| Autor: |
Ellora M. McTaggart, Noah W. Miller, Maria M. Ortiz-Juza, Nicolas C. Pégard, Jose Rodriguez-Romaguera |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Frontiers in Behavioral Neuroscience, Vol 18 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1662-5153 |
| DOI: |
10.3389/fnbeh.2024.1481935 |
| Popis: |
Social interactions are fundamental for our survival as a predominately social species. We need and seek positive social interactions to navigate the world. However, when social interactions are negative, and occur in the presence of an aversive event, learning occurs to associate such social interactions as threatening. Gaining insight into the neural circuits that drive social threat learning is crucial for understanding social interactions. Animal models can be leveraged to employ technologies that allow us to track neuronal processes with very high resolution to obtain a better understanding of the neural circuits involved. To accomplish this, we need robust behavioral models that are replicable and high throughput. Here, we present an open-source social interaction chamber that detects social interaction and automatically pairs it with foot shock. The social interaction chamber is designed to easily integrate into modular chambers commonly used for auditory and context threat learning. It contains an array of optical gates that precisely track mouse-to-mouse interactions in real time with digital triggers that can communicate with external devices to deliver a foot shock. We find that pairing social interactions with electric foot shock using our fully automated social interaction chamber is optimal for social threat associations. We further demonstrate that timing of social contact with foot shock produces optimal learning. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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