Protocol for labeling and manipulating “home”-activated neurons in mice
Bibliographic record
Abstract
Home-related cues, such as home bedding, influence affective behaviors in mice, including fear relief, anxiety reduction, and defensive aggression. Here, we present a protocol for labeling and manipulating “home”-activated neurons in the lateral hypothalamus (LH) using a Tet-Off inducible system. We describe steps for virus injection, postoperative recovery, and combining c-Fos-driven activity labeling with chemogenetics. This protocol integrates neuronal targeting with behavioral assays to provide insights into the functional role of “home”-activated neurons in home-driven behavioral adaptations. For complete details on the use and execution of this protocol, please refer to Su et al. 1 • Steps for injecting AAV2/9 viral vector into the mouse lateral hypothalamus • Guidance on c-Fos-driven labeling of neurons activated by home bedding cues • Procedures for chemogenetic manipulation to assess home-driven behaviors Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Home-related cues, such as home bedding, influence affective behaviors in mice, including fear relief, anxiety reduction, and defensive aggression. Here, we present a protocol for labeling and manipulating “home”-activated neurons in the lateral hypothalamus (LH) using a Tet-Off inducible system. We describe steps for virus injection, postoperative recovery, and combining c-Fos-driven activity labeling with chemogenetics. This protocol integrates neuronal targeting with behavioral assays to provide insights into the functional role of “home”-activated neurons in home-driven behavioral adaptations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.045 | 0.020 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".