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联合受约束的小组稀疏连接代表性改善了阿尔茨海默病的早期诊断在常规获得的T1加权成像基础的大脑网络上

Chuanzhen Zhu¹, Honglun Li², Zhiwei Song¹

¹School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China.

Health information science and systems

|March 11, 2024

概括

这项研究引入了一种使用结构性MRI分析阿尔茨海默病 (AD) 脑网络的新方法. 联合受约束组稀疏表示 (JCGS-radMBN) 通过整合白质连接性和区域相似性来改善早期AD诊断.

科学领域:

神经成像是一种神经成像.
生物医学工程生物医学工程
数据科学数据科学数据科学

背景情况:

来自结构性MRI (sMRI) 的基于放射学的形态大脑网络 (radMBN) 用于阿尔茨海默病 (AD) 研究.
目前的radMBN方法在表征AD方面存在局限性,因为sMRI的重点是解剖变化,而不是神经元活动.

研究的目的:

开发一个小组稀疏的radMBN (JCGS-radMBN) 代表,集成白质纤维连接和sMRI区域相似性.
通过结合解剖学和连接数据来增强AD的特征.

主要方法:

利用两个数据集 (ADNI和AIBL) 与sMRI和扩散MRI (dMRI) 进行JCGS-radMBN构建和早期AD诊断验证.
采用联合约束方法,包括集团级白质纤维连接和个人级的sMRI区域相似性.
使用三重图卷积网络用于早期AD诊断,并确定了歧视性大脑连接.

主要成果:

与其他五种脑网络构建方法相比,JCGS-radMBN表现出优越的分类性能.
在分类正常对照 (NC) 与正常对照 (NC) 之间获得了1-30%的更高准确性. 跨数据集的AD患者.
识别了与海马,准海马圈和基底腺相关的歧视性大脑连接,与认知得分 (MMSE) 相对应.

结论:

通过JCGS-radMBN方法,可以有效地使用常规获得的sMRI来描述AD中的大脑网络.
这种方法为早期AD检测提供了改进的诊断能力.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.图表神经网络的神经网络磁共振成像技术磁共振成像技术无线电学 (Radiomics) 是一种无线电学.

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