机器学习分类器和认知表现与海马体子场的关联在amnestic轻度认知障碍中的认知表现
Qi Feng1, Luoyu Wang1, Xue Tang2
1Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China.
Frontiers in aging neuroscience
|December 15, 2023
概括
河马子场体积差异可以帮助区分amnestic轻度认知障碍 (aMCI) 和正常认知. 特定的子场体积与认知测试分数相关,表明它们作为预测标记物的潜力.
科学领域:
- 神经成像是一种神经成像.
- 认知神经科学 认知神经科学
- 机器学习 机器学习
背景情况:
- 患有amnestic轻度认知障碍 (aMCI) 的个体表现出海马体体积和子场结构的改变.
- 有限的研究存在于区分aMCI与正常控制 (NCs) 使用海马子子子场体积建模.
- 在aMCI中,海马体体积和整体认知功能之间的关系需要进一步澄清.
研究的目的:
- 为了研究aMCI和NCs的个体之间的海马体子场体积的差异.
- 为了评估机器学习模型在基于海马子子子域体积的aMCI分类中的有效性.
- 探索海马子场体积与认知评估得分之间的相关性.
主要方法:
- 招募了50名患有aMCI和44名NC的个人.
- 单变通用线性模型分析了海马子子子场体积差异.
- 尺寸缩小和机器学习技术分类为aMCI与NCs.
- 评估了子领域体积和MMSE/MoCA得分之间的相关性.
主要成果:
- 在aMCI和NC组之间,在多个左侧和右侧海马体子场中发现了显著的体积差异.
- 机器学习分类器在区分组时实现了0.678 (左) 和0.701 (右) 的AUC值.
- 特定的子场体积,特别是在子体和分子层,与MoCA得分有很强的相关性.
结论:
- 在aMCI中,海马体子场体积变化是显著的.
- 河马子场体积建模显示了作为aMCI的预测标记物的潜力.
- 这些发现突出了海马体子场体积对认知表现的敏感性.
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