Associative emotional memory encoding: insights from network stability analysis of an fMRI-driven bilinear dynamics

Objectives The interplay between emotion and memory is a central topic in cognitive neuroscience, with open questions about the underlying neuronal mechanisms. This article aims to study the effects of order and intensity of emotional information on associative memory encoding. To this aim, we employ dynamic causal modeling to model the dynamic network composed of the hippocampus, amygdala, and orbitofrontal cortex during an fMRI associative memory encoding task and apply graph and control theory tools to obtain novel insights. Methods Participants were clustered into three condition groups, neutral–neutral, neutral–emotional, or emotional–emotional, and viewed image pairs associated with their assigned condition. Using the dynamic causal modeling framework, we explore several dynamic models and show that a stochastic bilinear state-space model best describes the neuronal dynamics in all conditions. Furthermore, we use graph and control theory techniques to both validate and analyze the model. Particularly, we analyze the network dynamics of each condition using tools from graph theory and stability theory and discuss the differences in the strength and direction of connectivity as well as stability of each of these networks. Results We confirm the prior finding that memory is enhanced in the neutral-emotional condition. In our work, this enhanced memory is associated with the increased hippocampus–amygdala coupling strength in this condition. Moreover, we show that in the emotional–emotional condition, coupling of hippocampus and amygdala, as well as the whole network connectivity increases. We further predict that the hippocampus–amygdala connectivity in this condition increases, when the first image's valence is substantially less negative rated than the second image, but decreases otherwise. This pattern mirrors the neutral–emotional condition, where the first image is emotionally neutral compared with the second. Moreover, our model-based analyses suggest that the amygdala predominantly influences the other two regions in the neutral–emotional condition. Conclusion Combined data-driven DCM modeling, stability analyses, and graph-theory tools led to new insights and enhanced the mechanistic understanding of dynamics of emotional associative memory. We discuss these insights, utilize these analytical tools to generalize our findings to some unmeasured conditions, and highlight the potential of these techniques to inform the development of future technological or pharmacological approaches targeting regulatory mechanisms.