T53. AN EFFECT-SIZE META-ANALYSIS OF WHITE MATTER DAMAGE RELATED TO CANNABIS USE: RELEVANCE TO THE ANATOMY OF PSYCHOSIS
Bibliographic record
Abstract
Cannabis appears to have a distinct effect on cortical and subcortical white matter, possibly mediated by altered oligodendrocyte function. This may have cognitive implications especially in patients with psychosis. We investigated whether this myelin-altering effect of cannabis is more likely to be concentrated in specific white matter tracts of the brain, especially those that have a high likelihood of being altered by the presence of schizophrenia. We undertook a coordinates-based effect size meta-analysis to locate tracts that are maximally affected by cannabis as well as the diagnosis of schizophrenia and studied the overlapping effects if these two factors. Using the key words ((diffusion AND (weighted or tensor)) AND ((substance OR drug OR alcohol OR cannabis) AND (use OR users OR abuse OR dependence))) in Medline we included the studies that met the following criteria: (1) used DW-MRI, and reported differences in fractional anisotropy (FA) (2) used a voxelwise or tract-based spatial statistic (TBSS) approach, (3) reported between-group comparisons of a defined group of cannabis users vs. non-users, irrespective of dose (4) published in English in a peer-reviewed journal, (5) published after 1990. The authors were then contacted to collect any relevant information. Effect-Size Signed Differential Mapping (ES-SDM) was used as the meta-analytical method of choice, as it combines peak co-ordinates and statistical parametric maps. A jackknife analysis was employed to determine the reproducibility of the results by iterating the mean analysis several times, each time excluding a specific study from the analysis. A heterogeneity analysis generated the Q statistic to assess the between-study variability in the results. Meta-regressions were conducted to study the sources of between-studies variability with age, gender, handedness, alcohol comorbidity, and MRI strength as predictors of interest. Of the 118 studies identified through PubMed, 75 were excluded in the initial stage as they did not involve cannabis. 43 abstracts were then assessed, and any studies using an ROI based framework were excluded. 10 studies, (5 VBA and 5 TBSS), comprising of data from 223 cannabis-users and 182 non-using controls were pooled. All except one study reported significant between-group differences. Seven regions show significant reduction in the FA in the cannabis user group and only one region showing a significant increase in FA compared to the non-users. The corpus callosum, right pons, left inferior network (inferior longitudinal fasciculus), left cortico-spinal projections, right superior longitudinal fasciculus III, left anterior thalamic projections, and the left frontal orbito-polar tract all showed FA reduction in cannabis users. The right inferior fronto-occipital fasciculus showed higher FA in cannabis users. Jackknife sensitivity analyses indicated that the deficit in the corpus callosum (MNI coordinates 14,-42,16, z=1.899, p=0.00001) was especially robust, withstanding the removal of any of the 10 studies. We have demonstrated that the corpus callosum is the most vulnerable brain region to the effects of recreational cannabis use. While we noted a significant between-studies heterogeneity, this was not explained by age, gender distribution or comorbid alcohol use in the samples. In long term cannabis users, longitudinal tracking of the integrity of corpus callosum can provide valuable insights to the relationship with emergence of psychosis as well as cognitive impairment related to the use of cannabis.
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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.045 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".