We investigated the association between CUD and measures of cognition, brain structure, and brain function in a large sample selected without regard to cannabis use status. Consistent with prior studies, CUD individuals showed poorer performance on measures of verbal intelligence, verbal memory, working memory, and motor speed. We did not find conclusive evidence for lower FA in the CUD group. Functional connectivity (FC) was altered in a component that included lower FC between the bilateral superior parietal gyrus (SPG) and right insula. In contrast, there was substantial evidence that neither neuroanatomical measures nor did graph‐theoretical metrics of FC differed between groups. Results were not related to alcohol drinking behavior, AUD, or SUD, although nicotine dependence explained a portion of the variance in FC.

Apart from acute effects (Ranganathan et al., 2017; Schuster et al., 2018), the causal link between cannabis use and educational attainment (Defoe, Khurana, Betancourt, Hurt, & Romer, 2018; Lynskey & Hall, 2000), or cognition is unclear (Morin et al., 2019; Scott, Slomiak, et al., 2018). A study on a cannabinoid receptor 1 (CB1) antagonist suggests that altered CB1‐signaling is involved in acute THC‐induced verbal memory impairment (Englund et al., 2016). Frequent cannabis use is associated with a downregulation of these receptors, but receptor density returns to baseline after abstinence (D'Souza et al., 2016; Hirvonen et al., 2012). Similarly, cognitive impairments (Scott, Slomiak, et al., 2018; Tait, Mackinnon, & Christensen, 2011) and FC (Blanco‐Hinojo et al., 2017) are recovered after abstinence, suggesting a causative role. However, longitudinal twin studies show that individuals at risk for substance abuse could have cognitive vulnerabilities before onset of cannabis use (Jackson et al., 2016; Meier et al., 2018; Ross et al., 2019), although there is also evidence from a longitudinal study for cognitive effects after persistent use (Meier et al., 2012). When correcting for education, many of the cognitive and FA differences between CUD and non‐CUD disappeared, except for verbal memory and FC. Verbal memory is one of the most robustly found domains to be affected in relation to cannabis use (Broyd et al., 2016; Crane et al., 2013). Possibly, these metrics may be associated with CUD through a different pathway.

The absence of significant morphometric differences associated with CUD is consistent with previous large studies on recreational cannabis use (Orr et al., 2016; Scott, Rosen, et al., 2018; Thayer et al., 2017), with similar effect sizes (Scott, Rosen, et al., 2018; Weiland et al., 2015). Our findings add to the literature that morphometric differences are absent even when individuals who meet CUD diagnostic criteria are compared with non‐users from the same population. However, a few recent large studies did find morphometric differences in association with cannabis use. Chye, Lorenzetti, et al. (2019); Chye, Suo, et al., 2019) found smaller hippocampi in CUD compared with controls and compared with nondependent users, but not in nondependent users compared with controls. Owens et al. (2020) found smaller hippocampi in THC+ participants, but not in participants with lifetime CUD, whereas Manza et al. (2019) found lower cortical thickness and gray matter density in the precuneus in lifetime CUD compared with carefully matched controls. These differences in results and sample characteristics suggest that different phenotypes (e.g., THC+, current vs. lifetime CUD, frequency of usage, etc.) may have different associations with brain morphometry. In addition, it emphasizes the need for replication studies.

Although the literature is consistent in reporting lower structural connectivity in participants with CUD (Arnone et al., 2008; Ashtari et al., 2009; Becker et al., 2015; Epstein & Kumra, 2015; Gruber et al., 2014; Jacobus et al., 2009; Jakabek et al., 2016; Manza et al., 2019; Murray et al., 2017; Orr et al., 2016; Shollenbarger et al., 2015; Zalesky et al., 2012), we did not find conclusive evidence for lower FA in the CUD group (0.3 < BF < 3). Functional connectivity studies, however, are more inconsistent. Studies using a whole brain approach to investigate CUD‐related resting state FC (Cheng et al., 2014; Filbey et al., 2018; C. Orr et al., 2013; Thijssen et al., 2017) have reported that cannabis users have: higher fractional amplitude of low‐frequency fluctuations in right superior frontal gyrus, SPG, semilunar node of the cerebellum, and weaker interhemispheric connectivity in the medial frontal cortex and pyramid of the cerebellum (C. Orr et al., 2013); stronger connectivity between frontal and precentral gyri, and between frontal and cingulate gyri (Cheng et al., 2014); weaker connectivity in the salience network and posterior cingulate gyrus (Filbey et al., 2018). The largest study (130 CUD, 47 controls) found no cannabis‐use associated differences in intra‐network connectivity but did report a negative association between duration of cannabis use and connectivity of the executive control network with the auditory and a sensorimotor network in cannabis users (Thijssen et al., 2017). We did not find differences in inter‐network connectivity of data processed with global signal regression (GSR); in non‐GSR data there could be (evidence was inconclusive) stronger inter‐network FC between the cingulo‐parietal (CP) and auditory network, and between the CP and somato‐motor network in CUD. These inconsistent results, emphasized by the difference between results with and without GSR, illustrate the need for more standardized methodological approaches and resist drawing generalized conclusions.

We report weaker FC between bilateral motor‐hand regions in the SPG and the bilateral insula in the NBS‐intensity and FDR approach, even after correcting for several confounders. In contrast, the NBS‐extent finding of stronger FC between nodes in the occipital cortex reached only marginal significance and did not survive correction for confounding variables. Although without GSR there were no group differences in NBS networks, the global signal (as inferred from mean FC over the entire matrix) was higher in the CUD group. Our results suggest that after removal of this signal, the CUD group still had different FC between specific regions. It should be stressed that the multiple comparison correction of NBS is at the component‐level rather than the edge‐level, meaning that the null hypothesis is not rejected for any specific edge, only for the component as a whole. The edges between the right SPG and right insula which reach FDR significance are not subject to this limitation. However, these findings were attenuated when controlling for nicotine dependence. A study on nicotine addiction reported that weaker FC between the insula (seed region) and pre‐ and postcentral gyri was related to higher chances of relapse (Addicott, Sweitzer, Froeliger, Rose, & McClernon, 2015). Although it is likely that these connections are not unique to nicotine addiction but substance abuse related (c.f. the involvement of the insula in drug craving [Naqvi, Gaznick, Tranel, & Bechara, 2014]), our results might be influenced by the higher prevalence of nicotine smokers in the CUD group. Taking that into account in combination with the marginal significance, our FC results should be treated with caution and replication is needed.

We did not find conclusive evidence for the presence or absence of a correlation between age of onset and duration of use and cognition or FC. The literature is also inconclusive on this association as some studies report that earlier onset and longer use have a more detrimental effect on cognition and the brain, whereas other studies report an absence of such association. For a comprehensive overview we refer the reader to (Nader & Sanchez, 2018). Discrepancies in the literature could be due to the definition of “cannabis users,” the fidelity of self‐report (Prince, Conner, & Pearson, 2018), or that early‐onset users may have a higher intensity and frequency of use compared with late‐onset users (Gruber, Sagar, Dahlgren, Racine, & Lukas, 2012). Additionally, THC content in cannabis preparations has increased from 3 to 12% between 1980 and 2014 (ElSohly et al., 2000; ElSohly et al., 2016). Although users may self‐titrate their desired level of THC consumption (Bidwell et al., 2018), this makes the cumulative THC intake difficult to estimate and compare. Last, specific timing of high frequency and/or high potent cannabis use may have a differential effect on outcomes. Frequency and potency of use could differ while age of onset is the same; more frequent use during adolescence versus (early) adulthood is likely to have more detrimental effects on cognition and the brain (Volkow et al., 2014, but see Meier et al., 2019) given that adolescence is a vulnerable period of educational opportunities and brain maturation. Future research should attempt to take these dynamics of cannabis use into account.

This study has several limitations. First, causality cannot be inferred from cross‐sectional studies. Second, we lack detailed information such as days since last use and intensity of recent use (with e.g., timeline follow‐back, hair analyses). As such, it is not clear whether the findings reported here are due to current THC‐bioavailability, long‐term changes, or premorbid vulnerability to CUD. However, a standardized DSM diagnosis can be seen as a more reliable phenotype than (long‐term) frequency of use or cumulative lifetime use (Prince et al., 2018). It is also important to realize that CUD captures those people who not just use, but where use leads to problems, which is potentially a different phenotype regardless of amount of use. Third, results of the resting state analysis could be specific to the parcellation atlas, as low resolution parcellations can lead to unstable estimations. However, individual differences in graph metrics were largely conserved in parcellations with more than 250 regions (Fornito, Zalesky, & Bullmore, 2010), suggesting our findings on a 333‐region cortical parcellation should be stable. Fourth, although our participants were not acutely intoxicated, cognitive functioning and structural/functional connectivity might improve after complete cessation of cannabis use for an extended period of time (Blanco‐Hinojo et al., 2017; D'Souza et al., 2016; Hirvonen et al., 2012; Scott, Slomiak, et al., 2018; Tait et al., 2011). Yet, the required duration of abstinence may depend on the frequency and intensity of use. THC is extremely fat‐soluble and can remain in fatty tissues (including brain) for extensive periods of time (many weeks), especially after chronic use. Last, our sample comprised African Americans, which could limit the generalizability of our findings. However, African Americans are an underrepresented group in psychiatry research while having a higher prevalence of CUD than Caucasians (Wu, Zhu, & Swartz, 2016) and we mostly confirm earlier findings.

In conclusion, in a large sample that was collected irrespective of cannabis use to minimize recruitment bias, we confirm the literature on poorer cognitive functioning in CUD, and an absence of volumetric brain differences between CUD and non‐CUD. We did not find evidence for or against a disruption of structural connectivity. We find disrupted FC in specific regions, although there was sufficient proof that organization of FC as determined via graph metrics does not differ between CUD and non‐user group.