Adaptation in the motor system following movement imagery training is related to motor system activation during movement imagery
Bibliographic record
Abstract
Movement imagery (MI) is a cognitive motor process that shares neural networks with movement execution and observation. Previous research using transcranial magnetic stimulation (TMS) has demonstrated that both physical and observational training can elicit motor-cortical adaptations in the representation of movement (e.g. Classen et al., 1998; Stefan et al., 2005). This same effect has recently been demonstrated with MI (Yoxon & Welsh, submitted). These changes are thought to occur because the training potentiates (increases the excitability of) the representation of the trained movement. In support of this account, a positive relationship was reported between the magnitude of motor adaptations following observational training and the magnitude of corticospinal activation during action observation (i.e. the difference in amplitude of motor evoked potentials [MEPs] between rest and during an instance of action observation). The current experiment assessed this same relationship with MI training. The dominant direction of TMS-evoked thumb movements (i.e. flexion or extension) was determined before and after training. Single-pulse TMS was also used to determine the amplitude of MEPs during imagined flexion and extension of the thumb. During the training session, participants imagined themselves moving their thumb in the opposite direction of the pre-determined dominant direction. A strong positive relationship was found between corticospinal excitation during MI and both the change in the proportion of movements in the training direction. Consequently, it appears that the activation of the corticospinal system is strongly related to motor adaptations following MI training.Acknowledgments: This research was supported by grants and scholarships from the Natural Sciences and Engineering Research Council of Canada
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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".