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Abstract：
Subthalamic deep brain stimulation (DBS) robustly generates high-frequency oscillations known as evoked resonant neural activity (ERNA). Recently the importance of ERNA has been demonstrated through its ability to predict the optimal DBS contact in the subthalamic nucleus in patients with Parkinson\'s disease. However, the underlying mechanisms of ERNA are not well understood, and previous modelling efforts have not managed to reproduce the wealth of published data describing the dynamics of ERNA. Here, we aim to present a minimal model capable of reproducing the characteristics of the slow ERNA dynamics published to date. We make biophysically-motivated modifications to the Kuramoto model and fit its parameters to the slow dynamics of ERNA obtained from data. Our results demonstrate that it is possible to reproduce the slow dynamics of ERNA (over hundreds of seconds) with a single neuronal population, and, crucially, with vesicle depletion as one of the key mechanisms behind the ERNA frequency decay in our model. We further validate the proposed model against experimental data from Parkinson\'s disease patients, where it captures the variations in ERNA frequency and amplitude in response to variable stimulation frequency, amplitude, and to stimulation pulse bursting. We provide a series of predictions from the model that could be the subject of future studies for further validation.
摘要：
丘脑深部脑刺激（DBS）强劲地产生称为诱发共振神经活动（ERNA）的高频振荡。最近，ERNA的重要性已通过其预测帕金森病患者丘脑底核中最佳DBS接触的能力得到证明。然而,ERNA的潜在机制还没有得到很好的理解，以前的建模工作未能重现描述ERNA动态的大量已发布数据。这里,我们的目标是提出一个最小模型，能够再现迄今为止发布的缓慢ERNA动力学的特征。我们对Kuramoto模型进行了生物物理上的修改，并将其参数拟合到从数据中获得的ERNA的缓慢动力学。我们的结果表明，有可能用单个神经元群体重现ERNA的缓慢动力学（在数百秒内），and,至关重要的是,在我们的模型中，囊泡耗尽是ERNA频率衰减背后的关键机制之一。我们针对帕金森病患者的实验数据进一步验证了所提出的模型，它捕获ERNA频率和振幅的变化，以响应可变的刺激频率，振幅,刺激脉冲爆发。我们从模型中提供了一系列预测，这些预测可能是未来研究的主题，以便进一步验证。
