Pain, No Gain: Acute Pain Interrupts Motor Imagery Processes and Affects Mental Training-Induced Plasticity
Bibliographic record
Abstract
Pain influences both motor behavior and neuroplastic adaptations induced by physical training. Motor imagery (MI) is a promising method to recover motor functions, for instance in clinical populations with limited endurance or concomitant pain. However, the influence of pain on the MI processes is not well established. This study investigated whether acute experimental pain could modulate corticospinal excitability assessed at rest and during MI (Exp. 1) and limit the use-dependent plasticity induced by MI practice (Exp. 2). Participants imagined thumb movements without pain or with painful electrical stimulations applied either on digit V or over the knee. We used transcranial magnetic stimulation to measure corticospinal excitability at rest and during MI (Exp. 1) and to evoke involuntary thumb movements before and after MI practice (Exp. 2). Regardless of its location, pain prevented the increase of corticospinal excitability that is classically observed during MI. In addition, pain blocked use-dependent plasticity following MI practice, as testified by a lack of significant posttraining deviations. These findings suggest that pain interferes with MI processes, preventing the corticospinal excitability facilitation needed to induce use-dependent plasticity. Pain should be carefully considered for rehabilitation programs using MI to restore motor function.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".