使用功能性磁共振成像解开大脑中的因果网络：当前方法的综述
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
摘要：
在过去的二十年中，功能性磁共振成像已被用于将神经元网络活动与认知过程和行为联系起来。最近，这种方法已经被算法增强，使我们能够推断神经元网络的组成群体之间的因果关系。已经提出了多种推理程序来解决这个研究问题，但到目前为止，每种方法在建立全脑连接模式时都有局限性。在这项工作中，我们讨论了八种方法来推断因果关系的功能磁共振成像研究：贝叶斯网络，动态因果模型，格兰杰因果关系，似然比，LiNGAM，帕特尔的Tau，结构方程模型，和转移熵。最后，我们将为这一领域的未来方向提出一些建议。
英文摘要：
In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
