IEEE Trans Med Imaging. 2018 Jul;37(7):1537-1550. doi: 10.1109/TMI.2018.2831261.
In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.
近年来，受到科学和临床关注的驱动，人们对功能脑网络的分析产生了浓厚的兴趣。这些分析的目的是更好地了解大脑区域如何相互作用，这种相互作用如何依赖于实验条件和行为测量，以及如何识别异常（疾病）。在本文中，我们首先简要回顾了一些主要的功能脑网络分析方法。但我们并没有像传统的综述那样对它们进行比较，而是提请注意它们的显著局限性和盲点。然后，第二，相关专家，勾勒出一些新兴的方法，可以突破这些局限性。特别是，我们讨论了五种这样的方法。前两种，随机块模型和指数随机图模型，为缺乏探索性图分析方法的网络分析提供了推理基础。另外三种方法是：通过持久同调进行网络比较、区分样本波动和神经波动的时变连通性，以及从时间自相关中提取推理强度的网络系统识别。
