Local brainstem circuits contribute to reticulospinal output in the mouse
Bibliographic record
Abstract
ABSTRACT Glutamatergic reticulospinal neurons in the gigantocellular reticular nucleus (GRN) of the medullary reticular formation can function as command neurons, transmitting motor commands to spinal cord circuits. Recent advances in our understanding of this neuron-dense region have been facilitated by the discovery of expression of the transcriptional regulator, Chx10, in excitatory reticulospinal neurons. Here, we address the capacity of local circuitry in the GRN to contribute to reticulospinal output. We define two sub-populations of Chx10-expressing neurons in this region, based on distinct electrophysiological properties and somata size (small and large), and show that these correspond to local interneurons and reticulospinal neurons, respectively. Using focal release of caged-glutamate combined with patch clamp recordings, we demonstrated that Chx10 neurons form microcircuits in which the Chx10 interneurons project to and facilitate the firing of Chx10 reticulospinal neurons. We discuss the implications of these microcircuits in terms of movement selection. SIGNIFICANCE STATEMENT Reticulospinal neurons in the medullary reticular formation play a key role in movement. The transcriptional regulator Chx10 defines a population of glutamatergic neurons in this region, a proportion of which have been shown to be involved in stopping, steering, and modulating locomotion. While it has been shown that these neurons integrate descending inputs, we asked whether local processing also ultimately contributes to reticulospinal outputs. Here, we define Chx10-expressing medullary reticular formation interneurons and reticulospinal neurons, and demonstrate how the former modulate the output of the latter. The results shed light on the internal organization and microcircuit formation of reticular formation neurons.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".