Understanding the heterogeneity of anxiety using a translational neuroscience approach

Abstract

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.

Keywords: Amygdala; Animal models; Anxiety disorders; Computational modeling; Fear and anxiety.

Fig. 1

Heterogeneity in the presentation of anxiety disorders. A schematic depicting a subset of anxiety disorder symptoms (left) and how a subset of patients can present with some but not all symptoms. Each patient can have a different symptom profile that can be shared with patients with distinct diagnoses. GAD = generalized anxiety disorder; SAD = social anxiety disorder; PD = panic disorder

Fig. 2

Anxiety research spans many disciplines. Animal models provide a framework for examining the neurobiology that gives rise to anxiety and fear, and unique animal models are better suited to answer specific questions at different levels of analysis. Computational models offer an opportunity to bridge the gap between different models and levels of analysis

Fig. 3

Diagram of circuit mechanisms that can contribute to the heterogeneity of fear and anxiety measures. Mutually inhibitory networks in CeL can trigger distinct populations of CeM output neurons that project to PAG and other downstream regions (such as the dorsal vagal nerve) to initiate the varied responses that are used in animal studies of fear and anxiety. CeL = central amygdala, lateral; CeM = central amygdala, medial; dPAG = dorsal periaqueductal gray; vlPAG = ventrolateral periaqueductal gray

Fig. 4

Model-based MVPA. A schematic of how different models of Ce function based on experiments in rodents can make predictions about the pattern of activation in human fMRI studies. Because the distribution of cell types contributing to different behaviors are not uniformly distributed across voxels, different behaviors are hypothesized to be associated with differences in the pattern of BOLD response across Ce voxels. Ce = central amygdala