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

ABSTRACT

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.

Keywords:

• Decision-making
• Dynamic synchrony
• Gaussian processes
• Spike trains

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Supplementary Materials

Supplementary materials are provided in four separate files contained in a single archive:

• Computational details: This file includes the details of our computational methods along with the Markov chain Monte Carlo (MCMC) algorithms we used for Bayesian inference.

• Negative correlation: This file includes an additional illustrative example showing that our method can easily detect negatively correlated neurons.

• Experimental details: This file provides the details of experimental data along with our results for model checking and diagnostics.

• Computer codes: This zipped file includes all the MATLAB codes to implement our method and reproduce our results. It also contains the experimental data discussed in our article.

Acknowledgments

The authors thank the associate editor and anonymous referees for their insightful suggestions and constructive comments that helped them to improve their article.

Funding

Babak Shahbaba was supported by the NIH grant R01 AI107034. David Moorman was supported by the NIH grant R21-DA032005. Hernando Ombao acknowledges the support from NSF Division of Mathematical Sciences and NSF Division of Social and Economic Sciences.