Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA.
Neuroimage. 2022 Sep;258:119250. doi: 10.1016/j.neuroimage.2022.119250. Epub 2022 Jun 1.
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
网络神经科学领域已经成为研究大脑的自然框架，并在神经科学的各种不同问题中得到了越来越多的应用。从学科角度来看，网络神经科学最初是作为图论（来自数学）和神经科学（来自生物学）的正式整合而出现的。这种早期的整合在描述神经单元的结构和功能的相互连接性质方面具有显著的作用，并强调了这种连接对于认知和行为的相关性。但自成立以来，该领域在方法论组成方面并非一成不变。相反，它已经发展到使用越来越先进的图论工具，并引入了其他几个学科视角，包括机器学习和系统工程，这些都被证明是互补的。通过这种方式，该学科可处理的问题空间明显扩大了。在这篇综述中，我们讨论了网络神经科学的三种不同类型的研究：（i）描述性网络神经科学，（ii）预测性网络神经科学，以及（iii）基于网络控制理论的最新进展的摄动网络神经科学。在考虑每个领域时，我们简要总结了这些方法，讨论了所获得的见解的性质，并强调了未来的方向。
