Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Neuroimage. 2018 Feb 1;166:385-399. doi: 10.1016/j.neuroimage.2017.11.015. Epub 2017 Nov 11.
The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network whose topology evolves with time. Despite the nontrivial dynamics that are germane to this networked system, experimental evidence demonstrates that functional interactions organize into putative brain systems that facilitate different facets of cognitive computation. We hypothesize that such dynamic functional networks are organized around a set of rules that constrain their spatial architecture - which brain regions may functionally interact - and their temporal architecture - how these interactions fluctuate over time. To objectively uncover these organizing principles, we apply an unsupervised machine learning approach called non-negative matrix factorization to time-evolving, resting state functional networks in 20 healthy subjects. This machine learning approach automatically groups temporally co-varying functional interactions into subgraphs that represent putative topological modes of dynamic functional architecture. We find that subgraphs are stratified based on both the underlying modular organization and the topographical distance of their strongest interactions: while many subgraphs are largely contained within modules, others span between modules and are expressed differently over time. The relationship between dynamic subgraphs and modular architecture is further highlighted by the ability of time-varying subgraph expression to explain inter-individual differences in module reorganization. Collectively, these results point to the critical role that subgraphs play in constraining the topography and topology of functional brain networks. More broadly, this machine learning approach opens a new door for understanding the architecture of dynamic functional networks during both task and rest states, and for probing alterations of that architecture in disease.
人脑处于不断变化的状态，不同的区域之间会进行短暂的交流，以支持基本行为和复杂认知。皮质和皮质下区域之间的相互作用集合形成了一个功能大脑网络，其拓扑结构随时间演变。尽管这个网络系统具有复杂的动态特性，但实验证据表明，功能相互作用组织成了假设的大脑系统，这些系统促进了认知计算的不同方面。我们假设，这种动态功能网络是围绕一组规则组织的，这些规则限制了它们的空间结构（即哪些脑区可能具有功能相互作用）和时间结构（这些相互作用随时间如何波动）。为了客观地揭示这些组织原则，我们应用了一种称为非负矩阵分解的无监督机器学习方法，对 20 名健康受试者的随时间变化的静息状态功能网络进行分析。这种机器学习方法自动将随时间变化的功能相互作用分组到表示动态功能架构拓扑模式的子图中。我们发现，子图是基于其最强相互作用的基础模块组织和拓扑距离来分层的：虽然许多子图主要包含在模块内，但其他子图则跨越模块，并随时间表达不同。动态子图与模块结构之间的关系进一步突出了时变子图表达能力，它可以解释模块重组的个体间差异。总的来说，这些结果表明子图在限制功能大脑网络的拓扑结构和拓扑结构方面起着关键作用。更广泛地说，这种机器学习方法为理解任务和休息状态下的动态功能网络架构以及探测该架构在疾病中的改变打开了一扇新的大门。
