Cigarette smoking is associated with thinner cingulate and insular cortices in patients with severe mental illness
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging (MRI) studies show reduced cortical thickness in patients with schizophrenia and bipolar disorder. These subtle brain abnormalities may provide insight into illness mechanisms. However, environmental and lifestyle-related factors, such as cigarette smoking, may contribute to brain structure changes. Cigarette smoking is highly prevalent in patients with severe mental illness. In nonpsychiatric samples, smoking has been associated with reduced thickness in the anterior (ACC) and posterior cingulate cortices, the insular cortex (INS), the dorsolateral prefrontal cortex and the orbitofrontal cortex. METHODS: We examined MRI scans from patients with schizophrenia, other psychotic disorders or bipolar disorder and healthy controls using FreeSurfer. RESULTS: We included 506 patients (49% smokers) and 237 controls (20% smokers) in our study. We found reduced cortical thickness in the left rostral ACC and the left INS in smoking patients compared with nonsmoking patients, but this difference was not found among healthy controls. No dose-response relationship was found between amount of smoking and cortical thickness in these regions. Among patients, maps of thickness along the whole cortical surface revealed reduced insular thickness but no effects in other regions. Among healthy controls, similar analyses revealed increased age-related cortical thinning in the left occipital lobe among smokers compared with nonsmokers. LIMITATIONS: The causal direction could not be determined owing to the cross-sectional design and lack of detailed data on smoking addiction and smoking history. CONCLUSION: The effect of cigarette smoking should be considered in MRI studies of patients with severe mental illness.
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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 | 0.001 | 0.001 |
| 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".