Nicotine dependence and functional connectivity of insular cortex subregions
Bibliographic record
Abstract
Abstract The insular cortex has been identified as a promising target in brain-based therapies for Tobacco Use Disorder, and has three major sub-regions (ventral anterior, dorsal anterior, and posterior) that serve distinct functional networks. How these subregions and associated networks contribute to nicotine dependence has not been well understood, and therefore was the subject of this study. Forty-seven individuals (24 women; 18-45 years old) who smoked cigarettes daily rated their dependence using the Fagerström Test for Nicotine Dependence (FTND), abstained from smoking overnight (~12 h), and underwent resting-state functional MRI. Correlations between dependence and resting-state functional connectivity (RSFC) of the major insular sub-regions were evaluated using whole-brain-corrected voxel-wise analyses and post-hoc region-of-interest (ROI) analyses. Dependence was analyzed both as a unitary (FTND total score) and bivariate construct – two FTND factors (“morning smoking” and “daytime smoking”). Dependence was negatively correlated with connectivity of both the right dorsal and left ventral anterior insula with the left precuneus, and with connectivity of the left posterior insula to the left putamen. In post-hoc analyses, dependence correlated negatively with connectivity between all anterior insula subregions and the left precuneus, and with bilateral posterior insula connectivity with the left posterior putamen. The latter finding was driven by “daytime smoking”. These results suggest an anterior-posterior distinction in functional insular networks associated with different dimensions of nicotine dependence, with greater dependence linked to weaker connectivity. They may inform therapeutic approaches involving brain stimulation that may elicit differential clinical outcomes depending on the insular subnetwork targeted.
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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".