• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Computational Neuroscience >Optimal deep brain stimulation of the subthalamic nucleus-a computational study
【24h】

Optimal deep brain stimulation of the subthalamic nucleus-a computational study

机译：丘脑底核的最佳深部脑刺激-计算研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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 Neu-roscience, 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 thala-mocortical cells. Second, we evaluate how DBS affects the GPi cells' auto- and cross-correlograms. In both cases, a nonlinear closed-loop learning algorithm iden-rntifies 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.
机译：丘脑底核的深部脑刺激（DBS）（通常采用周期性的高频脉冲序列）已被证明是治疗帕金森氏病（PD）运动症状的有效方法。在这里，我们使用了基于生物物理学的基底神经节突状细胞模型（Terman等人，Journal of Neu-roscience，22，2963-2976，2002; Rubin and Terman，Journal of Computational Neuroscience，16，211-235 （2004年），以提供计算证据，即DBS输入的替代时间模式可能与标准高频波形同样有效，但需要较低的幅度。在此模型中，可以通过两种方式评估DBS的性能。首先，我们确定DBS导致Gpi（globus pallidus pars interna）突触输出的程度，该突触输出在未受刺激的帕金森氏状态下呈突发状并同步化，以停止其对模拟丘脑-皮层细胞的病理调节。其次，我们评估DBS如何影响GPi细胞的自相关和互相关图。在这两种情况下，非线性闭环学习算法都会识别有效的DBS输入，这些输入经过优化后具有最小的强度。产生的网络动态不同于常规的夹带点火，之前的一些研究已经将其与常规高频DBS相关联。还发现这种类型的优化解决方案在固有的网络动态性和在各个小区接收的DBS输入的强度方面都具有异质性。可以在无模型学习算法的指导下，在实验或最终的临床环境中识别此类替代DBS输入。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号