Over one third of the estimated 3 million people with epilepsy in the United States are medication resistant. Responsive neurostimulation from chronically implanted electrodes provides a promising treatment alternative to resective surgery. However, determining optimal personalized stimulation parameters, including when and where to intervene to guarantee a positive patient outcome, is a major open challenge. Network neuroscience and control theory offer useful tools that may guide improvements in parameter selection for control of anomalous neural activity. Here we use a method to characterize dynamic controllability across consecutive effective connectivity (EC) networks based on regularized partial correlations between implanted electrodes during the onset, propagation, and termination regimes of 34 seizures. We estimate regularized partial correlation adjacency matrices from 1-s time windows of intracranial electrocorticography recordings using the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO). Average and modal controllability metrics calculated from each resulting EC network track the time-varying controllability of the brain on an evolving landscape of conditionally dependent network interactions. We show that average controllability increases throughout a seizure and is negatively correlated with modal controllability throughout. Our results support the hypothesis that the energy required to drive the brain to a seizure-free state from an ictal state is smallest during seizure onset, yet we find that applying control energy at electrodes in the seizure onset zone may not always be energetically favorable. Our work suggests that a low-complexity model of time-evolving controllability may offer insights for developing and improving control strategies targeting seizure suppression.

美国估计有 300 万癫痫患者，其中超过三分之一对药物产生耐药性。长期植入电极的响应性神经刺激为切除手术提供了一种有前景的治疗替代方案。然而，确定最佳的个性化刺激参数，包括何时何地进行干预以保证积极的患者结果，是一个重大的开放挑战。网络神经科学和控制理论提供了有用的工具，可以指导改进控制异常神经活动的参数选择。在这里，我们使用一种方法来表征连续有效连接 (EC) 网络的动态可控性，该方法基于 34 次癫痫发作的发作、传播和终止状态期间植入电极之间的正则化部分相关性。我们使用图形最小绝对收缩和选择算子 (GLASSO) 从颅内皮层电图记录的 1 秒时间窗估计正则化偏相关邻接矩阵。根据每个结果 EC 网络计算出的平均和模态可控性指标，跟踪大脑在条件依赖网络交互不断变化的情况下的时变可控性。我们发现，平均可控性在整个癫痫发作过程中都会增加，并且与整个过程中的模态可控性呈负相关。我们的结果支持这样的假设：在癫痫发作期间，将大脑从发作状态驱动到无癫痫发作状态所需的能量是最小的，但我们发现，在癫痫发作区域的电极上应用控制能量可能并不总是在能量上有利。 我们的工作表明，时间演化可控性的低复杂性模型可以为开发和改进针对癫痫发作抑制的控制策略提供见解。