Neural Mechanism of the Approach-Avoidance Conflict and its Relationship to Anxiety

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About this Research Topic

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Background

Many of our decisions involve weighing benefits against costs. The Approach-avoidance conflict is an essential psychological concept and has been used to characterize anxiety-like behavior and pessimistic decision-making. An optimistic view leads to more approach decisions; a pessimistic view leads to more avoidance decisions. Decisions under approach-avoidance conflict have primarily contributed to characterize the effect of various types of anti-anxiety drugs. Contrary to these advances in neuropharmacology, the underlying neural mechanisms of the approach-avoidance conflict have been less explored.

Recently, several significant advances in behavioral, neuroscientific, and computational fields were made. They include the neural correlates of the approach-avoidance motivation, circuit manipulation to regulate anxiety-like state in animals, and computational architecture of excessive avoidance characterizing a critical feature of anxiety disorders. The circuit manipulation studies in animal models reported several brain areas that could be involved in the regulation of approach-avoidance motivations. The areas include the pregenual anterior cingulate cortex and striatum of macaques and prelimbic and striosome compartment in the striatum in rodents. However, translational links to human anxiety disorder and the detailed neural mechanism that might underly such an abnormal state remains unclear. In light of recent progress, this Research Topic focuses on multi-disciplinary studies that contribute to a further understanding of the approach-avoidance conflict and their relationship to anxiety.

We attempt to explore the role of anxiety in decision-making from multi-disciplinary viewpoints. First, although the approach-avoidance conflict paradigm has been applied extensively for the anxiolytic drug discovery, we still do not have a full construct validity for the reason why we use conflict tests to measure the degree of anxiety. The conflict decision-making is suitable to examine the animal’s internal valuation for a potential threat, but its relationship to the anxiety disorders in humans is unclear. Secondly, although an increasing number of studies clarified the neural correlates of approach-avoidance behaviors, the underlying neural mechanism of how the behaviors and decisions were made under the conflict condition is still unclear. Third, we consider that the computational approach is one of the essential issues to be addressed in this field. We attempt to explore the computational approach to characterize the behavioral features in conflict decision-making and anxiety, aiming for constructing a better framework to explain the relationship between the approach-avoidance conflict and anxiety.

The goal of this Research Topic is thus to collect multi-disciplinary studies from various fields that could contribute to further understanding of the neural mechanism of the approach-avoidance conflict and its relationship to anxiety.
We welcome all article types addressing this topic in human, non-human primates, rodents, and other animal models, focusing on the following areas of investigation:

• Behavioral theory and experimental readouts underlying the relationship between anxiety disorder and conflict decision-making
• Neural and neuronal mechanisms involved in anxiety-like decision-making in humans, non-human primates, rodents, and other animals
• Neural mechanism of approach-avoidance conflict in humans, non-human primates, rodents, and other animal models, using neural recordings, circuit simulations, and behavioral analyses, and other neuroscientific techniques.
• Circuit mechanisms involved in the etiology of anxiety disorder.
• Computational approaches to characterize approach-avoidance behavior and conflict decision-making.
• Computational modeling of the neural mechanism involved in the anxiety disorder.

Keywords: Anxiety, approach-avoidance conflict, decision-making, computational modeling, neural circuits

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