生物标志物 生物标志物
Xingzhong Zhao1, Wei He1, Ziqian Xie1
1The University of Texas Health Science Center at Houston, Houston, TX, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
不监督的微分异构 (FA) 图像的深度学习创造了新的表型 (UDIP-FA),更好地捕捉了白质 (WM) 遗传性和与大脑疾病的遗传联系. 这种方法提供了一种更强大的方法来研究WM的微结构完整性及其与神经条件的关系.
科学领域:
- 神经成像是一种神经成像.
- 遗传学 是一个遗传学.
- 机器学习 机器学习
背景情况:
- 分数异构性 (FA) 是白质 (WM) 微结构完整性的关键MRI生物标志物.
- 目前基于图谱的FA分析方法由于提取变异性和忽视复杂的WM通道相互作用而存在局限性.
- 了解FA的遗传结构对于神经发育,衰老和阿尔茨海默氏症等疾病至关重要.
研究的目的:
- 开发一种新的,不偏见的方法,使用深度学习来导出WM成像表型.
- 研究这些新型现象的遗传结构及其与大脑疾病的关联.
- 探索将WM结构与大脑健康和疾病联系起来的生物机制.
主要方法:
- 在来自6000名英国生物库参与者的FA图像上训练了一个无监督的深度神经网络,以创建128维的无监督深度学习衍生成像FA表型 (UDIP-FA).
- 利用基于扰乱的解码器解释和大脑疾病分类任务进行评估.
- 对25875名参与者进行了UDIP-FA的全基因组关联研究 (GWAS),随后进行了验证,功能注释和基因映射.
主要成果:
- 在UDIP-FA中,可以对6种大脑疾病进行分类,AUC为0.64±0.08.
- 在UDIP-FA中,SNP遗传率明显高 (平均为50.81%),高于传统的FA表型.
- GWAS确定了3782个与156个UDIP-FA相关基因 (UFAGs) 映射的显著SNP,这些SNP富含着寡基细胞前体细胞 (OPC) 和质细胞.
- 发现UFAGs ZIC1和ZIC4可以调节阿尔茨海默病风险基因.
- UDIP-FA显示了与智力的显著遗传相关性.
结论:
- UDIP-FA 提供了对WM微观结构完整性的更加公正和可遗传的描述.
- 这种深度学习方法有助于揭示WM的遗传结构.
- 提供了一种有前途的方法来探索WM和大脑疾病之间的生物联系.
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