IEEE Trans Med Imaging. 2018 Feb;37(2):649-662. doi: 10.1109/TMI.2017.2774364.
There is intense interest in fMRI research on whole-brain functional connectivity, and however, two fundamental issues are still unresolved: the impact of spatiotemporal data resolution (spatial parcellation and temporal sampling) and the impact of the network construction method on the reliability of functional brain networks. In particular, the impact of spatiotemporal data resolution on the resulting connectivity findings has not been sufficiently investigated. In fact, a number of studies have already observed that functional networks often give different conclusions across different parcellation scales. If the interpretations from functional networks are inconsistent across spatiotemporal scales, then the whole validity of the functional network paradigm is called into question. This paper investigates the consistency of resting state network structure when using different temporal sampling or spatial parcellation, or different methods for constructing the networks. To pursue this, we develop a novel network comparison framework based on persistent homology from a topological data analysis. We use the new network comparison tools to characterize the spatial and temporal scales under which consistent functional networks can be constructed. The methods are illustrated on Human Connectome Project data, showing that the DISCOH network construction method outperforms other approaches at most data spatiotemporal resolutions.
人们对全脑功能连接的 fMRI 研究非常感兴趣，但仍有两个基本问题尚未解决：时空数据分辨率（空间分割和时间采样）的影响，以及网络构建方法对功能脑网络可靠性的影响。特别是，时空数据分辨率对连接发现的影响尚未得到充分研究。事实上，许多研究已经观察到，功能网络在不同的分割尺度上往往会得出不同的结论。如果功能网络的解释在时空尺度上不一致，那么功能网络范式的整体有效性就会受到质疑。本文研究了在使用不同的时间采样或空间分割，或不同的网络构建方法时，静息态网络结构的一致性。为了实现这一目标，我们从拓扑数据分析中开发了一种基于持久同调的新的网络比较框架。我们使用新的网络比较工具来描述可以构建一致功能网络的空间和时间尺度。该方法在人类连接组计划数据上进行了说明，结果表明，在大多数时空分辨率下，DISCOH 网络构建方法都优于其他方法。
