Controlling the human connectome with spatially diffuse input signals.
Controlling the human connectome with spatially diffuse input signals.
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用空间扩散的输入信号控制人体连接组。
DOI：
10.1101/2024.02.27.581006
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Parkes,Linden
中科院分区：
文献类型：
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作者：
Betzel,Richard;Puxeddu,MariaGrazia;Seguin,Caio;Bazinet,Vincent;Luppi,Andrea;Podschun,Alina;Singleton,SParker;Faskowitz,Joshua;Parakkattu,Vibin;Misic,Bratislav;Markett,Sebastian;Kuceyeski,Amy;Parkes,Linden
人类的大脑永远不会“休息”；它的活动随着时间的推移不断波动，从一种大脑状态（一种全脑活动模式）转变为另一种状态。网络控制理论提供了一个框架来理解与这些转变相关的努力（能量）。在这种情况下特别有用的控制理论的一个分支是“最优控制”，其中输入信号用于选择性地将大脑驱动到目标状态。通常，这些输入被独立地引入到网络的节点（每个输入信号恰好与一个节点相关联）。虽然很方便，但这种输入策略忽略了大脑皮层的连续性——从几何角度来看，每个区域都与其空间相邻区域相连，从而允许外源和内源的控制信号从其焦点传播到附近区域。此外，脑刺激技术的空间特异性是有限的，因此可以在刺激部位周围的组织中测量扰动的影响。在这里，我们调整网络控制模型，使输入信号的空间范围从输入站点开始呈指数衰减。我们表明，这种更现实的策略利用结构连接和活动的空间依赖性来减少与大脑状态转换相关的能量（努力）。我们进一步利用这些依赖性来探索接近最优的控制策略，以便在每次转换的基础上，给定控制任务所需的输入信号数量减少，在某些情况下减少两个数量级。这种近似产生了输入位点密度的网络范围图，我们将其与现有的功能、代谢、遗传和神经化学图谱数据库进行比较，发现了密切的对应关系。最终，我们不仅提出了一个更有效的框架，也更符合既定的大脑组织原则，而且我们还为最佳控制奠定了神经生物学基础。
The human brain is never at “rest”; its activity is constantly fluctuating over time, transitioning from one brain state–a whole-brain pattern of activity–to another. Network control theory offers a framework for understanding the effort – energy – associated with these transitions. One branch of control theory that is especially useful in this context is “optimal control”, in which input signals are used to selectively drive the brain into a target state. Typically, these inputs are introduced independently to the nodes of the network (each input signal is associated with exactly one node). Though convenient, this input strategy ignores the continuity of cerebral cortex – geometrically, each region is connected to its spatial neighbors, allowing control signals, both exogenous and endogenous, to spread from their foci to nearby regions. Additionally, the spatial specificity of brain stimulation techniques is limited, such that the effects of a perturbation are measurable in tissue surrounding the stimulation site. Here, we adapt the network control model so that input signals have a spatial extent that decays exponentially from the input site. We show that this more realistic strategy takes advantage of spatial dependencies in structural connectivity and activity to reduce the energy (effort) associated with brain state transitions. We further leverage these dependencies to explore near-optimal control strategies such that, on a per-transition basis, the number of input signals required for a given control task is reduced, in some cases by two orders of magnitude. This approximation yields network-wide maps of input site density, which we compare to an existing database of functional, metabolic, genetic, and neurochemical maps, finding a close correspondence. Ultimately, not only do we propose a more efficient framework that is also more adherent to well-established brain organizational principles, but we also posit neurobiologically grounded bases for optimal control.