Popova Mariia, Messé Arnaud, Gulberti Alessandro, Gerloff Christian, Pötter-Nerger Monika, Hilgetag Claus C
Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany.
Department of Neurology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany.
Netw Neurosci. 2024 Oct 1;8(3):926-945. doi: 10.1162/netn_a_00376. eCollection 2024.
Current treatments of Parkinson's disease (PD) have limited efficacy in alleviating freezing of gait (FoG). In this context, concomitant deep brain stimulation (DBS) of the subthalamic nucleus (STN) and the substantia nigra pars reticulata (SNr) has been suggested as a potential therapeutic approach. However, the mechanisms underlying this approach are unknown. While the current rationale relies on network-based hypotheses of intensified disinhibition of brainstem locomotor areas to facilitate the release of gait motor programs, it is still unclear how simultaneous high-frequency DBS in two interconnected basal ganglia nuclei affects large-scale cortico-subcortical network activity. Here, we use a basic model of neural excitation, the susceptible-excited-refractory (SER) model, to compare effects of different stimulation modes of the network underlying FoG based on the mouse brain connectivity atlas. We develop a network-based computational framework to compare subcortical DBS targets through exhaustive analysis of the brain attractor dynamics in the healthy, PD, and DBS states. We show that combined STN+SNr DBS outperforms STN DBS in terms of the normalization of spike propagation flow in the FoG network. The framework aims to move toward a mechanistic understanding of the network effects of DBS and may be applicable to further perturbation-based therapies of brain disorders.
目前帕金森病(PD)的治疗方法在缓解步态冻结(FoG)方面疗效有限。在此背景下,有人提出同时对丘脑底核(STN)和黑质网状部(SNr)进行深部脑刺激(DBS)作为一种潜在的治疗方法。然而,这种方法的潜在机制尚不清楚。虽然目前的理论依据依赖于基于网络的假说,即加强对脑干运动区域的去抑制以促进步态运动程序的释放,但目前仍不清楚在两个相互连接的基底神经节核中同时进行高频DBS如何影响大规模皮质-皮质下网络活动。在此,我们使用神经兴奋的基本模型——易感-兴奋-不应期(SER)模型,基于小鼠脑连接图谱比较基于FoG的网络的不同刺激模式的效果。我们开发了一个基于网络的计算框架,通过对健康、PD和DBS状态下的脑吸引子动力学进行详尽分析,来比较皮质下DBS靶点。我们表明,在FoG网络中,联合STN+SNr DBS在使峰电位传播流正常化方面优于STN DBS。该框架旨在朝着对DBS的网络效应进行机制性理解迈进,并且可能适用于进一步基于扰动的脑部疾病治疗。