Effect of Local Anesthesia on Trigeminal Somatosensory-evoked Magnetic Fields
Bibliographic record
Abstract
For objective neurophysiological evaluation of the function of the trigeminal system, magnetoencephalography- based TSEF (trigeminal somatosensory-evoked field) assessment would be valuable in providing spatial and temporal profiles of cortical responses. However, this necessitates knowledge of how TSEF varies with trigeminal nerve dysfunctions. We introduced a conduction block of the trigeminal nerve using local anesthesia (lidocaine) to temporally mimic nerve dysfunctions, and monitored TSEF changes. Following an electrical stimulation of the lower lip, a magnetic response with peak latency of approximately 20 ms was identified in all participants. Dipole for the peak was estimated on the post-central gyrus in the participant's own magnetic resonance image. After normalization to Montreal Neurological Institute (MNI) space and inter-participant data integration, the summary equivalent current dipole localization among participants remained in the post-central gyrus, suggesting validity of the use of MNI space. Partial anesthesia of the lower lip led to a loss of the waveform characteristics of TSEF for electrical stimulation to the trigeminal nerve. We verified that the 20-ms latency cortical response of TSEF components localized at the primary sensory cortex can serve as a robust neurofunctional marker of experimental trigeminal nerve dysfunction.
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) | 0.003 | 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".