Cue-induced Brain Activation and Relapse in Cigarette Smokers During Long-Term Smoking Cessation Treatment: A Prospective fMRI study
Bibliographic record
Abstract
Background: Studies investigating cue-induced brain activation as markers for cigarette use relapse have yet to be examined for their relevance in long-term cessation treatment. This feasibility functional magnetic resonance imaging (fMRI) investigation compared regions of cigarette cue-induced brain activation between cigarette smokers who relapsed versus those who abstained during a six-month intervention programme. Method: Eighteen adult cigarette smokers (>15x/day cigarette use >2 years) with tobacco use disorder completed a baseline fMRI cue exposure paradigm before undergoing treatment. Whole-brain fMRI contrasts between cue exposure conditions (cigarette, neutral) were assessed in patients who relapsed (≥1x cigarette use) compared to those who abstained during treatment. Subjective craving was assessed after each block. Results: Nine patients relapsed (38.9 ± 6.9 years old; 4F) and nine abstained (40.3 ± 7.4; 6F) from cigarette use. Relative to abstainers, patients who relapsed exhibited greater activation in parietal, fusiform, cingulate, prefrontal, orbitofrontal, and supplementary motor area regions. There were no group differences in craving. Conclusion: Cue-elicited brain activation associated with cigarette use relapse during treatment was observed in areas involved in value-driven attention. Cue-related neural activation in these areas may be potential vulnerability markers for cigarette use relapse during long-term interventions. Given the promising results in this small pilot, further investigations are warranted.
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) | 0.001 | 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".