HCTMFS:一个多模特特征选择框架,与ESRDaMCI的更高阶相关的拓分组
Chaofan Song1, Tongqiang Liu2, Haifeng Shi3
1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China.
Computer methods and programs in biomedicine
|November 6, 2023
概括
一个新的框架通过分析大脑网络,准确地诊断末期轻度认知障碍病 (ESRDaMCI). 这种方法提高了诊断准确度,有助于识别ESRDaMCI患者的神经退化.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 计算生物学 计算生物学
背景情况:
- 由于当前诊断工具的局限性,诊断轻度认知障碍 (ESRDaMCI) 末期病具有挑战性.
- 准确的诊断依赖于客观的认知评估,临床观察和神经心理评估,但仅这些方法就有局限性.
研究的目的:
- 开发和验证一种新的多模式特征选择框架,用于对ESRDaMCI患者进行分类.
- 使用先进的成像技术识别与ESRDaMCI相关的歧视性大脑区域.
主要方法:
- 为特征选择提出了一个更高阶的相关拓多重体 (HCTMFS) 框架.
- 使用扩散性曲解成像 (DKI) 和功能性MRI (fMRI) 构建大脑结构和功能网络.
- 提取了网络属性 (节点效率,聚类系数) 并使用了超图拉普拉斯矩阵来进行更高阶拓相关性分析.
主要成果:
- 在对ESRDaMCI患者进行分类时,HCTMFS框架实现了93.56%的准确率.
- 与最先进的方法相比,在灵敏度,特异性和AUC方面表现优越.
- 确定了关键的歧视性大脑区域,表明功能神经退化.
结论:
- 这项研究有效地反映了ESRDaMCI中的功能神经退化.
- 通过选择歧视性大脑区域,为改善ESRDaMCI诊断提供了有价值的参考资料.
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