一种用于动态功能连接性分析的正交半非负矩阵因子化方法及其应用于精神分裂症
IEEE journal of biomedical and health informatics
|November 26, 2025
概括
这项研究引入了一种分析大脑动态功能连接 (dFC) 的新方法,可以直接分析正负相关性. 该方法揭示了精神分裂症患者的独特大脑连接模式,提供了潜在的生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 数据分析 数据分析
背景情况:
- 动态功能连接 (dFC) 分析追踪了随着时间的推移改变大脑区域的相互作用.
- 非负矩阵分解 (NMF) 是有用的,但仅限于正值,不包括反相关性.
- 现有的方法无法完全捕捉到大脑功能相关性的复杂性.
研究的目的:
- 开发一种先进的NMF方法来分析混合符号的dFC数据,克服现有方法的局限性.
- 增强动态功能连接状态的独特性和可解释性.
- 为了确定神经系统疾病,特别是精神分裂症的可复制的dFC生物标志物.
主要方法:
- 提出了一种正交半非负矩阵因子化 (OSemiNMF) 方法来处理混合符号的dFC数据.
- 将对dFC状态 (基础) 纳入一个正交性约束,以提高它们的独特性.
- 在模拟数据集和来自健康对照组和精神分裂症患者的真实静止状态fMRI数据上验证了该方法.
主要成果:
- 在捕获模拟数据上的dFC状态和转换方面,OSemiNMF的表现优于对比方法.
- 在多个静态fMRI数据集中确定可重现的dFC状态和过渡.
- 与健康对照组相比,精神分裂症患者在高连接状态中的时间较短,而皮质下连接是关键的差异化因素.
结论:
- OSemiNMF方法为分析功能连接数据中的复杂大脑动态提供了一个强大的框架.
- 这种方法成功地确定了与精神分裂症相关的可复制的dFC生物标志物.
- 这些发现突显了OSemiNMF在促进我们对大脑疾病的理解方面的潜力.
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