Optimal deep brain stimulation of the subthalamic nucleus - a computational study
Optimal deep brain stimulation of the subthalamic nucleus - a computational study
复制标题
DOI：
10.1007/s10827-007-0031-0
复制
发表时间：
2007-12-01
影响因子：
1.2
通讯作者：
Rabitz, Herschel
中科院分区：
文献类型：
--
作者：
Feng, Xiao-Jiang;Shea-Brown, Eric;Rabitz, Herschel
丘脑底核深部脑刺激(DBS)是治疗帕金森氏病(PD)运动症状的有效方法。在这里，我们使用基于生物物理学的基底神经节尖峰细胞模型(Terman等人，Journal of NeuroScience，22,2963-2976,2002；Rubin和Terman，Journal of Computive NeuroScience，16,211-235,2004)来提供计算证据，证明DBS输入的替代时间模式可能与标准高频波形一样有效，但需要更低的幅度。在这个模型中，星展银行的业绩评估有两种方式。首先，我们确定DBS在多大程度上导致GPI(内侧苍白球)突触输出停止其对模拟丘脑皮质细胞的病理调节。其次，我们评估了DBS如何影响GPI细胞的自相关图和交叉相关图。在这两种情况下，非线性闭环学习算法识别被优化为具有最小强度的有效DBS输入。由此产生的网络动力学不同于一些先前研究与常规高频DBS相关联的规则的、夹带的激发。这种类型的优化解决方案在固有的网络动态和在不同小区接收的DBS输入的强度两者中也具有异质性。在无模型学习算法的指导下，在实验或最终的临床环境中，这种可选的DBS输入可能被识别出来。
Deep brain stimulation (DBS) of the subthalamic nucleus, typically with periodic, high frequency pulse trains, has proven to be an effective treatment for the motor symptoms of Parkinson's disease (PD). Here, we use a biophysically-based model of spiking cells in the basal ganglia (Terman et al., Journal of Neuroscience, 22, 2963-2976, 2002; Rubin and Terman, Journal of Computational Neuroscience, 16, 211-235, 2004) to provide computational evidence that alternative temporal patterns of DBS inputs might be equally effective as the standard high-frequency waveforms, but require lower amplitudes. Within this model, DBS performance is assessed in two ways. First, we determine the extent to which DBS causes Gpi (globus pallidus pars interna) synaptic outputs, which are burstlike and synchronized in the unstimulated Parkinsonian state, to cease their pathological modulation of simulated thalamocortical cells. Second, we evaluate how DBS affects the GPi cells' auto- and cross-correlograms. In both cases, a nonlinear closed-loop learning algorithm identifies effective DBS inputs that are optimized to have minimal strength. The network dynamics that result differ from the regular, entrained firing which some previous studies have associated with conventional high-frequency DBS. This type of optimized solution is also found with heterogeneity in both the intrinsic network dynamics and the strength of DBS inputs received at various cells. Such alternative DBS inputs could potentially be identified, guided by the model-free learning algorithm, in experimental or eventual clinical settings.