Cannabis, Cannabinoids, and Brain Morphology: A Review of the Evidence

Chye, Yann; Kirkham, Rebecca; Lorenzetti, Valentina; McTavish, Eugene; Solowij, Nadia; Yücel, Murat

doi:10.1016/j.bpsc.2020.07.009

Cited by 17 publications

(15 citation statements)

References 132 publications

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“…If true, an age gradient effect could explain some of the divergent results observed across studies. That age-related GMV differences in temporal regions are present in CU youth and decrease as a function of age is consistent with preclinical studies showing non-linear morphologic changes in CB1 receptor enriched brain regions following adolescent cannabis exposure (9,10). Our results parallel prior human imaging studies showing increased volume and thickness in temporal regions of early-adolescent cannabis users and decreased volumes and thickness in temporal regions of late-adolescent and young adult cannabis users (12,43,44).…”

Section: Discussionsupporting

confidence: 90%

“…Additionally, they could reflect sex differences in brain age at time of cannabis exposure, given that adolescent girls brains are at a more advanced stage of maturation compared to age-matched boys (47). Based upon this, increased R-MOG volume in CU girls relative to sexmatched controls could be related to disruptions in synaptic pruning (49) and decreased R-MOG volume in CU boys could reflect increased sensitivity to cannabis-related neurotoxicity via apoptotic-mechanisms (9). Alternatively, this finding could reflect general neurodevelopmental differences between boys and girls, although this is less likely as sex differences in adolescent brain morphology are less pronounced in occipital regions (65,66).…”

Section: Discussionmentioning

confidence: 99%

“…The use of cannabis during adolescence may have complex effects on brain structure and function that extend into adulthood ( 8 ). While preclinical studies show strong and consistent evidence for a causal relationship between exposure to cannabinoids and changes in brain morphology [see ( 9 ) and ( 10 ) for reviews], there is conflicting evidence on the long-term effects of cannabis on brain structure in humans ( 11 ). Evidence from human structural magnetic resonance imaging (sMRI) studies has been mixed to date, with some studies reporting increased brain volumes related to CU ( 12 ) and other studies reporting decreased brain volumes ( 13 ) or the absence of volumetric differences between CU and non-users ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

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Age- and Sex-Related Cortical Gray Matter Volume Differences in Adolescent Cannabis Users: A Systematic Review and Meta-Analysis of Voxel-Based Morphometry Studies

et al. 2021

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Introduction: Adolescent-onset cannabis use is rising in the era of marijuana legalization. Recent imaging studies have identified neuroanatomical differences between adult cannabis users and controls that are more prominent in early-onset users. Other studies point to sex-dependent effects of cannabis.Methods: A systematic review following PRISMA guidelines and subsequent effect-size seed-based d mapping (SDM) meta-analyses were conducted to investigate relationships between age (across the 12-to-21-year-old developmental window), sex, and gray matter volume (GMV) differences between cannabis using (CU) and typically developing (TD) youth.Results: Our search identified 1,326 citations, 24 of which were included in a qualitative analysis. A total of 6 whole-brain voxel-based morphometry (VBM) studies comparing regional GMV between 357 CU [mean (SD) age = 16.68 (1.28); 71% male] and 404 TD [mean (SD) age = 16.77 (1.36); 63% male] youth were included in the SDM-meta-analysis. Meta-analysis of whole-brain VBM studies identified no regions showing significant GMV difference between CU and TD youth. Meta-regressions showed divergent effects of age and sex on cortical GMV differences in CU vs. TD youth. Age effects were seen in the superior temporal gyrus (STG), with older-aged CU youth showing decreased and younger-aged CU youth showing increased STG GMV compared to age-matched TD youth. Parallel findings in the STG were also observed in relation to duration of CU (years) in supplemental meta-regressions. Regarding sex effects, a higher proportion of females in studies was associated with increased GMV in the middle occipital gyrus in CU vs. TD youth.Conclusions: These findings suggest that GMV differences between CU and TD youth, if present, are subtle, and may vary as a function of age, cumulative cannabis exposure, and sex in young people. Whether age- and sex-related GMV differences are attributable to common predispositional factors, cannabis-induced neuroadaptive changes, or both warrant further investigation.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Age- and Sex-Related Cortical Gray Matter Volume Differences in Adolescent Cannabis Users: A Systematic Review and Meta-Analysis of Voxel-Based Morphometry Studies

et al. 2021

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“…Individuals who have greater genetic liability for using cannabis may use cannabis more frequently, which has been associated with cortical alterations. 40,41 For example, work by Chye et al 12,41 indicated that adolescents who used cannabis showed reduced thickness in the bilateral insula, a brain region that has been closely linked to addictive behaviors, including craving and drug-seeking, interoceptive processing, response to reward, and impulsive decision making. 42,43 Other work has shown that individuals who initiate cannabis use had a smaller insula surface area in the right hemisphere.…”

Section: Discussionmentioning

confidence: 99%

Shared genetic etiology between cortical brain morphology and tobacco, alcohol, and cannabis use

Rabinowitz

Campos

Ong

et al. 2021

Preprint

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Genome-wide association studies (GWAS) have independently identified hundreds of genomic regions associated with brain morphology and substance use. However, the genetic overlap between brain structure and substance use has not been characterized. Here we leverage GWAS summary data of 71 brain imaging measures and alcohol, tobacco, and cannabis use to investigate their genetic overlap using LD score regression. We also used genomic structural equation modeling to model a substance use common genetic factor and examined its genetic overlap with brain structure. After accounting for multiple testing, we identified eight significant negative genetic correlations, including between alcoholic drinks per week and average cortical thickness and intracranial volume with the age of smoking initiation; and five positive genetic correlations, including between insula surface area and lifetime cannabis use, and between the common factor with pericalcarine surface area. Our findings highlight a shared genetic etiology between variation in cortical brain morphology and substance use.

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“…Structural cortical abnormalities have consistently been found in people with substance use disorders in general and CUD in particular (Chye et al, 2021;Chye et al, 2019;Cousijn et al, 2012;Lorenzetti, Chye, Silva, Solowij, & Roberts, 2019;Lorenzetti et al, 2020). Various factors (e.g., the types of drugs, addiction severity, and age of onset) affect the extent and location of morphological changes of the cerebral cortex.…”

Section: Introductionmentioning

confidence: 99%

Cortical Morphology in Cannabis Use Disorder: Implications for Transcranial Direct Current Stimulation Treatment

Soleimani

Towhidkhah

Saviz

et al. 2023

BCN

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Introduction: Transcranial direct current stimulation (tDCS) has been studied as an adjunctive treatment option for substance use disorders (SUDs). Alterations in brain structure following SUD may change tDCS-induced electric field (EF) and subsequent responses. However, group-level differences between healthy controls (HC) and participants with SUDs in terms of EF and its association with cortical architecture have not yet been modeled quantitatively. Objective: We provided a methodology for group-level analysis of computational head models (CHMs) to investigate the influence of cortical morphology metrics on EFs. Method: Whole-brain surface-based morphology was conducted and cortical thickness, volume, and surface area were compared between participants with CUD (n=20) and age-matched HC (n=22). We also simulated EFs for bilateral tDCS over DLPFC. Effects of structural alterations on EF distribution were investigated based on individualized CHMs. Results: In terms of EF, no significant difference was found within the prefrontal cortex. However, EFs were significantly different in left-postcentral and right-superior temporal gyrus (P < 0.05) with higher level of variance in CUD compared to HC (F39,43=5.31,P<0.0001,C =0.95). We found significant differences in cortical area (caudal anterior cingulate and rostral middle frontal), thickness (lateral orbitofrontal), and volume (paracentral and fusiform) between two groups. Conclusion: Brain morphology and tDCS-induced EFs may be changed following CUD. However, differences between CUD and HCs in EFs do not always overlap with brain areas that showed structural alterations. To sufficiently modulate stimulation targets, it should be checked if individuals with CUD need to be given different stimulation dose based on tDCS target location.

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Cannabis, Cannabinoids, and Brain Morphology: A Review of the Evidence

Cited by 17 publications

References 132 publications

Age- and Sex-Related Cortical Gray Matter Volume Differences in Adolescent Cannabis Users: A Systematic Review and Meta-Analysis of Voxel-Based Morphometry Studies

Age- and Sex-Related Cortical Gray Matter Volume Differences in Adolescent Cannabis Users: A Systematic Review and Meta-Analysis of Voxel-Based Morphometry Studies

Shared genetic etiology between cortical brain morphology and tobacco, alcohol, and cannabis use

Cortical Morphology in Cannabis Use Disorder: Implications for Transcranial Direct Current Stimulation Treatment

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