从正常的衰老过程中解开加速认知衰退及其遗传组件:基于神经成像的深度学习方法
Yulin Dai1, Yu-Chun Hsu2, Brisa S Fernandes1
1Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Journal of Alzheimer's disease : JAD
|February 2, 2024
概括
一个新的深度学习模型使用MRI扫描来区分阿尔茨海默病 (AD) 认知衰退与正常衰老. 这种方法确定了一个新的遗传变异,rs144614292,与AD进展和免疫路径有关.
科学领域:
- 神经科学是一个神经科学.
- 遗传学 是一个遗传学.
- 人工智能的人工智能
背景情况:
- 阿尔茨海默病 (AD) 的特点是渐进性的认知能力下降,通常与自然衰老交织在一起.
- 神经成像技术可以区分因衰老而导致的认知变化和由AD引起的认知变化.
研究的目的:
- 为了区分正常的衰老效应与阿尔茨海默病的加速认知衰退.
- 通过深度学习和神经成像来识别导致AD相关认知衰退的遗传因素.
主要方法:
- 开发了一个深度学习框架,使用双损失的罗人ResNet网络.
- 利用了来自阿尔茨海默氏病神经成像倡议 (ADNI) 研究的纵向结构磁共振成像 (MRI) 数据.
- 进行全基因组关联研究 (GWAS) 和GWAS后分析,以确定遗传基础.
主要成果:
- 深度学习模型处理了1,313个人的数据,准确地预测了认知衰退.
- 确定了两种与加速认知衰退相关的全基因组显著位置:在染色体11上的APOE位置和rs144614292.
- 在NELL1基因中发现了一种新型变异rs144614292,它与神经元生长和分化有关.
- GWAS信号丰富分析指向微质和免疫反应通路.
结论:
- 深度学习模型有效地提取了神经成像特征,以预测个人的认知衰退.
- 在NELL1基因中报告了一种新的遗传变异,rs144614292,为AD病原体提供了新的见解.
- 突出了免疫路径在与阿尔茨海默病相关的认知衰退中的作用.
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