Preparatory cortical and spinal settings to counteract anticipated and non-anticipated perturbations
Bibliographic record
Abstract
Little is known about how the central nervous system prepares postural responses differently in anticipated compared to non-anticipated perturbations. To investigate this, participants were exposed to translational and rotational perturbations presented in a blocked (anticipated) and a random (non-anticipated) design. The preparatory setting ('central set') was measured by H-reflexes, motor-evoked potentials (MEPs), and short-interval intracortical inhibition (SICI) shortly before perturbation onset in the soleus of 15 healthy adults. Additionally, the behavioral consequences of differential preparatory settings were analyzed by comparing the short- (SLR), medium- (MLR), and long-latency response (LLR) of the soleus after anticipated and non-anticipated rotations and translations. H-reflexes elicited before perturbation were different between conditions (p=0.023) with larger amplitudes in anticipated translations compared to anticipated rotations (37.0%; p=0.048). Reduced SICI was found in the three conditions containing perturbations compared to static standing (p<0.001). Muscular responses assessed after perturbations remained unchanged for the SLR and MLR, whereas the LLR was decreased in anticipated rotations (-36.2%; p=0.002) and increased in anticipated translations (16.7%; p=0.046) compared to the corresponding non-anticipated perturbation. As the SLR and MLR are organized at the spinal and the LLR at the cortical level, the preparatory setting seems to mainly influence cortically mediated postural responses. However, the modulation of the H-reflex before anticipated perturbations indicates that supraspinal centers adjusted Ia-afferent transmission for the soleus in a perturbation-specific manner. Intracortical inhibition was also modulated but differentiates to a lesser extent only between perturbation conditions and unperturbed 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) | 0.001 | 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".