The multilevel exploration test, a novel paradigm to measure exploratory behavior in depression animal models and the involvement of the PL-ZI circuit.

Summary

The Multilevel Exploration Test (MET) apparatus was developed to assess exploratory behavior in mice, revealing that depressed mice show reduced motivation for exploration. Stimulation of the prelimbic cortex to zona incerta circuit restored this exploratory deficit and alleviated other depression-like behaviors. Additionally, a machine learning model achieved over 92% accuracy in predicting individual emotional states. The MET offers a new way to study motivation-related issues in depression and could aid in identifying targets for antidepressant development.

Study at a glance

Abstract

Diminished drive is one of the core symptoms of major depressive disorder (MDD) diagnosis, yet its underlying neural mechanisms remain elusive, primarily due to a lack of appropriate animal models. We developed a novel Multilevel Exploration Test (MET) apparatus to evaluate exploratory behavior, which is captured as a dynamic, stage-dependent process involving "search", "attend/investigate", and "approach" phases. We employed fiber photometry to measure real-time dopamine dynamics in the nucleus accumbens. We further combined cFos staining and neural circuit tracing to identify relevant brain regions and circuits, and employed chemogenetics to selectively modulate prelimbic cortex (PL) inputs to zona incerta (ZI). The MET tests were conducted across five depression models, with ketamine administration to evaluate rescue effects. Machine learning algorithms were utilized to analyze MET data and predict individual emotional states (normal, anxiety-like, depression-like). Here, we developed a novel paradigm to assess exploratory behavior, which demonstrates etiological validity, face validity and predictive validity. Depressed mice exhibited reduced motivation for exploration in this paradigm, while stimulation of the PL-ZI circuit not only restored exploratory deficits but also alleviated other depression-like behaviors in these mice. Furthermore, we established a machine learning-based model to predict individual animals' emotional states by integrating data from the new paradigm, achieving a prediction accuracy of over 92%. The MET provides a functional, high-throughput paradigm for dissecting motivation-related pathology. It facilitates the assessment of depressive-like behaviors, enables the prediction of emotional states, and supports the discovery of novel targets for antidepressant development.

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