Effect of Cigarette Smoking and Alcohol Use on White Matter Tract Integrity
Bibliographic record
Abstract
Background:Cigarette smoking (CS) is highly comorbid with alcohol use disorder (AUD). Both are associated with lower white matter (WM) integrity, with potentially additive effects. This study is a starting point to determine the individual and combined effects of CS and AUD on WM integrity. Methods:Thirty subjects with varying CS and alcohol use (40.0 ± 12.9 years, 13 females, 17 males) underwent structural (T1-weighted) and diffusion weighted magnetic resonance imaging. Indices of WM integrity, fractional anisotropy (FA) and mean diffusivity (MD), were calculated at a voxel-wise level. Parametric maps of FA and MD were spatially normalized to Montreal Neurological Institute space. Average FA and MD values were extracted for 48 WM tracts from the Johns Hopkins University WM tract atlas. Alcohol drinking and CS were characterized by: DSM-5 AUD symptom checklist, Obsessive Compulsive Drinking Scale (OCDS, including obsessive and compulsive subscales), Timeline Followback (TLFB; drinks/drinking day and drinks/week), Alcohol Use Disorders Identification Test (AUDIT), Fagerstrom Test for Nicotine Dependence (FTND), self-reported pack years, and cigarettes/day. Linear regression was performed between FA and MD with alcohol and CS metrics. Results:We found significant negative correlations (p<0.01) between bilateral cingulate gyri FA and total OCDS score and subscales, drinks/drinking day, drinks/week, and AUDIT. Right inferior cerebellar peduncle FA was negatively correlated with FTND and cigarettes/day. Bilateral superior cerebellar peduncle MD was positively correlated with FTND and cigarettes/day. Left cingulate hippocampus MD was positively correlated with OCDS compulsivity. Conclusion:Alcohol use was negatively correlated with cingulate gyrus WM FA, which is implicated in goal-directed behavior and salience attribution. CS was negatively correlated with cerebellar peduncle FA; however, the interpretation of this is unclear. Our results support the hypothesis that both CS and AUD negatively impact WM integrity. Future work will determine the potentially additive effects of smoking and alcohol use.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".