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3D卷积神经网络揭示了阿尔茨海默病亚分数的模式特定的大脑成像预测因素

Kaida Ning¹, Pascale B Cannon², Jiawei Yu²

¹Peng Cheng Laboratory, Shenzhen, Guangdong, China.

Brain informatics

|February 4, 2024

概括

阿尔茨海默病 (AD) 的认知变化与特定的大脑特征有关. 磁共振成像 (MRI) 显示与皮层下结构的关联,而氧糖-正子辐射断层扫描 (FDG-PET) 则与皮层代谢有关.

科学领域:

神经科学是一个神经科学.
医疗成像医学成像
人工智能的人工智能

背景情况:

阿尔茨海默病 (AD) 显著影响认知功能.
了解大脑成像特征与阿尔茨海默病中的特定认知变化之间的联系是有限的.
这些协会在成像模式 (如MRI,FDG-PET) 中的差异还没有得到很好的定义.

研究的目的:

调查大脑成像特征与阿尔茨海默病评估量表-认知子量表 (ADAS-Cog13) 分分数之间的关联.
探索MRI和FDG-PET数据之间的这些关联如何不同.
用3D卷积神经网络 (CNN) 模型来预测认知变化.

主要方法:

训练有素的3D CNN模型使用MRI和FDG-PET数据预测ADAS-Cog13子得分.
分析训练网络以确定与每个认知子分数相关的关键大脑特征.
从MRI和FDG-PET模式中获得的相对关联模式.

主要成果:

每个ADAS-Cog13子评分与每个成像模式内的不同大脑特征相关.
核磁共振成像数据显示了认知子分数和皮下区域 (杏仁体,海马体,皮) 的结构变化之间的关联.
FDG-PET数据显示了脑皮层区域 (带回形,脑后皮层,中额头回形,前,小脑) 的认知功能和代谢变化之间的关联.

结论:

特定的大脑特征与阿尔茨海默病中明显的认知缺陷有独特的关联.
核磁共振和FDG-PET突出了AD中大脑认知关系的不同但互补的模式.
调查多种脑成像模式对于全面了解AD病因至关重要.

关键词:

阿尔茨海默氏症的疾病是阿尔茨海默氏症.脑部成像脑部成像认知功能认知功能神经网络的神经网络

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