发现发现

背景情况:

从MRI扫描中得出的大脑年龄差距 (BAG) 作为神经和精神疾病的生物标志物.
深度学习模型越来越多地用于大脑年龄的预测.

研究的目的:

为了比较五种深度学习模型在各种神经疾病中预测大脑年龄差距 (BAG) 的性能.
评估BAG预测在不同疾病队列和模型架构中的一致性.

主要方法:

利用五种深度学习模型 (2D/3D VGG,RelationNet,ResNet,SFCN) 来从T1加权的MRI扫描中预测大脑年龄.
对健康对照组和患有睡眠呼吸暂停,糖尿病,多发性硬化症,帕金森病,轻度认知障碍和阿尔茨海默病的队列进行了评估.
采用严格的统计分析,包括重复模型训练和线性混合效应模型.

主要成果:

所有模型都检测到糖尿病,多发性硬化症,轻度认知障碍和阿尔茨海默病的显著阳性BAG.
SFCN模型显示了最高的整体BAG,特别是在阿尔茨海默氏症痴呆症 (6.48年).
随着年龄的增长,观察到BAG下降的趋势,在患病的队列中更为明显.

结论:

阿尔茨海默氏症痴呆症和多发性硬化症始终显示出最大的BAG,证实了加速的大脑衰老.
该SFCN模型在检测跨疾病的BAG中表现出最高的灵敏度.
随着年龄的增长,BAG的下降趋势表明疾病进展,生存偏差和治疗效应的复杂相互作用.

关键词:

这是阿尔茨海默氏症痴呆症.帕金森病是帕金森氏症的一种疾病.英国生物银行大脑年龄大脑年龄大脑年龄差距的大脑年龄差距深度回归模型的深度回归模型糖尿病糖尿病患者糖尿病患者多发性硬化症多发性硬化症睡眠呼吸暂停 (Sleep Apnea) 是一个

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