Inferring brain-effective connectivity networks from neuroimaging data has become a very hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based on Bayesian network score have been greatly developed and become an emerging method for inferring effective connectivity. However, the previous score functions ignore the temporal information from functional magnetic resonance imaging (fMRI) series data and may not be able to determine all orientations in some cases. In this article, we propose a novel score function for inferring effective connectivity from fMRI data based on the conditional entropy and transfer entropy (TE) between brain regions. The new score employs the TE to capture the temporal information and can effectively infer connection directions between brain regions. Experimental results on both simulated and real-world data demonstrate the efficacy of our proposed score function.

中文翻译：

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使用时间熵分数从fMRI时间序列推断有效的连通性网络。

从神经影像数据推断出大脑有效的连接网络已成为神经信息学和生物信息学中的一个非常热门的话题。近年来，基于贝叶斯网络得分的搜索方法得到了极大的发展，并成为推断有效连通性的新兴方法。但是，以前的得分函数会忽略功能磁共振成像（fMRI）系列数据中的时间信息，在某些情况下可能无法确定所有方向。在本文中，我们提出了一种新颖的评分函数，可基于脑区域之间的条件熵和转移熵（TE）从功能磁共振成像数据中推断出有效的连通性。新分数使用TE来捕获时间信息，并且可以有效地推断大脑区域之间的连接方向。