Agent-based modeling of the central amygdala and pain using cell-type specific physiological parameters.
Agent-based modeling of the central amygdala and pain using cell-type specific physiological parameters.
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使用细胞类型特异性生理参数的中央杏仁核和疼痛的基于代理的建模。
DOI：
10.1371/journal.pcbi.1009097
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发表时间：
2021-06
影响因子：
4.3
通讯作者：
Kolber BJ
中科院分区：
文献类型：
--
作者：
Miller Neilan R;Majetic G;Gil-Silva M;Adke AP;Carrasquillo Y;Kolber BJ
杏仁核是一个涉及情绪调节和疼痛的大脑区域。在过去的20年里，许多研究人员研究了杏仁核内的感觉和运动连接，试图了解这种结构在疼痛感知和疼痛控制中的最终作用。许多研究人员一直在使用细胞类型特异性操作来探测杏仁核的潜在回路。随着数据在这一研究领域的积累，我们认识到迫切需要一个单一的框架来整合这些数据并评估紧急的系统级响应。在这篇手稿中，我们提出了一个基于代理的计算模型的两个不同的抑制性神经元群体的杏仁核，那些表达蛋白激酶C δ（PKCδ）和那些表达生长抑素（SOM）。我们利用神经链接网络来模拟神经元之间的连接和抑制信号的传输。描述这些神经元对伤害性刺激的反应的类型特异性参数是从已发表的生理学和免疫学数据以及我们自己的湿实验室实验中估计的。该模型输出一个抽象的疼痛测量值，它是根据杏仁核两个半球神经元的累积亲伤害性和抗伤害性活动来计算的。结果表明，该模型能够产生与已发表的研究一致的疼痛变化，并突出了几个模型参数的重要性。特别是，我们发现每个半球内PKCδ和SOM神经元的相对比例是预测疼痛的关键参数，我们探索了该参数的三个可能值的模型预测。我们将模型对疼痛的预测与我们早期行为研究的数据进行了比较，发现了数据集之间的相似性和差异。特别是这些差异，表明未来可以进行一些湿实验室实验。在这份手稿中，我们提出了一个计算建模的方法来理解和预测疼痛输出的一部分大脑，杏仁核，参与压力适应，情绪调节和疼痛。在过去的几年里，各种研究小组已经开始解剖负责杏仁核激活对疼痛的影响的特定细胞，这可能包括动物模型中疼痛和疼痛样输出的增加和减少。使用计算模型来开发一个框架来理解杏仁核是有帮助和必要的，因为这些湿实验室技术增加了我们对大脑结构的理解的复杂性。这里提出的模型是基于我们最近发表的生理学实验沿着多个表达数据的例子。这个模型可以用来设计未来的湿实验室实验，并可以继续完善，以帮助我们评估杏仁核和类似的边缘系统结构在疼痛和其他疾病中的影响。在这里，我们提出了第一个计算模型杏仁核信号，包括神经元的生理和组织学特性，并允许通过网络的伤害性信号传播的动态模拟。
The amygdala is a brain area involved in emotional regulation and pain. Over the course of the last 20 years, multiple researchers have studied sensory and motor connections within the amygdala in trying to understand the ultimate role of this structure in pain perception and descending control of pain. A number of investigators have been using cell-type specific manipulations to probe the underlying circuitry of the amygdala. As data have accumulated in this research space, we recognized a critical need for a single framework to integrate these data and evaluate emergent system-level responses. In this manuscript, we present an agent-based computational model of two distinct inhibitory neuron populations in the amygdala, those that express protein kinase C delta (PKCδ) and those that express somatostatin (SOM). We utilized a network of neural links to simulate connectivity and the transmission of inhibitory signals between neurons. Type-specific parameters describing the response of these neurons to noxious stimuli were estimated from published physiological and immunological data as well as our own wet-lab experiments. The model outputs an abstract measure of pain, which is calculated in terms of the cumulative pro-nociceptive and anti-nociceptive activity across neurons in both hemispheres of the amygdala. Results demonstrate the ability of the model to produce changes in pain that are consistent with published studies and highlight the importance of several model parameters. In particular, we found that the relative proportion of PKCδ and SOM neurons within each hemisphere is a key parameter in predicting pain and we explored model predictions for three possible values of this parameter. We compared model predictions of pain to data from our earlier behavioral studies and found areas of similarity as well as distinctions between the data sets. These differences, in particular, suggest a number of wet-lab experiments that could be done in the future. In this manuscript, we present a computational modeling approach to understand and predict pain output from a part of the brain, the amygdala, involved in stress adaptation, emotional regulation, and pain. Over the last several years, a variety of groups have begun to dissect the specific cells that are responsible for the impact of amygdala activation on pain, which can include both increases and decreases in pain and pain-like output in animal models. It is helpful and necessary to use computational models to develop a framework to understand the amygdala as these wet lab techniques add to the complexity of our understanding of the brain structure. The model presented here was based on our recent published physiology experiments along with multiple examples of expression data. This model can be used to design future wet-lab experiments and can continue to be refined to help us evaluate the impact of the amygdala and similar limbic system structures in pain and other disease. Here we present the first computational model for amygdala signaling that includes physiological and histological properties of neurons and allows dynamic simulation of nociceptive signal propagation through the network.
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影响因子：
10.6
作者：
Kovner R;Souaiaia T;Fox AS;French DA;Goss CE;Roseboom PH;Oler JA;Riedel MK;Fekete EM;Fudge JL;Knowles JA;Kalin NH
通讯作者：
Kalin NH
影响因子：
5.3
作者：
Dimitrov, Eugene L.;Tsuda, Mumeko C.;Usdin, Ted B.
通讯作者：
Usdin, Ted B.
影响因子：
1.2
作者：
Gurcan, Onder;Turker, Kemal S.;Glize, Pierre
通讯作者：
Glize, Pierre
DOI：
10.1523/jneurosci.1216-10.2010
发表时间：
2010-06-16
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Kolber BJ;Montana MC;Carrasquillo Y;Xu J;Heinemann SF;Muglia LJ;Gereau RW 4th
通讯作者：
Gereau RW 4th
影响因子：
64.5
作者：
Han S;Soleiman MT;Soden ME;Zweifel LS;Palmiter RD
通讯作者：
Palmiter RD