The use of cannabis is rising globally; currently, 209 million cannabis users are reported worldwide, which is approximately twice the number reported at the beginning of the 2000s (Davis et al., 2018). Young adults (18–25 years old) exhibit the highest prevalence of cannabis use, which can be attributed to the legalization of the drug in certain regions and clinical research encouraging cannabis consumption (Mullins, 2013). While short-term cannabis use can provide euphoria or pain relief (Bonn-Miller et al., 2014), it can also increase the risk of psychosis significantly in adolescents (Tao et al., 2020). Previous research has found that the effects of cannabis on brain development in adolescents cause these neurological side effects (Zehra et al., 2018), which may be related to the main component of cannabis, tetrahydrocannabinol (THC), interacting with receptors of the endogenous cannabinoid signaling system, in turn leading to structural impairment of the brain (Zehra et al., 2018).

Prior research has indicated that widespread brain regions containing a dense density of cannabinoid receptor 1 (CB1), including the hippocampus, amygdala, cerebellum, cingulate cortex, and prefrontal cortex, are predominantly affected when adolescents are exposed to THC (Scott et al., 2019), and these regions are key components of the reward or emotional networks (Lorenzetti et al., 2019). Our previous longitudinal study revealed that the right hippocampus in young adults with cannabis use developed slowly related to that in controls (Xu et al., 2022). Additionally, gray matter volume of the orbitofrontal cortex was smaller in young cannabis users (Battistella et al., 2014, Price et al., 2015). However, a meta-analysis suggests that the structural brain abnormalities in cannabis users are currently not consistent (Lorenzetti et al., 2019), with the possible causes of focusing only on isolated brain regions, lacking of structural networks analysis, and single brain structure calculation method, all of which may lead to a narrower range of results without overall alteration of brain networks (Lorenzetti et al., 2019).

Further, the effect of recent cannabis use (RCU) on the human brain has been the subject of few studies. Individuals with RCU are defined as those with a positive cannabinoid urine screening test (THC + ), specifically, one and three days after a single use or three to four weeks on average for heavy users (Musshoff & Madea, 2006). Thus, the selection of young adult RCUs contributes to the estimation of the impact of early cannabis use on brain structure. Further, to explore the structural alterations in the brains of young adult cannabis users, the structural covariance networks (SCNs) is an effective approach that focuses on covariate coordinated structures of the whole brain in gray matter morphology rather than one specific structure (Gong et al., 2012). SCNs can usually be constructed from cortical surface area (CSA) and cortical thickness (CT), CSA reflects the unfolding of cerebral cortex, and CT reflects the density and distribution of neuron cells(Winkler et al., 2018), which jointly comprise gray matter volume, with the advantage that CSA or CT may appear abnormal when no abnormality is detected in gray matter volume (Ducharme et al., 2016, Winkler et al., 2018). Moreover, because of distinct cellular regulatory mechanisms in CSA or CT, their responses to particular factors affecting the brain may differ (Evans, 2013). In addition, brain networks can provide information about interregional connectivity and can be used to distinguish whether functional networks are receiving effects based on changes of graph theory metrics, this can balance the network in whole and its nodes (Nestor et al., 2020), which can help to complement the gray matter changes which have been reported. However, no research has been reported in this direction.

Hence, this study constructed SCNs using CT and CSA from the Human Connectome Project (HCP) imaging data to calculate separation and integration network graph theory metrics in young adults with RCU. This study aimed to (i) detect regions of whole-brain SCNs abnormality in young adults with RCU compared to non-cannabis users and (ii) identify abnormal network architectures of whole-brain SCNs in young adults with RCU.