Plasticity and sensory motor integration in cerebellum and motor cortex following cervical extensor muscle fatigue and motor skill acquisition task
Bibliographic record
Abstract
Cervical extensor muscle (CEM) fatigue alters upper limb proprioception and impairs the learning of an upper limb-tracking task (Zabihhosseinian et al., 2015). Somatosensory evoked potentials show that cerebellum disinhibition (CBI) is a characteristic response of motor skill learning, which is a process more recently shown to be altered by neck pain (Baarbe et al., 2015). While previous studies demonstrated these responses as changes in sensory processing, the impact on motor cortex output remains unknown. This study aimed to determine whether CEM fatigue alters the CBI response to motor skill acquisition. Sixteen healthy individuals were randomly assigned to either a CEM fatigue or control intervention. Double cone coil transcranial magnetic stimulation (TMS) was applied over the ipsilateral cerebellum 5 ms prior to contralateral stimulation of the primary motor cortex (M1) area supplying the first dorsal interosseous muscle. Cerebellar stimulation levels were delivered to create approximately 50% motor evoked potential inhibition (CBI50), as well as at 5 (CBI50+5) and 10% (CBI50+10) above CBI50. Both groups completed a novel motor tracing task using their right index finger before, immediately after, and 24-hours after experiencing five minutes of neck fatigue or rest. A significant effect of training showed greater disinhibition at CBI50 versus CBI50+10% (P < 0.008), and at CBI50+5% versus CBI50+10% (P < 0.009). Motor training lead to significant cerebellar disinhibition with no impact of CEM fatigue, indicating that the sensory changes reported previously (Zabihhosseinian, 2018) did not lead to the same changes in the cerebellar-M1 pathway.Acknowledgments: Natural Science and Engineering Research Council of Canada (NSERC), Ontario Graduate Scholarships, and University of Ontario Institute of Technology
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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 | 0.000 |
| 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".