揭示白质微观结构的遗传架构,通过未经监督的分数异质图的深度表示学习
Xingzhong Zhao1, Ziqian Xie1, Wei He1
1McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
Research square
|September 26, 2025
概括
这项研究引入了一种新的深度学习方法 (UDR-WM),使用voxel-wise FA地图分析白质完整性. 这种方法揭示了大脑结构,认知特征和神经系统疾病之间的新型遗传联系.
科学领域:
- 神经成像和遗传学
- 白物质完整性分析
- 深度学习在神经科学中的应用
背景情况:
- 来自扩散MRI的微分异位性 (FA) 是白质 (WM) 完整性的关键.
- 传统的FA研究平均数据,可能缺少局部微结构变异.
- 关于WM完整性的遗传研究受到简化表型的限制.
研究的目的:
- 开发一个深度学习框架 (UDR-WM) 来提取全面的大脑范围的FA特征 (UDIP-FA).
- 以更高的分辨率研究白质完整性的遗传结构.
- 探索白质结构,认知功能和大脑疾病之间的遗传联系.
主要方法:
- 使用voxel-wise的FA地图作为WM (UDR-WM) 框架无监督深度表示的输入.
- 提取了非监督的FA深度集成表型 (UDIP-FA),捕获分布式微观结构变异.
- 对已识别的基因进行多变体全基因组关联研究 (GWAS) 和网络分析.
主要成果:
- 与传统的FA表型相比,UDIP-FA对衰老有更高的敏感性和更大的遗传性.
- 在586个位点中确定了939个显著的SNP,映射到3480个UDIP-FA相关基因 (UFAGs).
- UFAG与质细胞 (星球细胞,寡类细胞),精神分裂症,帕金森病和认知特征有关,这对髓化和轴突结构有影响.
结论:
- UDIP-FA是一种敏感的表型,用于对WM完整性的高分辨率遗传分析.
- 这些发现突出了WM结构与大脑疾病/认知特征之间的显著遗传相关性.
- 确定了神经精神疾病的潜在治疗点 (例如,ACHE,ALDH2).
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