Craving-Induced EEG Reactivity in Smokers: Effects of Mood Induction, Nicotine Dependence and Gender
Bibliographic record
Abstract
BACKGROUND/AIMS: Cigarette craving is a core symptom of smoking withdrawal, which is more intense and more frequently observed in smokers with depressed mood. Using self-reports and electroencephalographic (EEG) indices of frontal hemispheric asymmetry, which has been shown to be sensitive to mood states, the purpose of this study was to investigate the neural basis of cue-elicited cigarette craving, its variation with experimentally induced depressed mood, and with differences in gender and smoker type. METHODS: Cigarette-cue reactivity was examined in 11 (5 male) regular and 11 (6 male) light smokers in two sessions involving the induction of neutral or depressed mood. RESULTS: Frontal EEG alpha asymmetry changes reflecting left frontal hypoactivation were evident with cigarette-cue exposure, particularly in female smokers. During cigarette-cue exposure, EEG evidenced both decreases and increases in brain state activation, with the latter activational increments also being influenced by depressed mood. Exposure to the cigarette cue, in addition to increasing withdrawal symptoms, increased cravings and negative affect, these latter effects being more evident in female and regular smokers. CONCLUSION: These findings, which appear to provide a physiological basis for 'withdrawal-like' negative affective experiences during craving, are discussed in relation to theories of drug reinforcement and smoking motivation.
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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".