Increased Connectivity Between the Nucleus Accumbens and the Default Mode Network in Patients With Schizophrenia During Cigarette Cravings
Bibliographic record
Abstract
Objective: Compared to the general population, tobacco smoking cessation rates are lower in populations with schizophrenia. Unfortunately, the potential neurophysiologic mechanisms underlying these low cessation rates in schizophrenia have been seldom studied using functional neuroimaging. Recently, it has been shown that tobacco cravings are increased in smokers with schizophrenia compared to smokers with no comorbid psychiatric disorder. Given the critical role of the brain reward system in the neurobiology of addiction, we sought to examine the functional connectivity of core regions of this system in smokers with schizophrenia during the viewing of appetitive smoking cues. Methods: Smokers with (n = 18) and without (n = 24) schizophrenia were scanned using functional magnetic resonance imaging while viewing appetitive cigarette images. Functional connectivity analyses were performed using the bilateral nucleus accumbens as the seed regions. Results: Smokers with schizophrenia and smokers with no psychiatric comorbidity did not differ in subjective cravings in response to appetitive smoking cues. However, in smokers with schizophrenia relative to control smokers, we found an increased connectivity between the nucleus accumbens and regions involved in the default mode network (e.g., middle temporal gyrus and precuneus), which are involved in self-referential processes. Moreover, a positive correlation was observed between the left nucleus accumbens and left middle temporal gyrus connectivity and cigarette cravings across both groups of smokers. Conclusions: These results highlight a key role of the nucleus accumbens in cigarette craving in schizophrenia and suggest that the subjective valuation of cigarette cues is increased in this population. Similar neurofunctional studies on cravings for other psychoactive substances in schizophrenia are warranted.
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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".