Neural basis of smoking‐induced relief of craving and negative affect: Contribution of nicotine
Bibliographic record
Abstract
Smoking-induced relief of craving and withdrawal promotes continued cigarette use. Understanding how relief is produced and the role of nicotine in this process may facilitate development of new smoking-cessation therapies. As the US Food and Drug Administration considers setting a standard for reduced nicotine content in cigarettes to improve public health, knowledge of how nicotine contributes to relief also can inform policy. We assessed effects of nicotine using resting state functional magnetic resonance imaging (MRI) and behavioral assessments of craving and negative affect. Twenty-one young (18-25 years old) daily smokers underwent overnight abstinence on 4 days. On each of the following mornings, they self-rated their cigarette craving and negative affect and underwent resting-state functional MRI (fMRI) before and after smoking a cigarette that delivered 0.027, 0.110, 0.231, or 0.763 mg of nicotine. Functional connectivity between the anterior insula and anterior cingulate cortex (ACC) and between the nucleus accumbens and orbitofrontal cortex (OFC) was assessed. Smoking reduced craving, negative affect, and nucleus accumbens-OFC connectivity irrespective of nicotine dose, with positive correlations of the effects on behavioral and connectivity measures. Only the highest nicotine dose (0.763 mg) reduced right anterior insula-ACC connectivity; this reduction was positively correlated with the behavioral effects of the 0.763-mg dose only. While nicotine-based therapies may act on right anterior insula-ACC functional circuits to facilitate smoking cessation, non-nicotine (eg, the conditioned and sensorimotor) aspects of smoking may promote cessation by reducing OFC-accumbens connectivity to alleviate withdrawal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".