Motor cortex neuroplasticity associated with lingual nerve injury in rats
Bibliographic record
Abstract
The aim of this study was to determine if lingual nerve trauma affects the features of face primary motor cortex (MI) defined by intracortical microstimulation (ICMS). The left lingual nerve was transected in adult male rats by an oral surgical procedure; sham rats (oral surgery but no nerve transection) as well as naive intact rats served as control groups. ICMS was applied at post-operative days 0, 7, 14, 21, and 28 to map the jaw and tongue motor representations in face MI by analyzing ICMS-evoked movements and electromyographic activity recorded in the genioglossus (GG) and anterior digastric (AD) muscles. There were no statistically significant effects of acute (day 0) nerve transection or sham procedure (p > 0.05). The surgery in the sham animals was associated with limited post-operative change; this was reflected in a significant (p < 0.05) increase in the number of GG sites in left MI at post-operative day 14 compared to day 0. However, nerve transection was associated with significant increases in the total number of AD and GG sites in left or right MI or specifically the number of GG sites in rats at post-operative days 21 or 28 compared to earlier time periods. There were also significant differences between nerve-transected and sham groups at post-operative days 7, 14, or 21. These findings suggest that lingual nerve transection is associated with significant time-dependent neuroplastic changes in the tongue motor representations in face MI.
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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) | 0.001 | 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 | 0.001 |
| 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".