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使用霍奇金-赫胥黎模型估计近矩形刺激的运动阈值。

Estimation of the Motor Threshold for Near-Rectangular Stimuli Using the Hodgkin-Huxley Model.

作者信息

Memarian Sorkhabi Majid, Wendt Karen, Wilson Marcus T, Denison Timothy

机构信息

MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, Oxford OX1 3TH, UK.

Te Aka Mātuatua-School of Science, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand.

出版信息

Comput Intell Neurosci. 2021 May 31;2021:4716161. doi: 10.1155/2021/4716161. eCollection 2021.

Abstract

The motor threshold measurement is a standard in preintervention probing in TMS experiments. We aim to predict the motor threshold for near-rectangular stimuli to efficiently determine the motor threshold size before any experiments take place. Estimating the behavior of large-scale networks requires dynamically accurate and efficient modeling. We utilized a Hodgkin-Huxley (HH) type model to evaluate motor threshold values and computationally validated its function with known true threshold data from 50 participants trials from state-of-the-art published datasets. For monophasic, bidirectional, and unidirectional rectangular stimuli in posterior-anterior or anterior-posterior directions as generated by the cTMS device, computational modeling of the HH model captured the experimentally measured population-averaged motor threshold values at high precision (maximum error ≤ 8%). The convergence of our biophysically based modeling study with experimental data in humans reveals that the effect of the stimulus shape is strongly correlated with the activation kinetics of the voltage-gated ion channels. The proposed method can reliably predict motor threshold size using the conductance-based neuronal models and could therefore be embedded in new generation neurostimulators. Advancements in neural modeling will make it possible to enhance treatment procedures by reducing the number of delivered magnetic stimuli to participants.

摘要

运动阈值测量是经颅磁刺激(TMS)实验干预前探测的一项标准。我们旨在预测近矩形刺激的运动阈值,以便在任何实验进行之前有效地确定运动阈值大小。估计大规模网络的行为需要动态准确且高效的建模。我们利用霍奇金 - 赫胥黎(HH)类型模型来评估运动阈值,并使用来自最先进的已发表数据集的50名参与者试验的已知真实阈值数据对其功能进行了计算验证。对于由cTMS设备产生的后 - 前或前 - 后方向的单相、双向和单向矩形刺激,HH模型的计算建模以高精度捕获了实验测量的群体平均运动阈值(最大误差≤8%)。我们基于生物物理学的建模研究与人类实验数据的一致性表明,刺激形状的影响与电压门控离子通道的激活动力学密切相关。所提出的方法可以使用基于电导的神经元模型可靠地预测运动阈值大小,因此可以嵌入新一代神经刺激器中。神经建模的进步将有可能通过减少向参与者施加的磁刺激数量来改进治疗程序。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/996c/8184325/532aa89a7280/CIN2021-4716161.001.jpg

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