IEEE Trans Med Imaging. 2015 Apr;34(4):846-60. doi: 10.1109/TMI.2014.2358681. Epub 2014 Sep 19.
We develop a new approach to functional brain connectivity analysis, which deals with four fundamental aspects of connectivity not previously jointly treated. These are: temporal correlation, spurious spatial correlation, sparsity, and network construction using trajectory (as opposed to marginal) Mutual Information. We call the new method Sparse Conditional Trajectory Mutual Information (SCoTMI). We demonstrate SCoTMI on simulated and real fMRI data, showing that SCoTMI gives more accurate and more repeatable detection of network links than competing network estimation methods.
我们开发了一种新的功能脑连接分析方法，该方法处理了以前未共同处理的连接的四个基本方面。这些方面是：时间相关性，虚假空间相关性，稀疏性以及使用轨迹（而不是边缘）互信息进行网络构建。我们称新方法为稀疏条件轨迹互信息（SCoTMI）。我们在模拟和真实 fMRI 数据上对 SCoTMI 进行了演示，结果表明 SCoTMI 比竞争的网络估计方法更准确，更可重复地检测网络链接。
