Functional Connectivity of the Raphe Nuclei: Link to Tobacco Withdrawal in Smokers
Bibliographic record
Abstract
Background: Although nicotine alters serotonergic neurochemistry, clinical trials of serotonergic medications for smoking cessation have provided mixed results. Understanding the role of serotonergic dysfunction in tobacco use disorder may advance development of novel pharmacotherapies. Methods: Functional magnetic resonance imaging was used to measure resting-state functional connectivity of the raphe nuclei as an indicator of serotonergic function. Connectivity of the dorsal and median raphe nuclei was compared between 18 young smokers (briefly abstinent, ~40 minutes post-smoking) and 19 young nonsmokers (16-21 years old); connectivity was also examined in a separate sample of overnight-abstinent smokers (18-25 years old), before and after smoking the first cigarette of the day. Relationships between connectivity of the raphe nuclei with psychological withdrawal and craving were tested in smokers. Results: Connectivity of the median raphe nucleus with the right hippocampal complex was weaker in smokers than in nonsmokers and was negatively correlated with psychological withdrawal in smokers. In overnight-abstinent smokers, smoking increased connectivity of the median raphe nucleus with the right hippocampal complex, and the increase was positively correlated with the decrease in psychological withdrawal. Conclusions: Relief of withdrawal due to smoking is potentially linked to the serotonergic pathway that includes the median raphe nucleus and hippocampal complex. These results suggest that serotonergic medications may be especially beneficial for smokers who endorse strong psychological withdrawal during abstinence from smoking.
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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 | 0.001 |
| 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) | 0.002 | 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".