Furriel Brunna Carolinne Rocha Silva, Furriel Geovanne Pereira, Cunha Xavier Pinto Mauro, Lemos Rodrigo Pinto
Instituto Federal de Goiás, Goiânia, Brazil.
Universidade Federal de Goias, School of Electrical, Mechanical and Computer Engineering, Goiânia, Brazil.
Front Syst Neurosci. 2024 Dec 24;18:1454336. doi: 10.3389/fnsys.2024.1454336. eCollection 2024.
Dysfunction in fear and stress responses is intrinsically linked to various neurological diseases, including anxiety disorders, depression, and Post-Traumatic Stress Disorder. Previous studies using in vivo models with Immediate-Extinction Deficit (IED) and Stress Enhanced Fear Learning (SEFL) protocols have provided valuable insights into these mechanisms and aided the development of new therapeutic approaches. However, assessing these dysfunctions in animal subjects using IED and SEFL protocols can cause significant pain and suffering. To advance the understanding of fear and stress, this study presents a biologically and behaviorally plausible computational architecture that integrates several subregions of key brain structures, such as the amygdala, hippocampus, and medial prefrontal cortex. Additionally, the model incorporates stress hormone curves and employs spiking neural networks with conductance-based integrate-and-fire neurons. The proposed approach was validated using the well-established Contextual Fear Conditioning paradigm and subsequently tested with IED and SEFL protocols. The results confirmed that higher intensity aversive stimuli result in more robust and persistent fear memories, making extinction more challenging. They also underscore the importance of the timing of extinction and the significant influence of stress. To our knowledge, this is the first instance of computational modeling being applied to IED and SEFL protocols. This study validates our computational model's complexity and biological realism in analyzing responses to fear and stress through fear conditioning, IED, and SEFL protocols. Rather than providing new biological insights, the primary contribution of this work lies in its methodological innovation, demonstrating that complex, biologically plausible neural architectures can effectively replicate established findings in fear and stress research. By simulating protocols typically conducted -often involving significant pain and suffering-in an insilico environment, our model offers a promising tool for studying fear-related mechanisms. These findings support the potential of computational models to reduce the reliance on animal testing while setting the stage for new therapeutic approaches.
恐惧和应激反应功能障碍与多种神经系统疾病有着内在联系，包括焦虑症、抑郁症和创伤后应激障碍。以往使用即时消退缺陷（IED）和应激增强恐惧学习（SEFL）方案的体内模型研究，为这些机制提供了有价值的见解，并有助于新治疗方法的开发。然而，使用IED和SEFL方案评估动物受试者的这些功能障碍会导致严重的疼痛和痛苦。为了增进对恐惧和应激的理解，本研究提出了一种在生物学和行为学上合理的计算架构，该架构整合了关键脑结构的几个子区域，如杏仁核、海马体和内侧前额叶皮层。此外，该模型纳入了应激激素曲线，并采用了基于电导的积分发放神经元的脉冲神经网络。所提出的方法通过成熟的情境恐惧条件范式进行了验证，随后用IED和SEFL方案进行了测试。结果证实，更高强度的厌恶刺激会导致更强烈和持久的恐惧记忆，使消退更具挑战性。它们还强调了消退时机的重要性以及应激的显著影响。据我们所知，这是计算建模首次应用于IED和SEFL方案。本研究通过恐惧条件反射、IED和SEFL方案验证了我们的计算模型在分析恐惧和应激反应方面的复杂性和生物学真实性。这项工作的主要贡献不在于提供新的生物学见解，而在于其方法创新，表明复杂的、生物学上合理的神经架构可以有效地复制恐惧和应激研究中的既定发现。通过在计算机环境中模拟通常进行的、往往涉及严重疼痛和痛苦的方案，我们的模型为研究恐惧相关机制提供了一个有前途的工具。这些发现支持了计算模型在减少对动物测试的依赖方面的潜力，同时为新的治疗方法奠定了基础。
