Consortium Profile: The Methylation, Imaging and NeuroDevelopment (MIND) Consortium.

Epigenetic processes, such as DNA methylation, show potential as biological markers and mechanisms underlying gene-environment interplay in the prediction of mental health and other brain-based phenotypes. However, little is known about how peripheral epigenetic patterns relate to individual differences in the brain itself. An increasingly popular approach to address this is by combining epigenetic and neuroimaging data; yet, research in this area is almost entirely comprised of cross-sectional studies in adults. To bridge this gap, we established the Methylation, Imaging and NeuroDevelopment (MIND) Consortium, which aims to bring a developmental focus to the emerging field of Neuroimaging Epigenetics by (i) promoting collaborative, adequately powered developmental research via multi-cohort analyses; (ii) increasing scientific rigor through the establishment of shared pipelines and open science practices; and (iii) advancing our understanding of DNA methylation-brain dynamics at different developmental periods (from birth to emerging adulthood), by leveraging data from prospective, longitudinal pediatric studies. MIND currently integrates 15 cohorts worldwide, comprising (repeated) measures of DNA methylation in peripheral tissues (blood, buccal cells, and saliva) and neuroimaging by magnetic resonance imaging across up to five time points over a period of up to 21 years (Npooled DNAm = 11,299; Npooled neuroimaging = 10,133; Npooled combined = 4,914). By triangulating associations across multiple developmental time points and study types, we hope to generate new insights into the dynamic relationships between peripheral DNA methylation and the brain, and how these ultimately relate to neurodevelopmental and psychiatric phenotypes.

Persistent increased severity of cannabis use disorder symptoms in adolescents compared to adults: a one-year longitudinal study.

Adolescence is a developmental period characterised by increased vulnerability to cannabis use disorder (CUD). However, previous investigations of this vulnerability have relied on cross-sectional comparisons and lack a detailed assessment of cannabis quantity, a potentially important confounding factor. Here, we aimed to investigate the one-year course of CUD in adolescents compared to adults who currently use cannabis, adjusting for a comprehensive measure of cannabis quantity. Data are from a one-year observational longitudinal study (CannTeen) of adolescents and adults who currently used cannabis regularly with five waves of assessment at 3-monthly intervals, based in London, UK. Participants were n = 70 adults (26-29, 45.7% female), who did not regularly use cannabis when they were under age 18, and n = 76 adolescents (16-17, 50.0% female). The exposure was adolescent (compared to adult) frequent cannabis use. The primary outcome was CUD symptoms measured using the cannabis use disorder identification test revised (CUDIT-R) at five time points. Models were adjusted for cannabis quantity using mean weekly standard THC units (one unit = 5 mg THC). Other covariates included gender, and whether each session occurred before or during the COVID-19 pandemic. In models adjusted for pre-registered covariates, adolescents scored 3.7 points higher on the CUDIT-R compared to the adult group across the 5 assessment waves (3.66 95% CIs 1.99, 5.34). There was also evidence of a linear reduction in symptoms over time in both groups (-0.47, 95%CIs -0.67, -0.27). Adolescents had persistently increased CUD symptoms compared to adults across the 12-month period. This association was robust after adjusting for the quantity of cannabis consumed and other covariates.

Applying machine learning to international drug monitoring: classifying cannabis resin collected in Europe using cannabinoid concentrations.

In Europe, concentrations of ∆9-tetrahydrocannabinol (THC) in cannabis resin (also known as hash) have risen markedly in the past decade, potentially increasing risks of mental health disorders. Current approaches to international drug monitoring cannot distinguish between different types of cannabis resin which may have contrasting health effects due to THC and cannabidiol (CBD) content. Here, we compared concentrations of THC and CBD in different types of cannabis resin collected in Europe (either Moroccan-type, or Dutch-type). We then tested the ability of machine learning algorithms to classify the type of cannabis resin (either Moroccan-type, or Dutch-type) using routinely collected monitoring data on THC and CBD. Finally, we applied the optimal algorithm to new samples collected in countries where the type of cannabis resin was unknown, the UK and Denmark. Results showed that overall, Dutch-type samples had higher THC (Hedges' g = 2.39) and lower CBD (Hedges' g = 0.81) than Moroccan-type samples. A Support Vector Machine algorithm achieved classification accuracy exceeding 95%, with little variation in this estimate, good interpretability, and plausibility. It made contrasting predictions about the type of cannabis resin collected in the UK (94% Moroccan-type; 6% Dutch-type) and Denmark (36% Moroccan-type; 64% Dutch-type). In conclusion, we provide proof-of-concept evidence for the potential of machine learning to inform international drug monitoring. Our findings should not be interpreted as objective confirmatory evidence but suggest that Dutch-type cannabis resin has higher THC concentrations than Moroccan-type cannabis resin, which may contribute to variation in drug markets and health outcomes for people who use cannabis in Europe.

Acute effects of different types of cannabis on young adult and adolescent resting-state brain networks.

Adolescence is a time of rapid neurodevelopment and the endocannabinoid system is particularly prone to change during this time. Cannabis is a commonly used drug with a particularly high prevalence of use among adolescents. The two predominant phytocannabinoids are Delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), which affect the endocannabinoid system. It is unknown whether this period of rapid development makes adolescents more or less vulnerable to the effects of cannabis on brain-network connectivity, and whether CBD may attenuate the effects of THC. Using fMRI, we explored the impact of vaporized cannabis (placebo, THC: 8 mg/75 kg, THC + CBD: 8 mg/75 kg THC & 24 mg/75 kg CBD) on resting-state networks in groups of semi-regular cannabis users (usage frequency between 0.5 and 3 days/week), consisting of 22 adolescents (16-17 years) and 24 young adults (26-29 years) matched for cannabis use frequency. Cannabis caused reductions in within-network connectivity in the default mode (F[2,88] = 3.97, P = 0.022, η² = 0.018), executive control (F[2,88] = 18.62, P < 0.001, η² = 0.123), salience (F[2,88] = 12.12, P < 0.001, η² = 0.076), hippocampal (F[2,88] = 14.65, P < 0.001, η² = 0.087), and limbic striatal (F[2,88] = 16.19, P < 0.001, η² = 0.102) networks compared to placebo. Whole-brain analysis showed cannabis significantly disrupted functional connectivity with cortical regions and the executive control, salience, hippocampal, and limbic striatal networks compared to placebo. CBD did not counteract THC's effects and further reduced connectivity both within networks and the whole brain. While age-related differences were observed, there were no interactions between age group and cannabis treatment in any brain network. Overall, these results challenge the assumption that CBD can make cannabis safer, as CBD did not attenuate THC effects (and in some cases potentiated them); furthermore, they show that cannabis causes similar disruption to resting-state connectivity in the adolescent and adult brain.

The acute effects of cannabis, with and without cannabidiol, on attentional bias to cannabis related cues: a randomised, double-blind, placebo-controlled, cross-over study.

RATIONALE: Attentional bias to drug-related stimuli is hypothesised to contribute towards addiction. However, the acute effects of Δ9-tetrahydrocannabinol (THC) on attentional bias to cannabis cues, the differential response in adults and adolescents, and the moderating effect of cannabidiol (CBD) are unknown. OBJECTIVES: Our study investigated (1) the acute effects of vaporised cannabis on attentional bias to cannabis-related images in adults and adolescents and (2) the moderating influences of age and CBD. METHODS: We conducted a randomised, double-blind, placebo-controlled, cross-over study where three weight-adjusted vaporised cannabis preparations: 'THC' (8 mg THC for a 75-kg person), 'THC + CBD' (8 mg THC and 24 mg CBD for a 75-kg person) and PLA (matched placebo). Cannabis was administered on 3 separate days to 48 participants, who used cannabis 0.5-3 days/week: 24 adolescents (12 females, aged 16-17) and 24 adults (12 females, aged 26-29). Participants completed a visual probe task with cannabis cues. Our primary outcome was attentional bias to cannabis stimuli, measured using the differential reaction time to a cannabis vs. neutral probe, on 200-ms trials. RESULTS: In contrast to hypotheses, attention was directed away from cannabis cues on placebo, and there was a main effect of the drug (F(2,92) = 3.865, p = 0.024, η2p = 0.077), indicating THC administration eliminated this bias. There was no significant impact of CBD nor an age-by-drug interaction. CONCLUSIONS: Acute THC intoxication eliminated attentional bias away from cannabis cues. There was no evidence of differential response in adolescents compared to adults and no evidence that a moderate vaporised dose of CBD altered the impact of cannabis on attentional bias. TRIAL REGISTRATION: This study was listed with the US National Library of Medicine and registered on ClinicalTrials.gov, URL: Do Adolescents and Adults Differ in Their Acute Response to Cannabis?-Full Text View-ClinicalTrials.gov, registration number: NCT04851392.

Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study.

Neuroimaging studies consistently show advanced brain age in schizophrenia, suggesting that brain structure is often 'older' than expected at a given chronological age. Whether advanced brain age is linked to genetic liability for schizophrenia remains unclear. In this pre-registered secondary data analysis, we utilised a recall-by-genotype approach applied to a population-based subsample from the Avon Longitudinal Study of Parents and Children to assess brain age differences between young adults aged 21-24 years with relatively high (n = 96) and low (n = 93) polygenic risk for schizophrenia (SCZ-PRS). A global index of brain age (or brain-predicted age) was estimated using a publicly available machine learning model previously trained on a combination of region-wise gray-matter measures, including cortical thickness, surface area and subcortical volumes derived from T1-weighted magnetic resonance imaging (MRI) scans. We found no difference in mean brain-PAD (the difference between brain-predicted age and chronological age) between the high- and low-SCZ-PRS groups, controlling for the effects of sex and age at time of scanning (b = -.21; 95% CI -2.00, 1.58; p = .82; Cohen's d = -.034; partial R2 = .00029). These findings do not support an association between SCZ-PRS and brain-PAD based on global age-related structural brain patterns, suggesting that brain age may not be a vulnerability marker of common genetic risk for SCZ. Future studies with larger samples and multimodal brain age measures could further investigate global or localised effects of SCZ-PRS.

Enhanced cannabis timeline followback (EC-TLFB): Comprehensive assessment of cannabis use including standard THC units and validation through biological measures.

AIMS: The aims of this study were to present an enhanced cannabis timeline followback (EC-TLFB) enabling comprehensive assessment of cannabis use measures, including standard tetrahydrocannabinol (THC) units, and to validate these against objectively indexed urinary 11-nor-9-carboxy-tetrahydrocannabinol (THC-COOH) concentrations. DESIGN: We used cross-sectional baseline data from the 'CannTeen' observational longitudinal study. SETTING: The study was conducted in London, UK. PARTICIPANTS: A total of 147 participants who used cannabis regularly took part in the study (n = 71 female, n = 76 male; mean age = 21.90, standard deviation = 5.32). MEASUREMENTS: The EC-TLFB was used to calculate frequency of cannabis use, method of administration, including co-administration with tobacco, amount of cannabis used (measured with unaided self-report and also using pictorial aided self-report) and type of cannabis product (flower, hash) which was used to estimate THC concentration (both from published data on THC concentration of products and analysis of cannabis samples donated by participants in this study). We calculated total weekly standard THC units (i.e. 5 mg THC for all cannabis products and methods of administration) using the EC-TLFB. The outcome variable for validation of past week EC-TLFB assessments was creatinine-normalized carboxy-tetrahydrocannabinol (THC-COOH) in urine. FINDINGS: All measures of cannabis exposure included in this analysis were positively correlated with levels of THC-COOH in urine (r = 0.41-0.52). Standard THC units, calculated with average concentrations of THC in cannabis in the UK and unaided self-report measures of amount of cannabis used in grams showed the strongest correlation with THC-COOH in urine (r = 0.52, 95% bias-corrected and accelerated = 0.26-0.70). CONCLUSIONS: The enhanced cannabis timeline followback (EC-TLFB) can provide a valid assessment of a comprehensive set of cannabis use measures including standard tetrahydrocannabinol units as well as and traditional TLFB assessments (e.g. frequency of use and grams of cannabis use).