Analyzing the Neural Dynamics of Emotional Associative Memory Encoding through Data-Driven Modeling

Abstract

Researchers have been interested in studying the connection between emotion and memory for decades but much remains unknown due to the elusive nature of the human brain. Furthering our understanding of the phenomenon is crucial for improving the treatment of neurological disorders associated with emotion dysregulation, as well as for enhancing autonomous systems that interact with humans, such as robotic prostheses and artificial intelligence. A promising approach to studying cognitive processes is developing computational models of brain activity, which have the potential to uncover the underlying neural mechanisms.
This master's thesis presents a novel dynamical model of the neural activity underpinning the emotional associative memory encoding process. This type of memory is especially complex and poorly understood as it requires memorizing the relationships between various environmental stimuli that may elicit distinct emotions. To model this phenomenon, the thesis proposes a network model using the Dynamic Causal Modeling (DCM) framework, focusing on the Amygdala (Amy), the Hippocampus (Hip), and the Orbitofrontal Cortex (OFC) due to their central roles in emotional associative memory. Furthermore, the thesis provides an analysis of network state coordination and state error stability, deriving analytical bounds for state error dynamics that illustrate how the model's properties are linked to improved memory encoding.
The dataset used to estimate the DCM model comes from a functional Magnetic Resonance Imaging (fMRI) study conducted by Zhu et al. at Donders Institute for Brain, Cognition and Behaviour, where participants were asked to memorize pairs of two emotionally potent images (group EE), two neutral images (group NN), or one neutral and one emotional image (group NE). The DCM model developed in this thesis reflects how the neural dynamics differ among the groups and the modeled brain regions, corroborating many of the behavioral results of the experiment and bringing novel insights. Based on stability and error dynamics analysis, the model of group NE is found to exhibit the best overall information flow, which is consistent with the experiment, where this group achieved the best memorization results. Furthermore, the coupling between Amy-Hip is shown to play a key role in improving memory encoding.
Another important contribution of this thesis is the extensive overview of DCM in Chapter 2, which discusses the mathematical foundations, the system identification algorithm, criticism raised against the method, studies of its statistical validity, and advice on implementation. Additionally, Appendix B presents the first guide for DCM script development in the SPM12 toolbox in MATLAB. Furthermore, this thesis compares the performance of four different variations of DCM, proving that Stochastic DCM achieves superior results in experimental paradigms with brief emotional stimuli. The success of this approach is believed to stem from its capacity to handle model misspecification and neuronal noise. Overall, the Stochastic DCM developed in this thesis is considered to provide the most comprehensive representation of neural dynamics governing the encoding of emotional associative memory compared to other existing dynamical models.