Department of Psychiatry, University of Oxford, UK; Center for Music in the Brain, Aarhus University, Denmark.
Center of Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Institució Catalana de Recerca i Estudis Avancats (ICREA), Spain.
Neuroimage. 2017 Oct 15;160:84-96. doi: 10.1016/j.neuroimage.2017.03.045. Epub 2017 Mar 23.
Over the last decade, we have observed a revolution in brain structural and functional Connectomics. On one hand, we have an ever-more detailed characterization of the brain's white matter structural connectome. On the other, we have a repertoire of consistent functional networks that form and dissipate over time during rest. Despite the evident spatial similarities between structural and functional connectivity, understanding how different time-evolving functional networks spontaneously emerge from a single structural network requires analyzing the problem from the perspective of complex network dynamics and dynamical system's theory. In that direction, bottom-up computational models are useful tools to test theoretical scenarios and depict the mechanisms at the genesis of resting-state activity. Here, we provide an overview of the different mechanistic scenarios proposed over the last decade via computational models. Importantly, we highlight the need of incorporating additional model constraints considering the properties observed at finer temporal scales with MEG and the dynamical properties of FC in order to refresh the list of candidate scenarios.
在过去的十年中，我们见证了大脑结构和功能连接组学的革命。一方面，我们对大脑白质结构连接组学的特征有了更详细的描述。另一方面，我们有一系列一致的功能网络，它们在休息时随着时间的推移而形成和消散。尽管结构连接和功能连接之间存在明显的空间相似性，但要理解不同的时变功能网络如何自发地从单个结构网络中出现，需要从复杂网络动力学和动力系统理论的角度来分析这个问题。在这方面，自下而上的计算模型是测试理论场景和描述静息态活动起源机制的有用工具。在这里，我们提供了过去十年通过计算模型提出的不同机制场景的概述。重要的是，我们强调需要结合额外的模型约束，考虑到 MEG 观察到的更精细时间尺度上的特性和 FC 的动力学特性，以更新候选场景列表。
