Comparison of the reticulospinal drive to lumbar erector spinae muscles in postural and voluntary tasks using the StartReact paradigm
Bibliographic record
Abstract
Introduction: While lesion and neurophysiological animal studies point toward a notable involvement of subcortical pathways in the control of low back muscles, little attention has been dedicated to the subject in humans. The StartReact paradigm may allow to indirectly test the potential contribution of the reticulospinal system during motor control, thus addressing this gap of knowledge. In this study, we aimed to compare the potential contribution of the reticulospinal system in the control of low back muscles during voluntary (lumbar spine extension) and postural (upper limb movement eliciting anticipatory postural adjustment) tasks using the StartReact paradigm. Methods: was measured within a simple precued RT task while conditioned by startling (SAS-116 dB) or non-startling (NSAS-80 dB) acoustic stimuli. Results: The reduction in RT was similar during the postural and voluntary tasks. However, RT was more shortened with the SAS condition compared to the NSAS condition in both tasks. This finding was replicated using a cumulative distribution functions analysis. Discussion: For the first time, a StartReact effect of back muscles was demonstrated during a voluntary task and was shown to be similar to that observed in a postural task. Therefore, these results suggest a contribution of the reticulospinal tract in the postural and voluntary control of back muscles in humans.
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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) | 0.001 | 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".