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本文引用的文献

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Evidence-based modeling of network discharge dynamics during periodic pacing to control epileptiform activity.基于证据的周期性起搏网络放电动力学模型控制癫痫样活动。
J Neurosci Methods. 2012 Mar 15;204(2):318-25. doi: 10.1016/j.jneumeth.2011.11.029. Epub 2011 Dec 8.
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In vitro ictogenesis and parahippocampal networks in a rodent model of temporal lobe epilepsy.在颞叶癫痫啮齿动物模型中的体外癫痫发生和海马旁网络。
Neurobiol Dis. 2010 Sep;39(3):372-80. doi: 10.1016/j.nbd.2010.05.003. Epub 2010 May 7.
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Treating epilepsy via adaptive neurostimulation: a reinforcement learning approach.通过自适应神经刺激治疗癫痫:一种强化学习方法。
Int J Neural Syst. 2009 Aug;19(4):227-40. doi: 10.1142/S0129065709001987.
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Deep brain stimulation for the treatment of epilepsy.深部脑刺激治疗癫痫。
Int J Neural Syst. 2009 Jun;19(3):213-26. doi: 10.1142/S0129065709001975.
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Deep brain stimulation for medically refractory epilepsy.深部脑刺激治疗药物难治性癫痫。
Neurosurg Focus. 2008 Sep;25(3):E11. doi: 10.3171/FOC/2008/25/9/E11.
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Deep brain stimulation in patients with refractory temporal lobe epilepsy.难治性颞叶癫痫患者的脑深部电刺激治疗
Epilepsia. 2007 Aug;48(8):1551-60. doi: 10.1111/j.1528-1167.2007.01005.x.
7
Cellular mechanisms underlying antiepileptic effects of low- and high-frequency electrical stimulation in acute epilepsy in neocortical brain slices in vitro.体外新皮质脑片急性癫痫模型中低频和高频电刺激抗癫痫作用的细胞机制
J Neurophysiol. 2007 Mar;97(3):1887-902. doi: 10.1152/jn.00514.2006. Epub 2006 Dec 6.
8
Implantation of a closed-loop stimulation in the management of medically refractory focal epilepsy: a technical note.闭环刺激植入术治疗药物难治性局灶性癫痫:技术说明
Stereotact Funct Neurosurg. 2005;83(4):153-8. doi: 10.1159/000088656. Epub 2005 Oct 3.
9
Repetitive low-frequency stimulation reduces epileptiform synchronization in limbic neuronal networks.重复低频刺激可降低边缘神经元网络中的癫痫样同步化。
Neurobiol Dis. 2005 Jun-Jul;19(1-2):119-28. doi: 10.1016/j.nbd.2004.11.012.
10
Automated seizure abatement in humans using electrical stimulation.利用电刺激实现人类癫痫发作的自动缓解。
Ann Neurol. 2005 Feb;57(2):258-68. doi: 10.1002/ana.20377.

在边缘性癫痫发作的体外模型中对癫痫样兴奋性进行自适应控制。

Adaptive control of epileptiform excitability in an in vitro model of limbic seizures.

机构信息

Montreal Neurological Institute, Department of Neurology & Neurosurgery, McGill University, 3801 University Street, Montreal, QC, Canada H3A 2B4.

出版信息

Exp Neurol. 2013 Mar;241:179-83. doi: 10.1016/j.expneurol.2013.01.002. Epub 2013 Jan 9.

DOI:10.1016/j.expneurol.2013.01.002
PMID:23313899
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4891193/
Abstract

Deep brain stimulation (DBS) is a promising tool for treating drug-resistant epileptic patients. Currently, the most common approach is fixed-frequency stimulation (periodic pacing) by means of stimulating devices that operate under open-loop control. However, a drawback of this DBS strategy is the impossibility of tailoring a personalized treatment, which also limits the optimization of the stimulating apparatus. Here, we propose a novel DBS methodology based on a closed-loop control strategy, developed by exploiting statistical machine learning techniques, in which stimulation parameters are adapted to the current neural activity thus allowing for seizure suppression that is fine-tuned on the individual scale (adaptive stimulation). By means of field potential recording from adult rat hippocampus-entorhinal cortex (EC) slices treated with the convulsant drug 4-aminopyridine we determined the effectiveness of this approach compared to low-frequency periodic pacing, and found that the closed-loop stimulation strategy: (i) has similar efficacy as low-frequency periodic pacing in suppressing ictal-like events but (ii) is more efficient than periodic pacing in that it requires less electrical pulses. We also provide evidence that the closed-loop stimulation strategy can alternatively be employed to tune the frequency of a periodic pacing strategy. Our findings indicate that the adaptive stimulation strategy may represent a novel, promising approach to DBS for individually-tailored epilepsy treatment.

摘要

深部脑刺激(DBS)是治疗耐药性癫痫患者的一种很有前途的工具。目前,最常见的方法是通过使用开环控制的刺激设备进行固定频率刺激(周期性起搏)。然而,这种 DBS 策略的一个缺点是不可能进行个性化治疗,这也限制了刺激设备的优化。在这里,我们提出了一种基于闭环控制策略的新型 DBS 方法,该方法利用统计机器学习技术开发,其中刺激参数适应当前的神经活动,从而允许在个体尺度上进行精细调整的癫痫抑制(自适应刺激)。通过记录用致惊厥药物 4-氨基吡啶处理的成年大鼠海马-内嗅皮层(EC)切片中的场电位,我们确定了与低频周期性起搏相比,这种方法的有效性,并发现闭环刺激策略:(i)在抑制癫痫样事件方面与低频周期性起搏具有相似的疗效,但(ii)比周期性起搏更有效,因为它需要的电脉冲更少。我们还提供了证据表明,闭环刺激策略可以替代周期性起搏策略来调整其频率。我们的研究结果表明,自适应刺激策略可能代表一种新的、有前途的 DBS 方法,用于针对个体的癫痫治疗。