Abstract：
Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.
摘要：
功能网络（FN）分析在发现对脑功能的见解和理解各种脑部疾病的病理生理学中起着关键作用。本文重点介绍了从功能磁共振成像（fMRI）数据中导出脑FN的经典和先进方法。我们系统地回顾了他们的基本原则，优势,缺点,和相互关系，包括静态和动态FN提取方法。在静态FN提取的背景下，我们提出了假设驱动的方法，如基于感兴趣区域(ROI)的方法以及数据驱动的方法，包括矩阵分解，聚类，和深度学习。对于动态FN提取，关于时变FN的估计和FN状态的后续计算，研究了基于窗口和无窗口的方法。我们还讨论了各种方法的适用范围和未来改进的途径。
