使用里曼的部分最小平方捕获功能连接学
Matthew Ryan1, Gary Glonek2, Jono Tuke2
1School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, 5005, Australia. matthew.ryan@adelaide.edu.au.
Scientific reports
|October 13, 2023
概括
这项研究引入了R-PLS,这是一种分析大脑功能连接矩阵的新方法. R-PLS具有独特的矩阵特性,可以改善神经疾病中关键神经连接的识别.
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:
- 功能性和解剖学性大脑连接体有助于理解神经系统疾病.
- 功能磁共振成像 (fMRI) 通过血液氧气水平依赖 (BOLD) 信号测量大脑功能.
- 来自fMRI的功能连接矩阵代表大脑网络相互作用.
研究的目的:
- 在分析功能连接矩阵时解决传统的部分最小方程 (PLS) 的局限性.
- 引入一种通用PLS方法 (R-PLS),该方法包含这些矩阵的正确确性属性.
- 将R-PLS应用于神经成像数据集,以识别重要的功能性大脑连接.
主要方法:
- 开发了对里曼的多元体的概括部分最小平方 (R-PLS) 方法.
- 将R-PLS应用到使用亲属不变几何学对称的正确数矩阵.
- 利用预测 (VIP) 统计数据中的变量重要性来解释R-PLS结果.
主要成果:
- 在COBRE和ABIDE功能成像数据集中应用了R-PLS.
- 在数据集中确定了关键的功能联系,比较了精神分裂症患者与对照者,以及自闭症谱系障碍患者与神经类型的个人.
- 识别的连接与现有文献保持一致,验证了R-PLS方法.
结论:
- R-PLS提供了一种统计学上可靠的方法来分析大脑功能连接矩阵.
- 该方法成功地在临床数据集中确定了相关的功能联系.
- 在神经成像和多模式数据分析方面,R-PLS具有更广泛应用的潜力.
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