Homologous Basal Ganglia Network Models in Physiological and Parkinsonian Conditions.
Homologous Basal Ganglia Network Models in Physiological and Parkinsonian Conditions.
复制标题
生理和帕金森氏症条件下的同源基底神经节网络模型。
DOI：
10.3389/fncom.2017.00079
复制
发表时间：
2017
影响因子：
3.2
通讯作者：
Morrison A
中科院分区：
文献类型：
--
作者：
Bahuguna J;Tetzlaff T;Kumar A;Hellgren Kotaleski J;Morrison A
近年来，随着核内亚群和以前未知的投射的发现，基底神经节的经典模型得到了改进。一个这样的发现是存在的arkypallidal和原型的苍白球，这是以前被认为是一个主要的同质核的外神经元的亚群。开发这些多个相互连接的原子核的计算模型是具有挑战性的，因为连接的强度在很大程度上是未知的。因此，我们使用遗传算法来搜索未知的连接参数的射击率模型。我们应用一个二元成本函数来自经验的放电率和相位关系的生理和帕金森病的条件下的数据。我们的方法为每个条件生成了超过1,000个配置或同源性的集合，其中许多参数值分布广泛，并且两个条件之间存在重叠。然而，由此产生的连接或原型和arkypallidal神经元的有效权重与实验数据是一致的。我们调查的重要性的重量变化，通过操纵参数单独和累积，并得出结论，观察到的参数之间的相关性是必要的生成动态的两个条件。然后，我们调查的网络的反应，一个短暂的皮层刺激，并表明，网络分类为生理有效地抑制活动的内部苍白球，是不容易受到振荡，而帕金森氏症的网络表现出相反的趋势。因此，我们的结论是，在苍白球中观察到的速率和相位关系是预测实验观察到的更高层次的动态功能的生理和帕金森基底神经节，和我们的方法产生的解决方案的多样性可能是一个自然的多样性基底神经节网络的指示。我们建议，我们的方法生成和分析一个欠定网络模型的多个解决方案的合奏提供了更大的信心，其预测比那些来自一个独特的解决方案，并预测这样的同源网络在一个低维空间的合理选择的动力学特征提供了一个更好的机会比纯粹的结构分析在理解复杂的病理，如帕金森氏病。
The classical model of basal ganglia has been refined in recent years with discoveries of subpopulations within a nucleus and previously unknown projections. One such discovery is the presence of subpopulations of arkypallidal and prototypical neurons in external globus pallidus, which was previously considered to be a primarily homogeneous nucleus. Developing a computational model of these multiple interconnected nuclei is challenging, because the strengths of the connections are largely unknown. We therefore use a genetic algorithm to search for the unknown connectivity parameters in a firing rate model. We apply a binary cost function derived from empirical firing rate and phase relationship data for the physiological and Parkinsonian conditions. Our approach generates ensembles of over 1,000 configurations, or homologies, for each condition, with broad distributions for many of the parameter values and overlap between the two conditions. However, the resulting effective weights of connections from or to prototypical and arkypallidal neurons are consistent with the experimental data. We investigate the significance of the weight variability by manipulating the parameters individually and cumulatively, and conclude that the correlation observed between the parameters is necessary for generating the dynamics of the two conditions. We then investigate the response of the networks to a transient cortical stimulus, and demonstrate that networks classified as physiological effectively suppress activity in the internal globus pallidus, and are not susceptible to oscillations, whereas parkinsonian networks show the opposite tendency. Thus, we conclude that the rates and phase relationships observed in the globus pallidus are predictive of experimentally observed higher level dynamical features of the physiological and parkinsonian basal ganglia, and that the multiplicity of solutions generated by our method may well be indicative of a natural diversity in basal ganglia networks. We propose that our approach of generating and analyzing an ensemble of multiple solutions to an underdetermined network model provides greater confidence in its predictions than those derived from a unique solution, and that projecting such homologous networks on a lower dimensional space of sensibly chosen dynamical features gives a better chance than a purely structural analysis at understanding complex pathologies such as Parkinson's disease.
登录
查看更多内容
纹状体培养基神经元的功能连接组。
DOI：
10.1523/jneurosci.3833-10.2011
发表时间：
2011-01-26
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Chuhma N;Tanaka KF;Hen R;Rayport S
通讯作者：
Rayport S
影响因子：
3
作者：
Flores-Barrera, Eden;Vizcarra-Chacon, Bianca J.;Galarraga, Elvira
通讯作者：
Galarraga, Elvira
影响因子：
3.3
作者：
Ammari, R.;Lopez, C.;Hammond, C.
通讯作者：
Hammond, C.
影响因子：
2.5
作者：
Boraud, T;Bezard, E;Gross, CE
通讯作者：
Gross, CE
DOI：
10.1111/ejn.12822
发表时间：
2015-03
期刊：
The European journal of neuroscience
影响因子：
--
作者：
Chen MC;Ferrari L;Sacchet MD;Foland-Ross LC;Qiu MH;Gotlib IH;Fuller PM;Arrigoni E;Lu J
通讯作者：
Lu J