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

Depressed mice show reduced motivation to explore in a new behavioral test called the Multilevel Exploration Test (MET), which breaks exploration into search, attend/investigate, and approach phases. Activating a specific neural circuit from the prelimbic cortex to the zona incerta restored exploratory deficits and alleviated other depression-like behaviors. A machine learning model using MET data predicted individual emotional states—normal, anxiety-like, or depression-like—with over 92% accuracy. The MET offers a high-throughput way to study motivation-related brain mechanisms and may help identify new antidepressant targets.

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.