Modeling shifts in the rate and pattern of subthalamopallidal network activity during deep brain stimulation.
Modeling shifts in the rate and pattern of subthalamopallidal network activity during deep brain stimulation.
复制标题
DOI：
10.1007/s10827-010-0225-8
复制
发表时间：
2010-06
影响因子：
1.2
通讯作者：
McIntyre, Cameron C.
中科院分区：
文献类型：
--
作者：
Hahn, Philip J.;McIntyre, Cameron C.
丘脑板下核(STN)深部脑刺激(DBS)是治疗难治性帕金森病的有效方法；然而，对其对基底神经节网络活动的影响的了解仍然有限。我们构建了丘脑底核网络的计算模型，对其进行训练以适应帕金森病猴子的活体记录，并评估其对STN DBS的反应。该网络模型是由STN和苍白球神经元的突触连接的单室生物物理模型创建的，并由皮质β节律驱动的随机定义的输入。开发了一种最小均方误差训练算法来参数化网络连接，并在与帕金森条件下的实验尖峰和突发率进行比较时将误差最小化。然后将训练网络的输出与训练过程中未使用的实验数据进行比较。我们发现，减少皮质β输入对模型的影响产生的活动与正常猴子的记录很好地吻合。此外，在帕金森症条件下的STN DBS期间，模拟再现了现有实验数据中发现的GPI突发的减少。该模型还提供了极大地扩展对GPI爆发活动的分析的机会，产生了三个主要预测。首先，它的减少与DBS激活的STN的体积成正比。其次，GPI猝发以刺激频率依赖的方式减少，在与临床治疗DBS一致的值饱和。第三，消融STN神经元，据报道产生了与STN DBS相似的治疗结果，也减少了GPI的爆发。我们对刺激诱导的网络活动的理论分析表明，GPI放电的规律性依赖于激活的STN组织的体积，可能需要一个阈值水平的猝发减少才能达到治疗效果。
Deep brain stimulation (DBS) of the subthlamic nucleus (STN) represents an effective treatment for medically refractory Parkinson’s disease; however, understanding of its effects on basal ganglia network activity remains limited. We constructed a computational model of the subthalamopallidal network, trained it to fit in vivo recordings from parkinsonian monkeys, and evaluated its response to STN DBS. The network model was created with synaptically connected single compartment biophysical models of STN and pallidal neurons, and stochastically defined inputs driven by cortical beta rhythms. A least mean square error training algorithm was developed to parameterize network connections and minimize error when compared to experimental spike and burst rates in the parkinsonian condition. The output of the trained network was then compared to experimental data not used in the training process. We found that reducing the influence of the cortical beta input on the model generated activity that agreed well with recordings from normal monkeys. Further, during STN DBS in the parkinsonian condition the simulations reproduced the reduction in GPi bursting found in existing experimental data. The model also provided the opportunity to greatly expand analysis of GPi bursting activity, generating three major predictions. First, its reduction was proportional to the volume of STN activated by DBS. Second, GPi bursting decreased in a stimulation frequency dependent manner, saturating at values consistent with clinically therapeutic DBS. And third, ablating STN neurons, reported to generate similar therapeutic outcomes as STN DBS, also reduced GPi bursting. Our theoretical analysis of stimulation induced network activity suggests that regularization of GPi firing is dependent on the volume of STN tissue activated and a threshold level of burst reduction may be necessary for therapeutic effect.
登录
查看更多内容
影响因子：
2.5
作者：
Dejean, Cyril;Gross, Christian E.;Boraud, Thomas
通讯作者：
Boraud, Thomas
影响因子：
1.7
作者：
Birdno, Merrill J.;Kuncel, Alexis M.;Grill, Warren M.
通讯作者：
Grill, Warren M.
影响因子：
5.3
作者：
Brown, P;Oliviero, A;Di Lazzaro, V
通讯作者：
Di Lazzaro, V
影响因子：
2.5
作者：
BERGMAN, H;WICHMANN, T;DELONG, MR
通讯作者：
DELONG, MR
影响因子：
5.3
作者：
Leblois, A;Boraud, T;Hansel, D
通讯作者：
Hansel, D