Drzewiecki Carly M, Fox Andrew S
California National Primate Research Center, University of California, Davis, CA, USA.
Department of Psychology, University of California, Davis, CA, USA.
Cogn Affect Behav Neurosci. 2024 Apr;24(2):228-245. doi: 10.3758/s13415-024-01162-3. Epub 2024 Feb 14.
Anxiety disorders affect millions of people worldwide and present a challenge in neuroscience research because of their substantial heterogeneity in clinical presentation. While a great deal of progress has been made in understanding the neurobiology of fear and anxiety, these insights have not led to effective treatments. Understanding the relationship between phenotypic heterogeneity and the underlying biology is a critical first step in solving this problem. We show translation, reverse translation, and computational modeling can contribute to a refined, cross-species understanding of fear and anxiety as well as anxiety disorders. More specifically, we outline how animal models can be leveraged to develop testable hypotheses in humans by using targeted, cross-species approaches and ethologically informed behavioral paradigms. We discuss reverse translational approaches that can guide and prioritize animal research in nontraditional research species. Finally, we advocate for the use of computational models to harmonize cross-species and cross-methodology research into anxiety. Together, this translational neuroscience approach will help to bridge the widening gap between how we currently conceptualize and diagnose anxiety disorders, as well as aid in the discovery of better treatments for these conditions.
焦虑症影响着全球数百万人,由于其临床表现存在显著异质性,给神经科学研究带来了挑战。尽管在理解恐惧和焦虑的神经生物学方面已经取得了很大进展,但这些见解尚未带来有效的治疗方法。理解表型异质性与潜在生物学之间的关系是解决这一问题的关键第一步。我们表明,正向翻译、反向翻译和计算建模有助于对恐惧、焦虑以及焦虑症形成更精确的跨物种理解。更具体地说,我们概述了如何通过使用有针对性的跨物种方法和基于行为学的行为范式,利用动物模型来提出可在人类中进行测试的假设。我们讨论了可以指导非传统研究物种的动物研究并确定其优先级的反向翻译方法。最后,我们主张使用计算模型来协调对焦虑的跨物种和跨方法研究。这种转化神经科学方法将共同帮助弥合我们目前在概念化和诊断焦虑症方面的差距,并有助于发现针对这些病症的更好治疗方法。