Predicting Wide-Dynamic Range Neuron Activity from Peripheral Nerve Stimulation using Linear Parameter Varying Models.
Predicting Wide-Dynamic Range Neuron Activity from Peripheral Nerve Stimulation using Linear Parameter Varying Models.
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使用线性参数变化模型，从周围神经刺激中预测宽动力范围的神经元活性。
DOI：
10.1109/embc46164.2021.9630368
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发表时间：
2021-11
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慢性疼痛的神经调节治疗是在对神经纤维的电刺激如何影响脊髓和大脑中疼痛处理神经元的动态反应的有限了解的情况下进行的。通过用易于处理的表示法模拟这些效应，我们可能能够提高刺激疗法的疗效。然而，脊髓背角的痛传递神经元是神经系统中的第一个疼痛中继站，对周围神经刺激(PNS)具有复杂的反应，具有非线性和历史效应。宽动态范围(WDR)神经元在疼痛模型中得到了很好的研究，并对周围伤害性和非伤害性刺激做出反应。我们建议使用线性参数变化(LPV)模型来捕捉脊髓背角深层WDR神经元的PNS反应。在这里，我们表明，LPV模型比单一的线性时不变(LTI)模型更好地描述了WDR神经元对PNS电流幅值变化较大的反应。在未来，我们可以使用这些模型和LPV控制技术来设计可能实现最佳疼痛治疗目标的闭环PNS刺激。
Neuromodulation treatments for chronic pain are programmed with limited knowledge of how electrical stimulation of nerve fibers affects the dynamic response of pain-processing neurons in the spinal cord and the brain. By modeling these effects with tractable representations, we may be able to improve efficacy of stimulation therapy. However, pain transmitting neurons in the dorsal horn of the spinal cord, the first pain relay station in the nervous system, have complex responses to peripheral nerve stimulation (PNS) with nonlinearities and history effects. Wide-dynamic range (WDR) neurons are well studied in pain models and respond to peripheral noxious and non-noxious stimuli. We propose to use linear parameter varying (LPV) models to capture PNS responses of WDR neurons of the deep lamina in the dorsal horn in the spinal cord. Here we show that LPV models perform better than a single linear time-invariant (LTI) model in representing the responses of the WDR neurons to widely varying amplitudes of PNS current. In the future, we can use these models alongside LPV control techniques to design closed-loop PNS stimulation that may accomplish optimal pain treatment goals.