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

The interplay between emotion and memory is a central topic in cognitive neuroscience, with open questions about the underlying neuronal mechanisms. This article studies dynamic interactions among the hippocampus, amygdala, and orbitofrontal cortex during an fMRI associative memory encoding task. 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. In particular, 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 the stability of each of these networks. We confirm the prior finding that memory is enhanced in emotional conditions, in particular in the Neutral-Emotional condition. In our work, this enhanced memory is associated with increased hippocampus-amygdala coupling and overall network connectivity. In addition, we show that in the Emotional-Emotional condition, the coupling of the hippocampus and amygdala, as well as the whole network connectivity increases when the first images valence is substantially less negative rated than the second image. This pattern mirrors the Neutral-Emotional condition, where the first image is neutral compared with the second one. Moreover, our model-based analyses suggest that the amygdala predominantly influences the other two regions in the Neutral-Emotional condition, whereas the OFC plays a dominant role in the other two conditions. Combined data-driven modeling, stability analyses, and graph-theory tools led to new insights and enhanced the mechanistic understanding of cortical 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 design of future regulatory mechanisms.