Locomotor-related propriospinal V3 neurons produce primary afferent depolarization and modulate sensory transmission to motoneurons
Bibliographic record
Abstract
ABSTRACT When a muscle is stretched it not only responds with a reflex, but the sensory afferent feedback also depolarizes many afferents throughout the spinal cord (termed primary afferent depolarization, PAD), readying the whole limb for further disturbances. This sensory-evoked PAD is thought to be caused by a trisynaptic circuit, where sensory input activates first order excitatory neurons that activate GABAergic neurons that in turn activate GABA A receptors on afferents to cause PAD, though the identity of these first order neurons is unclear. Here we show that these first order neurons are propriospinal V3 neurons, since they receive extensive sensory input and in turn innervate GABAergic neurons that cause PAD, because optogenetic activation or inhibition of V3 neurons in mice mimics or inhibits sensory-evoked PAD, respectively. Furthermore, persistent inward sodium currents (Na PICs) intrinsic to V3 neurons enable them to respond to transient inputs with long-lasting responses, explaining the long time-course of PAD. Also, local optogenetic activation of V3 neurons at one segment causes PAD in other segments, due to the long propriospinal tracts of these neurons, explaining the widespread radiation of PAD across the spinal cord. This in turn facilitates monosynaptic reflex transmission to motoneurons across the spinal cord. Additionally, we find that V3 neurons directly innervate proprioceptive afferents, causing a glutamate receptor mediated PAD (glutamate PAD). Finally, we show that increasing the spinal cord excitability with either GABA A receptor blockers or chronic spinal cord injury causes an increase in the glutamate PAD, perhaps contributing to spasms after SCI. Overall, we show the V3 neuron has a prominent role in modulating sensory transmission, in addition to its previously described role in locomotion.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".