Ivanoska Ilinka, Trivodaliev Kire, Kalajdziski Slobodan, Zanin Massimiliano
Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, North Macedonia.
Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain.
Brain Sci. 2021 May 31;11(6):735. doi: 10.3390/brainsci11060735.
Network-based representations have introduced a revolution in neuroscience, expanding the understanding of the brain from the activity of individual regions to the interactions between them. This augmented network view comes at the cost of high dimensionality, which hinders both our capacity of deciphering the main mechanisms behind pathologies, and the significance of any statistical and/or machine learning task used in processing this data. A link selection method, allowing to remove irrelevant connections in a given scenario, is an obvious solution that provides improved utilization of these network representations. In this contribution we review a large set of statistical and machine learning link selection methods and evaluate them on real brain functional networks. Results indicate that most methods perform in a qualitatively similar way, with NBS (Network Based Statistics) winning in terms of quantity of retained information, AnovaNet in terms of stability and ExT (Extra Trees) in terms of lower computational cost. While machine learning methods are conceptually more complex than statistical ones, they do not yield a clear advantage. At the same time, the high heterogeneity in the set of links retained by each method suggests that they are offering complementary views to the data. The implications of these results in neuroscience tasks are finally discussed.
基于网络的表示法在神经科学领域引发了一场革命，将对大脑的理解从单个区域的活动扩展到它们之间的相互作用。这种扩展的网络观点是以高维度为代价的，这既阻碍了我们解读病理背后主要机制的能力，也阻碍了处理这些数据时所使用的任何统计和/或机器学习任务的意义。一种允许在给定场景中去除不相关连接的链接选择方法，是一种明显的解决方案，它能提高这些网络表示法的利用率。在本论文中，我们回顾了大量的统计和机器学习链接选择方法，并在真实的脑功能网络上对它们进行评估。结果表明，大多数方法的表现性质上相似，基于网络的统计方法（NBS）在保留信息的数量方面胜出，方差分析网络（AnovaNet）在稳定性方面胜出，极端随机树（ExT）在计算成本较低方面胜出。虽然机器学习方法在概念上比统计方法更复杂，但它们并没有产生明显优势。同时，每种方法保留的链接集的高度异质性表明，它们为数据提供了互补的视角。最后讨论了这些结果在神经科学任务中的意义。
