Motor Imagery Improves Force Control in Older and Young Females
Bibliographic record
Abstract
Abstract Motor imagery training (MIT) is the mental rehearsal of a motor task with no overt movement that enhances physical performance through adaptations in neural excitability. MIT may prime the motor system for physical execution. In older adults, with physical practice, force steadiness (FS) improves and changes are related to improved performance of functional tasks, and associated with adaptations in neural excitability. The purpose of this study was to determine if one session of MIT influences corticospinal excitability and improves FS of isometric elbow flexion contractions in young and older female adults. To test the hypotheses that MIT would increase corticospinal excitability and improve isometric elbow flexion FS to a greater extent in older compared to young females fourteen older (67-89 years old) and twenty-two younger (19-33 years old) participants were randomly assigned to a MIT group or Control group. Participants, in a block design, performed isometric elbow flexion contractions at 10% of maximal force prior to and following MIT (training group) or no training (Control group). Elbow flexion contractions were performed in blocks 1, 3, and 5. MIT or documentary viewing was performed in blocks 2 and 4. Motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation were collected within the last five seconds of each submaximal contraction. The MEPs were reduced in the older MIT group from block 1 to block 5 (p=0.039) but not the young MIT group (p=0.761). Force steadiness in the older (p=0.005) and young (p=0.001) females improved from baseline after 20 minutes of MIT. Older females improved force steadiness relative to the baseline to a greater extent than young females (older 8.44% and young 5.0%), and the improvements were significant in older females in the first 10 minutes. In older females, MIT primes the motor system and improves FS earlier and to a greater extent than in young females.
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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) | 0.003 | 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".