Enhanced corticobulbar excitability in chronic smokers during visual exposure to cigarette smoking cues
Bibliographic record
Abstract
BACKGROUND: Neuroimaging studies of chronic smokers report altered activity of several neural regions involved in the processing of rewarding outcomes. Neuroanatomical evidence suggests that these regions are directly connected to the tongue muscle through the corticobulbar pathways. Accordingly, we examined whether corticobulbar excitability might be considered a somatic marker for nicotine craving. METHODS: We compared motor-evoked potential (MEP) amplitudes recorded from the tongue and the extensor carpi radialis (control muscle) of chronic smokers under drug withdrawal and intake conditions as well as a nonsmoker group. All participants were tested during passive exposure to pictures showing a smoking cue or a meaningless stimulus. In the intake condition, chronic smokers were asked to smoke a real cigarette (CSn: group 1) or a placebo (CSp: group 2). RESULTS: Results show that MEP amplitudes recorded from the tongues of participants in the CSn and CSp groups under the withdrawal condition were selectively enhanced during exposure to a smoking cue. However, this effect on tongue MEP amplitudes disappeared in the intake condition for both the CSn and CSp groups. LIMITATIONS: Limitations include the fact that the study was conducted in 2 different laboratories, the small sample size, the absence of data on chronic smoker craving strength and the different tastes of the real and placebo cigarettes. CONCLUSION: These results suggest that, in chronic smokers, tongue muscle MEP amplitudes are sensitive to neural processes active under the physiological status of nicotine craving. This finding implicates a possible functional link between neural excitability of the corticobulbar pathway and the reward system in chronic smokers.
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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) | 0.002 | 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".