AI 中文总结
该研究提出基于持续拓扑环的驱动节点选择标准，与基于度的标准控制能相近但可控子空间几何更优，能捕捉标量能量遗漏的脑网络控制信息。
AI 中文摘要
应用于结构连接组的网络控制理论通常根据脑区的结构连接强度将其列为候选驱动节点，并通过标量控制能评估性能。我们检验这种框架是否捕捉到了与驱动节点选择如何塑造脑网络控制相关的最关键信息。我们引入一种基于每个节点参与的持续拓扑环的替代标准——这是一种介观整合的度量，能捕捉超出局部连接的特征，并在三种划分尺度的70个人类结构连接组上将其与基于标准度的选择进行比较。基于拓扑和度的驱动集实现了几乎相同的标量控制能，差异约为0.2%。然而，可控子空间的几何结构存在显著差异：基于拓扑的集将可控性分布在状态空间的更多维度上，并产生条件更好的可控性矩阵。当移除高度枢纽节点时，这种几何优势得以保留，且具有功能特征：由于两种标准将驱动节点置于不同的皮层区域，每种标准能最有效地到达不同类别的目标状态。因此，即使平均控制成本不变，节点排序标准的选择也会影响哪些脑状态转换在能量上更受青睐。结果揭示了控制成本与控制几何之间的分离，并证明持续拓扑捕捉到了标量能量摘要遗漏的脑网络控制信息。
英文摘要
Network control theory applied to structural connectomes typically ranks brain regions as candidate driver nodes by their structural connectivity strength, and evaluates performance through scalar control energy. We test whether this framing captures the most relevant information about how driver-node selection shapes brain network control. We introduce an alternative criterion based on the persistent topological cycles in which each node participates---a measure of mesoscale integration that captures features beyond local connectivity---and compare it to standard degree-based selection across 70 human structural connectomes at three parcellation scales. Topology- and degree-informed driver sets achieve nearly identical scalar control energy, differing by approximately 0.2%. The geometry of the controllable subspace, however, differs substantially: topology-informed sets distribute controllability across more dimensions of state space and produce better-conditioned controllability matrices. This geometric advantage is preserved when high-degree hub nodes are removed, and it carries a functional signature: because the two criteria place driver nodes in different cortical territory, each most efficiently reaches a different class of target state. The choice of node-ranking criterion therefore shapes which brain-state transitions are energetically favored even when average control cost is unchanged. The results reveal a dissociation between control cost and control geometry, and demonstrate that persistent topology captures information about brain network control that scalar energy summaries miss.
Comments21 pages, 7 figures
