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Bipolar oscillations between positive and negative mood states in a computational model of Basal Ganglia

机译:基础神经节计算模型中正面和负面情绪状态的双极振荡

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Bipolar disorder is characterized by mood swings-oscillations between manic and depressive states. The swings (oscillations) mark the length of an episode in a patient's mood cycle (period), and can vary from hours to years. The proposed modeling study uses decision making framework to investigate the role of basal ganglia network in generating bipolar oscillations. In this model, the basal ganglia system performs a two-arm bandit task in which one of the arms (action responses) leads to a positive outcome, while the other leads to a negative outcome. We explore the dynamics of key reward and risk related parameters in the system while the model agent receives various outcomes. Particularly, we study the system using a model that represents the fast dynamics of decision making, and a module to capture the slow dynamics that describe the variation of some meta-parameters of fast dynamics over long time scales. The model is cast at three levels of abstraction: (1) a two-dimensional dynamical system model, that is a simple two variable model capable of showing bistability for rewarding and punitive outcomes; (2) a phenomenological basal ganglia model, to extend the implications from the reduced model to a cortico-basal ganglia setup; (3) a detailed network model of basal ganglia, that incorporates detailed cellular level models for a more realistic understanding. In healthy conditions, the model chooses positive action and avoids negative one, whereas under bipolar conditions, the model exhibits slow oscillations in its choice of positive or negative outcomes, reminiscent of bipolar oscillations. Phase-plane analyses on the simple reduced dynamical system with two variables reveal the essential parameters that generate pathological 'bipolar-like' oscillations. Phenomenological and network models of the basal ganglia extend that logic, and interpret bipolar oscillations in terms of the activity of dopaminergic and serotonergic projections on the cortico-basal ganglia network dynamics. The network's dysfunction, specifically in terms of reward and risk sensitivity, is shown to be responsible for the pathological bipolar oscillations. The study proposes a computational model that explores the effects of impaired serotonergic neuromodulation on the dynamics of the cortico basal ganglia network, and relates this impairment to abstract mood states (manic and depressive episodes) and oscillations of bipolar disorder.
机译:双极性障碍的特征在于躁狂和抑郁状态之间的情绪波动 - 振荡。摇摆(振荡)标志着患者情绪周期(周期)中的集的长度,并且可以在几小时内变化。建议的建模研究使用决策框架来调查基础神经节网络在发电双极振荡中的作用。在该模型中,基础神经节系统执行双臂强盗任务,其中一个臂(动作响应)导致正结果,而另一个导致负面结果。在模型代理接收各种结果时,我们探索系统中的关键奖励和风险相关参数的动态。特别是,我们使用代表决策制作的快速动态的模型研究系统,以及模块,以捕获慢速动态的模块,该模块在长时间尺度上描述了快速动态的一些元参数的变化。该模型以三级抽象施放:(1)一种二维动态系统模型,即一种简单的两个可变模型,能够显示有益处和惩罚性结果的双稳态; (2)一种现象学基础神经节模型,将缩减模型的影响扩展到皮质基底Ganglia设置; (3)基础神经节的详细网络模型,包括更现实的理解的详细蜂窝水平模型。在健康的条件下,模型选择积极的作用,避免了负面作用,而在双极条件下,该模型在选择正面或负面结果时表现出缓慢的振荡,让人联想到双极振荡。在具有两个变量的简单减少动态系统上的相平面分析显示了产生病理'双极状'振荡的基本参数。基础神经节的现象学和网络模型在皮质基天文节目网络动态上的多巴胺能和血清onerogic突起的活性方面延伸了该逻辑,并解释了双极振荡。网络的功能障碍,特别是在奖励和风险敏感度方面,被证明是对病理双极振荡负责。该研究提出了一种计算模型,该计算模型探讨了血清组织神经调节受损对Cortico基础神经节网络的动态的影响,并将这种损伤与抽象情绪状态(躁狂和抑郁发作)和双相情感障碍的振荡相关。

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