Reticulospinal involvement in force production depends on effector and degree of force modulation
Bibliographic record
Abstract
The nervous system executes movements primarily through the corticospinal and reticulospinal descending tracts. Although it was previously thought that the reticulospinal tracts are predominantly used for locomotion and postural adjustments, recent evidence implicates reticulospinal involvement in voluntary movements, although in differing degrees depending on the type of movement and muscles involved. The purpose of the current study was to assess the relative degree of reticulospinal contributions to various movements executed using wrist flexors versus wrist extensors. Participants performed a bimanual force production task that required either wrist flexion, wrist extension, or wrist flexion with one arm and wrist extension with the other arm. In addition to the use of different effectors, participants either produced a constant force or were required to track an oscillating force target to assess how force modulation affected the pathways involved in movement control. Neural contributions were assessed using EMG-EMG coherence, which quantifies the relative degree of common drive to both muscles in the frequency domain. During the constant force task, results showed increased 8-20 Hz coherence for bimanual flexion movements compared to all other conditions, indicative of greater reticulospinal drive. Additionally, increased coherence in the 8-12 Hz (alpha) band was found for force tracking as compared to constant force for all movement types, which may represent continuous monitoring of force feedback and tracking error. Overall, these findings provide evidence that brainstem structures contribute to force production differentially depending on involved effectors and may be implicated in force control and error prediction.Acknowledgments: This research was supported by the Natural Sciences and Engineering Research Council of Canada and the Ontario Ministry of Research, Innovation and Science.
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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 | 0.002 |
| 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".