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.

中文翻译：

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基底神经节计算模型中正负情绪状态之间的双极振荡

躁郁症的特征在于情绪波动，即躁狂和抑郁状态之间的波动。摆动（振荡）标志着患者情绪周期（周期）中发作的持续时间，并且可能从数小时到数年不等。拟议的建模研究使用决策框架来研究基底神经节网络在产生双极振荡中的作用。在此模型中，基底神经节系统执行两臂强盗任务，其中一个臂（动作响应）导致正面结果，而另一臂导致负面结果。当模型代理收到各种结果时，我们探索系统中关键奖励和风险相关参数的动态。特别是，我们使用代表决策快速动态的模型研究系统，以及捕获慢速动力学的模块，该模块描述了快速动力学的某些元参数在长时间范围内的变化。该模型在三个抽象层次上进行转换：（1）二维动力学系统模型，它是一个简单的两个变量模型，能够显示出奖励和惩罚性结果的双稳态；（2）现象学的基底神经节模型，将其含义从简化模型扩展到皮质基底神经节的建立；（3）基底神经节的详细网络模型，该模型结合了详细的细胞水平模型，以便更实际地理解。在健康情况下，该模型选择积极的行动，避免消极的行动，而在双相情况下，该模型在选择积极或消极结果方面表现出缓慢的振荡，让人联想到双极振荡。在具有两个变量的简单简化动力系统上的相平面分析揭示了产生病理性“双极型”振荡的基本参数。基底神经节的现象学和网络模型扩展了这种逻辑，并根据对皮质-基底神经节网络动力学的多巴胺能和血清素能投射的活动来解释双极振荡。网络的功能异常，特别是在奖励和风险敏感性方面，被证明是造成病理性双极性振荡的原因。该研究提出了一个计算模型，该模型探讨了5-羟色胺神经调节受损对皮质基底神经节网络动力学的影响，并将这种损害与抽象的情绪状态（躁狂和抑郁发作）和双相情感障碍的振荡相关。