Closed-loop deep brain stimulation (DBS) paradigm is gaining tremendous favor due to its potential capability of further and more efficient improvements in neurological diseases. Preclinical validation of closed-loop controller is quite necessary in order to minimize injury risks of clinical trials to patients, which can greatly benefit from real-time computational models and thus potentially reduce research and development costs and time. Here we developed an embedded multi-core real-time simulation platform (EMC-RTP) for a biological-faithful computational network model of basal ganglia (BG). The single neuron model is implemented in a highly real-time manner using a reasonable simplification. A modular mapping architecture with hierarchical routing organization was constructed to mimic the pathological neural activities of BG observed in parkinsonian conditions. A closed-loop simulation testbed for DBS validation was then set up using a host computer as the DBS controller. The availability of EMC-RTP and the testbed system was validated by comparing the performance of open-loop and proportional-integral (PI) controllers. Our experimental results showed that the proposed EMC-RTP reproduces abnormal beta bursts of BG in parkinsonian conditions while meets requirements of both real-time and computational accuracy as well. Closed-loop DBS experiments using the EMC-RTP suggested that the platform could perform reasonable output under different kinds of DBS strategies, indicating the usability of the platform.

中文翻译：

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用于深部脑刺激的基底神经节嵌入式多核实时仿真平台


闭环深部脑刺激（DBS）范式因其进一步、更有效地改善神经系统疾病的潜在能力而受到极大青睐。为了最大限度地减少临床试验对患者造成的伤害风险，闭环控制器的临床前验证是非常必要的，这可以极大地受益于实时计算模型，从而潜在地减少研发成本和时间。在这里，我们开发了一个嵌入式多核实时仿真平台（EMC-RTP），用于基底神经节（BG）的生物忠实计算网络模型。单神经元模型通过合理的简化以高度实时的方式实现。构建了具有分层路由组织的模块化映射架构来模拟帕金森病条件下观察到的 BG 病理神经活动。然后使用主机作为 DBS 控制器建立了用于 DBS 验证的闭环仿真测试台。通过比较开环和比例积分 (PI) 控制器的性能，验证了 EMC-RTP 和测试台系统的可用性。我们的实验结果表明，所提出的 EMC-RTP 在帕金森病条件下重现了 BG 的异常 beta 爆发，同时满足实时性和计算精度的要求。使用EMC-RTP进行的闭环DBS实验表明，该平台在不同类型的DBS策略下都能得到合理的输出，说明了该平台的可用性。