Widespread reductions in cortical thickness following ketamine abuse
Bibliographic record
Abstract
BACKGROUND: Esketamine is a version of ketamine that has been approved for treatment-resistant depression, but our previous studies showed a link between non-medical use of ketamine and brain structural and functional alterations, including dorsal prefrontal grey matter reduction among chronic ketamine users. In this study, we sought to determine cortical thickness abnormalities following long-term, non-medical use of ketamine. METHODS: We acquired structural brain images for patients with ketamine use disorder and drug-free healthy controls. We used FreeSurfer software to measure cortical thickness for 68 brain regions. We compared cortical thickness between the 2 groups using analysis of covariance with covariates of age, gender, educational level, smoking, drinking, and whole-brain mean cortical thickness. RESULTS: We included images from 95 patients with ketamine use disorder and 169 controls. Compared with healthy controls, patients with ketamine use disorder had widespread decreased cortical thickness, with the most extensive reductions in the frontal (including the dorsolateral prefrontal cortex) and parietal (including the precuneus) lobes. Increased cortical thickness was not observed among ketamine users relative to comparison participants. Estimated total lifetime ketamine consumption was correlated with reductions in the right inferior parietal and the right rostral middle frontal cortical thickness. LIMITATIONS: We conducted a retrospective cross-sectional study, but longitudinal studies are needed to further validate decreased cortical thickness after nonmedical use of ketamine. CONCLUSION: This study provided evidence that, compared with healthy controls, chronic ketamine users have widespread reductions in cortical thickness. Our study underscores the importance of the long-term effects of ketamine on brain structure and serves as a reference for the antidepressant use of ketamine.
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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 | 0.000 |
| 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".