Balasubramani Pragathi Priyadharsini, Chakravarthy V Srinivasa
1Department of Psychiatry, University of California San Diego, La Jolla, CA 92037 USA.
2Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology-Madras, Chennai, 36 India.
Cogn Neurodyn. 2020 Apr;14(2):181-202. doi: 10.1007/s11571-019-09564-7. Epub 2019 Nov 20.
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.
双相情感障碍的特征是情绪波动——在躁狂和抑郁状态之间振荡。这些波动（振荡）标志着患者情绪周期（阶段）中一个发作期的时长，其时长可从数小时到数年不等。拟进行的建模研究使用决策框架来探究基底神经节网络在产生双相情感障碍振荡中的作用。在这个模型中，基底神经节系统执行双臂赌博任务，其中一个臂（动作反应）会导致积极结果，而另一个臂则导致消极结果。当模型主体接收各种结果时，我们探究系统中关键奖励和风险相关参数的动态变化。特别地，我们使用一个代表决策快速动态变化的模型以及一个捕捉慢动态变化的模块来研究该系统，慢动态变化描述了快速动态变化的一些元参数在长时间尺度上的变化。该模型在三个抽象层次上构建：（1）二维动力学系统模型，即一个简单的双变量模型，能够显示奖励和惩罚结果的双稳态；（2）现象学基底神经节模型，将简化模型的含义扩展到皮质 - 基底神经节设置；（3）基底神经节的详细网络模型，纳入详细的细胞水平模型以获得更现实的理解。在健康状态下，该模型选择积极动作并避免消极动作，而在双相情感障碍状态下，该模型在选择积极或消极结果时表现出缓慢振荡，这让人联想到双相情感障碍振荡。对具有两个变量的简单简化动力学系统进行相平面分析，揭示了产生病理性“双相情感障碍样”振荡的基本参数。基底神经节的现象学和网络模型扩展了该逻辑，并根据多巴胺能和5-羟色胺能投射对皮质 - 基底神经节网络动力学的活动来解释双相情感障碍振荡。该网络的功能障碍，特别是在奖励和风险敏感性方面，被证明是病理性双相情感障碍振荡的原因。该研究提出了一个计算模型，该模型探究了5-羟色胺能神经调节受损对皮质 - 基底神经节网络动力学的影响，并将这种损伤与抽象的情绪状态（躁狂和抑郁发作）以及双相情感障碍的振荡联系起来。
