基于人工智能的大脑衰老模式的遗传和临床相关性在认知不受损的个体中
Ioanna Skampardoni1,2, Ilya M Nasrallah1,3, Ahmed Abdulkadir1,4
1Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.
JAMA psychiatry
|February 14, 2024
概括
在没有认知障碍的人群中,发现了三种不同的老化大脑模式. 这些子组与遗传学和心血管因素有关,预测未来的认知能力下降,有助于早期发现疾病和个性化医疗.
科学领域:
- 神经成像是一种神经成像.
- 遗传学 遗传学 是一个
- 认知神经科学 认知神经科学
背景情况:
- 大脑衰老涉及到由各种病理影响的复杂的神经解剖学变化.
- 了解结构性大脑变化异质性是确定临床前神经退行性疾病阶段的关键.
研究的目的:
- 在没有被诊断出认知障碍 (WODCI) 的个体中识别出具有共同大脑结构变异模式的独特子组.
- 将这些子组与遗传因素,生物医学措施和认知衰退轨迹联系起来.
主要方法:
- 利用深度学习,半监督集群方法 (生成对抗网络) 来建模特定年龄的结构性大脑变异.
- 分析了来自iSTAGING联盟的5万多个时间点的数据,涉及27,402名WODCI个人,年龄在45-85岁之间.
主要成果:
- 确定了三个一致的子组:典型衰老 (A1) 和两个加速衰老组 (A2,A3).
- A2显示与高血压,白质强度 (WMH),血管疾病遗传学,粉样β (Aβ) 阳性和APOE ε4.4相关.
- A3表现出严重的缩,心血管风险因素 (CVRF) 和更快的认知衰退. 基因关联在各个子组之间有显著的差异.
结论:
- 确定的子组显示出与CVRFs,遗传学和认知衰退的明显联系.
- 这些模式可能代表了各种神经病理过程,影响了阿尔茨海默病的易感性.
- 研究结果支持患者在早期无症状阶段的分层,用于精准医学和临床试验.
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