Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA.
J Math Biol. 2020 Jul;81(1):25-64. doi: 10.1007/s00285-020-01501-1. Epub 2020 May 16.
Deep brain stimulation (DBS) is an increasingly used medical treatment for various neurological disorders. While its mechanisms are not fully understood, experimental evidence suggests that through application of periodic electrical stimulation DBS may act to desynchronize pathologically synchronized populations of neurons resulting desirable changes to a larger brain circuit. However, the underlying mathematical mechanisms by which periodic stimulation can engender desynchronization in a coupled population of neurons is not well understood. In this work, a reduced phase-amplitude reduction framework is used to characterize the desynchronizing influence of periodic stimulation on a population of coupled oscillators. Subsequently, optimal control theory allows for the design of periodic, open-loop stimuli with the capacity to destabilize completely synchronized solutions while simultaneously stabilizing rotating block solutions. This framework exploits system nonlinearities in order to strategically modify unstable Floquet exponents. In the limit of weak neural coupling, it is shown that this method only requires information about the phase response curves of the individual neurons. The effects of noise and heterogeneity are also considered and numerical results are presented. This framework could ultimately be used to inform the design of more efficient deep brain stimulation waveforms for the treatment of neurological disease.
脑深部电刺激(DBS)是一种越来越被用于治疗各种神经疾病的医学治疗方法。虽然其机制尚未完全理解,但实验证据表明,通过周期性电刺激的应用,DBS 可以作用于去同步病理性同步的神经元群体,从而导致对更大的大脑回路产生理想的变化。然而,周期性刺激如何在耦合神经元群体中产生去同步的基本数学机制尚不清楚。在这项工作中,使用简化的相位-幅度降低框架来描述周期性刺激对耦合振荡器群体的去同步影响。随后,最优控制理论允许设计具有完全破坏同步解的能力的周期性开环刺激,同时稳定旋转块解。该框架利用系统的非线性来有策略地修改不稳定的 Floquet 指数。在弱神经耦合的极限下,表明该方法仅需要有关单个神经元相位响应曲线的信息。还考虑了噪声和异质性的影响,并给出了数值结果。该框架最终可用于为治疗神经疾病的深部脑刺激波形的设计提供信息,以提高效率。