Effects of incisor extraction on jaw and tongue motor representations within face sensorimotor cortex of adult rats
Bibliographic record
Abstract
Loss of teeth is associated with changes in somatosensory inputs and altered patterns of mastication, but it is unclear whether tooth loss is associated with changes in motor representations within face sensorimotor cortex of rats. We used intracortical microstimulation (ICMS) and recordings of cortically evoked muscle electromyographic (EMG) activities to test whether changes occur in the ICMS-defined motor representations of the left and right jaw muscles [masseter, anterior digastric (LAD, RAD)] and tongue muscle [genioglossus (GG)] within the cytoarchitectonically defined face primary motor cortex (face-M1) and adjacent face primary somatosensory cortex (face-S1) 1 week following extraction of the right mandibular incisor in anesthetized (ketamine-HCl) adult male Sprague-Dawley rats. Under local and general anesthesia, an "extraction" group (n = 8) received mucoalveolar bone surgery and extraction of the mandibular right incisor. A "sham-extraction" group (n = 6) received surgery with no extraction. A "naive" group (n = 6) had neither surgery nor extraction. Data were compared by using mixed-model repeated-measures ANOVA. Dental extraction was associated with a significantly increased number of sites within face-M1 and face-S1 from which ICMS evoked RAD EMG activities, a lateral shift of the RAD and LAD centers of gravity within face-M1, shorter onset latencies of ICMS-evoked GG activities within face-M1 and face-S1, and an increased number of sites within face-M1 from which ICMS simultaneously evoked RAD and GG activities. Our novel findings suggest that dental extraction may be associated with significant neuroplastic changes within the rat's face-M1 and adjacent face-S1 that may be related to the animal's ability to adapt to the altered oral state.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".