Dorsolateral prefrontal and orbitofrontal cortex interactions during self-control of cigarette craving
Bibliographic record
Abstract
Drug-related cues induce craving, which may perpetuate drug use or trigger relapse in addicted individuals. Craving is also under the influence of other factors in daily life, such as drug availability and self-control. Neuroimaging studies using drug cue paradigms have shown frontal lobe involvement in this contextual influence on cue reactivity, but have not clarified how and which frontal area accounts for this phenomenon. We explored frontal lobe contributions to cue-induced drug craving under different intertemporal drug availability conditions by combining transcranial magnetic stimulation and functional magnetic resonance imaging in smokers. We hypothesized that the dorsolateral prefrontal cortex (DLPFC) regulates craving during changes in intertemporal availability. Subjective craving was greater when cigarettes were immediately available, and this effect was eliminated by transiently inactivating the DLPFC with transcranial magnetic stimulation. Functional magnetic resonance imaging demonstrated that the signal most proportional to subjective craving was located in the medial orbitofrontal cortex across all contexts, whereas the DLPFC most strongly encoded intertemporal availability information. The craving-related signal in the medial orbitofrontal cortex was attenuated by inactivation of the DLPFC, particularly when cigarettes were immediately available. Inactivation of the DLPFC also reduced craving-related signals in the anterior cingulate and ventral striatum, areas implicated in transforming value signals into action. These findings indicate that DLPFC builds up value signals based on knowledge of drug availability, and support a model wherein aberrant circuitry linking dorsolateral prefrontal and orbitofrontal cortices may underlie addiction.
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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 | 0.000 |
| 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".