Abnormal structural covariance networks in young adults with recent cannabis use

Recent cannabis use (RCU) exerts adverse effects on the brain. However, the effect of RCU on structural covariance networks (SCNs) is still unclear. This retrospective cross-sectional study aimed to explore the effects of RCU on SCNs in young adults in terms of whole cerebral cortical thickness (CT) and cortical surface area (CSA).

A total of 117 participants taking tetrahydrocannabinol (RCU group) and 896 participants not using cannabis (control group) were included in this study. All participants underwent MRI scanning following urinalysis screening, after which FreeSurfer 5.3 was used to calculate the CT and CSA, and SCNs matrices were constructed by Brain Connectivity Toolbox. Subsequently, the global and nodal network measures of the SCNs were computed based on these matrices. A nonparametric permutation test was used to investigate the group differences by Matlab.

Regarding global network measures of CT, young adults with RCU exhibited altered small-worldness (P = 0.020) and clustering coefficient (P = 0.031) compared to controls, whereas there were no significant group differences in terms of SCNs constructed with CSA. Additionally, SCNs based on CT and CSA displayed abnormal nodal degree, nodal efficiency, and nodal betweenness centrality in vital brain regions of the triple network, including the dorsolateral and ventrolateral prefrontal cortex, and anterior cingulate cortex.

The effects of RCU on brain structure in young adults can be detected by SCNs, in which structural abnormalities in the triple network are dominant, indicating that RCU can be detrimental to brain function.