Locomotor suppression by a monosynaptic amygdala to brainstem circuit
Bibliographic record
Abstract
Abstract The control of locomotion is fundamental to vertebrate animal survival. Defensive situations require an animal to rapidly decide whether to run away or suppress locomotor activity to avoid detection. While much of the neural circuitry involved in defensive action selection has been elucidated, top-down modulation of brainstem locomotor circuitry remains unclear. Here we provide evidence for the existence and functionality of a monosynaptic connection from the central amygdala (CeA) to the mesencephalic locomotor region (MLR) that inhibits locomotion in unconditioned and conditioned defensive behavior in mice. We show that locomotion stimulated by airpuff coincides with increased activity of MLR glutamatergic neurons. Using retrograde tracing and ex vivo electrophysiology, we find that the CeA makes a monosynaptic connection with the MLR. In the open field, in vivo stimulation of this projection suppressed spontaneous locomotion, whereas inhibition of this projection had no effect. However, inhibiting CeA terminals within the MLR increased both neural activity and locomotor responses to airpuff. Finally, using a conditioned avoidance paradigm known to activate CeA neurons, we find that inhibition of the CeA projection increased successful escape, whereas activating the projection reduced escape. Together these results provide evidence for a new circuit substrate influencing locomotion and defensive behaviors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".