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从正常的衰老过程中解开加速认知衰退及其遗传成分:基于神经成像的深度学习方法

Yulin Dai¹, Hsu Yu-Chun¹, Brisa S Fernandes¹

¹The University of Texas Health Science Center at Houston.

Research square

|September 18, 2023

概括

一个深度学习模型有效地区分了阿尔茨海默病 (AD) 的认知衰退与正常衰老,使用MRI扫描. 研究人员在NELL1基因中发现了一种新的基因变异rs144614292,与加速认知衰退有关.

科学领域:

神经科学是一个神经科学.
遗传学遗传学是一个
人工智能的人工智能

背景情况:

阿尔茨海默氏病 (AD) 涉及渐进性的认知衰退,与自然衰老并行.
神经成像可以区分认知变化与典型的衰老和AD.

研究的目的:

开发一个深度学习框架来分析纵向MRI数据,以区分与AD相关的认知衰退.
通过全基因组关联研究 (GWAS) 识别导致AD加速认知衰退的遗传因素.

主要方法:

一个双损失的语ResNet深度学习框架被用来分析来自阿尔茨海默病神经成像倡议 (ADNI) 研究的结构MRI数据.
全基因组关联研究 (GWAS) 和GWAS后分析是在认知衰退数据上进行的.

主要成果:

深度学习模型成功预测了1,313个人的认知衰退.
两个全基因组显著的位置,APOE和rs144614292 (在NELL1内),被确定为加速认知衰退.
通过功能丰富分析突出了微质和免疫反应途径,并且在认知衰退斜率GWAS和以前的AD GWAS之间发现了正相关性.

结论:

深度学习模型有效地提取神经成像特征并预测认知衰退.
在NELL1基因中报告了一种新型变异rs144614292.
这种方法可以将加速认知衰退与正常衰老区分开来,并识别潜在的早期干预的相关遗传因素.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.认知能力下降认知能力下降双损 Siamese ResNet 网络网络的双损.全基因组关联研究研究.神经成像是一种神经成像.多基因风险评分多基因风险评分.

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