Can training of a skilled pelvic movement change corticomotor control of back muscles? Comparison of single and paired‐pulse transcranial magnetic stimulation
Bibliographic record
Abstract
Evidence suggests excitability of the motor cortex (M1) changes in response to motor skill learning of the upper limb. Few studies have examined immediate changes in corticospinal excitability and intra-cortical mechanisms following motor learning in the lower back. Further, it is unknown which transcranial magnetic stimulation (TMS) paradigms are likely to reveal changes in cortical function in this region. This study aimed to (1) compare corticospinal excitability and intra-cortical mechanisms in the lower back region of M1 before and after a single session of lumbopelvic tilt motor learning task in healthy people and (2) compare these measures between two TMS coils and two methods of recruitment curve (RC) acquisition. Twenty-eight young participants (23.6 ± 4.6 years) completed a lumbopelvic tilting task involving three 5-min blocks. Single-pulse (RC from 70% to 150% of active motor threshold) and paired-pulse TMS measures (ICF, SICF and SICI) were undertaken before (using 2 coils: figure-of-8 and double cone) and after (using double cone coil only) training. RCs were also acquired using a traditional and rapid method. A significant increase in corticospinal excitability was found after training as measured by RC intensities, but this was not related to the RC slope. No significant differences were found for paired-pulse measures after training. Finally, there was good agreement between RC parameters when measured with the two different TMS coils or different acquisition methods (traditional vs. rapid). Changes in corticospinal excitability after a single session of lumbopelvic motor learning task are seen, but these changes are not explained by changes in intra-cortical mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".