A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making

The goal of this article is to develop a novel statistical model for studying cross-neuronal spike train interactions during decision-making. For an individual to successfully complete the task of decision-making, a number of temporally organized events must occur: stimuli must be detected, potential outcomes must be evaluated, behaviors must be executed or inhibited, and outcomes (such as reward or no-reward) must be experienced. Due to the complexity of this process, it is likely the case that decision-making is encoded by the temporally precise interactions between large populations of neurons. Most existing statistical models, however, are inadequate for analyzing such a phenomenon because they provide only an aggregated measure of interactions over time. To address this considerable limitation, we propose a dynamic Bayesian model that captures the time-varying nature of neuronal activity (such as the time-varying strength of the interactions between neurons). The proposed method yielded results that reveal new insight into the dynamic nature of population coding in the prefrontal cortex during decision-making. In our analysis, we note that while some neurons in the prefrontal cortex do not synchronize their firing activity until the presence of a reward, a different set of neurons synchronizes their activity shortly after stimulus onset. These differentially synchronizing subpopulations of neurons suggest a continuum of population representation of the reward-seeking task. Second, our analyses also suggest that the degree of synchronization differs between the rewarded and nonrewarded conditions. Moreover, the proposed model is scalable to handle data on many simultaneously recorded neurons and is applicable to analyzing other types of multivariate time series data with latent structure. Supplementary materials (including computer codes) for our article are available online.

本文旨在构建一种全新的统计模型，以研究决策过程中神经元锋电位序列（spike train）间的跨神经元交互作用。个体若要顺利完成决策任务，需依次发生一系列时序规整的事件：感知刺激、评估潜在结果、执行或抑制行为，以及体验结果（如奖励或无奖励）。由于该过程的复杂性，决策行为大概率是通过大量神经元群体间的时序精准交互进行编码的。然而，现有多数统计模型仅能给出交互作用随时间的聚合度量，无法有效分析此类现象，存在显著局限。为解决这一问题，本文提出一种动态贝叶斯模型（dynamic Bayesian model），可捕捉神经元活动的时变特性——例如神经元间交互作用的时变强度。所提方法所得结果，为决策过程中前额叶皮层（prefrontal cortex）的群体编码动态特性提供了全新的研究视角。分析过程中我们发现：前额叶皮层内部分神经元需直至奖励出现时才会同步放电，而另一组神经元则会在刺激起始（stimulus onset）后不久即实现活动同步。这类具备差异化同步特性的神经元亚群，表明奖赏追寻任务的群体表征呈现连续谱系。其次，分析结果还表明，奖赏条件与无奖赏条件下的神经元同步程度存在显著差异。此外，所提模型具备良好可扩展性，可处理多神经元同步记录数据集，同时适用于分析其他带有潜在结构（latent structure）的多变量时间序列（multivariate time series）数据。本文的补充材料（含计算机代码）可在线获取。