Effect of standing posture on inhibitory postsynaptic potentials in gastrocnemius motoneurons
Bibliographic record
Abstract
This study examined the task dependence of sensory inputs on motoneuron excitability by comparing the inhibitory postsynaptic potential (IPSP) evoked by stimulation of the sural nerve between a standing postural task (Free Standing) and a comparable voluntary isometric contraction performed in a supine position (Lying Supine). We hypothesized that there would be a smaller IPSP in standing than in the supine position, based on the task dependence of the ankle plantarflexor activity on the standing task. Ten healthy participants participated in a total of 15 experiments. Single motor unit (MU) firings were recorded with both intramuscular fine-wire electrodes and high-density surface electromyography. Participants maintained the MU discharge at 6-8 Hz in Free Standing or Lying Supine while the right sural nerve was stimulated at random intervals between 1 and 3 s. To evaluate the reflex response, the firing times of the discriminated MUs were used to construct peristimulus time histograms and peristimulus frequencygrams. The sural nerve stimulation resulted in weaker inhibition in Free Standing than in Lying Supine. This finding is discussed in relation to the putative activation of persistent inward currents in standing posture and the task-dependent advantages of overriding inhibitory synaptic inputs to the plantarflexors to maintain the standing posture. NEW & NOTEWORTHY The task-dependent modulation of sensory inputs on motoneuron excitability in standing is not well understood. Evoking an inhibitory postsynaptic potential (IPSP) resulted in a smaller IPSP in gastrocnemius motoneurons in standing than in the supine position. Mildly painful sensory inputs produced weaker motoneuron inhibition in standing, suggesting an imperative to maintain ankle plantarflexion activity for the task of upright stance.
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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 | 0.001 |
| 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) | 0.002 | 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".