Influência do medo e do estresse na formação da memória: uma abordagem neurocomputacional

Abstract

Dysfunction of fear and stress responses contribute directly to a range of neurological diseases, including anxiety disorders, depression, and Post- Traumatic Stress Disorder. Previous studies utilizing in vivo models with Immediate-Extinction Deficit (IED) and Stress Enhanced Fear Learning (SEFL) protocols have provided valuable information on the mechanisms underlying these diseases and for developing new therapeutic approaches. However, it is crucial to acknowledge that assessing these dysfunctions using IED and SEFL protocols in animal subjects potentially subjects them to pain and suffering. In order to help better understand the neural mechanisms underlying fear and stress, this study develops a biologically plausible computational architecture that integrates several subregions of crucial brain structures, such as the amygdala, hippocampus, and medial prefrontal cortex. Furthermore, it proposes an innovative computational model incorporating stress hormone curves and employing spiking neural networks with conductance-based integrate-and-fire neurons. Initially, a computational model with a reduced architecture (Model 1) was developed and used to validate the simulated neural network functionally; subsequently, a second, more comprehensive model (Model 2) was applied to simulate more complex experimental conditions. The study tested and validated both models using the Contextual Fear Conditioning paradigm. Subsequently, in Model 2, the proposed approach was tested using IED and SEFL protocols to assess its applicability to the study of fear- and stress-related disorders. The results confirmed that the greater the intensity of the aversive stimuli, the more robust and persistent the fear memory, making extinction more difficult. Extinction performed immediately after exposure to the stressful stimulus tended to increase fear generalization. Furthermore, the results highlighted that, under stressful conditions, the brain model encoded the fear memory more intensely, making the extinction process more complex and challenging. This is the first study to apply computational modeling to the IED and SEFL protocols. The results elucidate how aversive stimuli impact the acquisition and extinction of fear, highlighting the relevance of the moment of extinction and the significant influence of stress. Finally, this computational approach may facilitate the formulation and testing of hypotheses, enhancing advances in therapeutic efficacy for disorders linked to fear and stress.