Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom.
J Neural Eng. 2021 Apr 6;18(4):046023. doi: 10.1088/1741-2552/abd90d.
Deep brain stimulation is a treatment for medically refractory essential tremor. To improve the therapy, closed-loop approaches are designed to deliver stimulation according to the system's state, which is constantly monitored by recording a pathological signal associated with symptoms (e.g. brain signal or limb tremor). Since the space of possible closed-loop stimulation strategies is vast and cannot be fully explored experimentally, how to stimulate according to the state should be informed by modeling. A typical modeling goal is to design a stimulation strategy that aims to maximally reduce the Hilbert amplitude of the pathological signal in order to minimize symptoms. Isostables provide a notion of amplitude related to convergence time to the attractor, which can be beneficial in model-based control problems. However, how isostable and Hilbert amplitudes compare when optimizing the amplitude response to stimulation in models constrained by data is unknown.
We formulate a simple closed-loop stimulation strategy based on models previously fitted to phase-locked deep brain stimulation data from essential tremor patients. We compare the performance of this strategy in suppressing oscillatory power when based on Hilbert amplitude and when based on isostable amplitude. We also compare performance to phase-locked stimulation and open-loop high-frequency stimulation.
For our closed-loop phase space stimulation strategy, stimulation based on isostable amplitude is significantly more effective than stimulation based on Hilbert amplitude when amplitude field computation time is limited to minutes. Performance is similar when there are no constraints, however constraints on computation time are expected in clinical applications. Even when computation time is limited to minutes, closed-loop phase space stimulation based on isostable amplitude is advantageous compared to phase-locked stimulation, and is more efficient than high-frequency stimulation.
Our results suggest a potential benefit to using isostable amplitude more broadly for model-based optimization of stimulation in neurological disorders.
深部脑刺激是治疗药物难治性原发性震颤的一种方法。为了改善治疗效果，设计了闭环方法，根据系统状态提供刺激，系统状态通过记录与症状相关的病理信号（例如脑信号或肢体震颤）进行连续监测。由于可能的闭环刺激策略空间非常广阔，无法通过实验完全探索，因此应该通过建模来告知如何根据状态进行刺激。典型的建模目标是设计一种刺激策略，旨在最大限度地降低病理信号的希尔伯特幅度，从而最小化症状。等度稳定提供了与吸引子收敛时间相关的幅度概念，这在基于模型的控制问题中可能是有益的。然而，在受数据约束的模型中，优化对刺激的幅度响应时，等度稳定幅度和希尔伯特幅度如何比较是未知的。
我们基于以前为原发性震颤患者的锁相深部脑刺激数据拟合的模型，制定了一种简单的闭环刺激策略。我们比较了基于希尔伯特幅度和基于等度稳定幅度的策略在抑制振荡功率方面的性能。我们还将性能与锁相刺激和开环高频刺激进行了比较。
对于我们的闭环相空间刺激策略，当幅度场计算时间限制在分钟内时，基于等度稳定幅度的刺激比基于希尔伯特幅度的刺激更有效。当没有约束时，性能是相似的，但是在临床应用中预计会有计算时间的约束。即使计算时间限制在分钟内，基于等度稳定幅度的闭环相空间刺激与锁相刺激相比也具有优势，并且比高频刺激更有效。
我们的结果表明，在基于模型的神经障碍刺激优化中更广泛地使用等度稳定幅度可能具有潜在的益处。
