T53. AN EFFECT-SIZE META-ANALYSIS OF WHITE MATTER DAMAGE RELATED TO CANNABIS USE: RELEVANCE TO THE ANATOMY OF PSYCHOSIS
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,045 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».