Using neuroimaging to predict relapse to smoking: role of possible moderators and mediators
Bibliographic record
Abstract
BACKGROUND AND AIMS: Preclinical animal studies have established stressors, substance use associated cues, and priming as distinct triggers of relapse in substance dependence. These triggers seem to induce relapse by activating distinct brain pathways. In order to test these findings in humans, it is necessary to establish new human research paradigms. Neuroimaging may help to study brain regions involved in mediating the effects of these distinct triggers of relapse and to further delineate mediators of these pathways. In order to understand individual differences it is crucial to assess the impact of moderators on these pathways to relapse. METHODS: Paradigms to study distinct relapse triggers are currently being set up for tobacco dependence. It is practically impossible to study human relapse and specifically its neurobiological pathways in the natural surrounding. Instead we aim to establish vulnerability patterns in a laboratory environment, applying functional magnetic resonance imaging (fMRI) assessments during trigger exposure. Brain activation determined by fMRI may constitute a sensitive measure to assess responses to cues, stress, and priming. Establishing these paradigms will then allow to further delineate the role of possible mediators (e.g. attention, inhibition) and moderators (e.g. sex, genetic factors) underlying relapse to smoking. RESULTS: Initial results are encouraging, but this approach needs further studies to proof its usefulness. CONCLUSIONS: We outline an approach to study nicotine relapse within a laboratory environment, using fMRI assessments during trigger exposure. The long term goal is rational treatment development. To reach this goal it is crucial to identify, include and investigate critical moderators and mediators of relapse within this approach.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".