Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

The long-term effectiveness of a personality-targeted substance use prevention program on aggression from adolescence to early adulthood.

BACKGROUND: Addressing aggressive behavior in adolescence is a key step toward preventing violence and associated social and economic costs in adulthood. This study examined the secondary effects of the personality-targeted substance use preventive program Preventure on aggressive behavior from ages 13 to 20. METHODS: In total, 339 young people from nine independent schools (M age = 13.03 years, s.d. = 0.47, range = 12-15) who rated highly on one of the four personality traits associated with increased substance use and other emotional/behavioral symptoms (i.e. impulsivity, anxiety sensitivity, sensation seeking, and negative thinking) were included in the analyses (n = 145 in Preventure, n = 194 in control). Self-report assessments were administered at baseline and follow-up (6 months, 1, 2, 3, 5.5, and 7 years). Overall aggression and subtypes of aggressive behaviors (proactive, reactive) were examined using multilevel mixed-effects analysis accounting for school-level clustering. RESULTS: Across the 7-year follow-up period, the average yearly reduction in the frequency of aggressive behaviors (b = -0.42; 95% confidence interval [CI] -0.64 to -0.20; p < 0.001), reactive aggression (b = -0.22; 95% CI 0.35 to -0.10; p = 0.001), and proactive aggression (b = -0.14; 95% CI -0.23 to -0.05; p = 0.002) was greater for the Preventure group compared to the control group. CONCLUSIONS: The study suggests a brief personality-targeted intervention may have long-term impacts on aggression among young people; however, this interpretation is limited by imbalance of sex ratios between study groups.

Neurocognitive consequences of adolescent sleep disruptions and their relationship to psychosis vulnerability: a longitudinal cohort study.

Adolescence is a key period for neurocognitive maturation where deviation from normal developmental trajectories may be tied to adverse mental health outcomes. Cognitive disruptions have been noted in populations at risk for psychosis and are known to accompany periods of sleep deprivation. This study aims to assess the role of cognition as a mediator between sleep disruptions and psychosis risk. A cohort of 3801 high school students (51% female, mean age = 12.8, SD = 0.45 years) was recruited from 31 Montreal high schools. Measures of sleep, psychotic-like experiences, inhibition, working memory, perceptual reasoning, and delayed recall were collected from participants on a yearly basis over the five years of their high school education. A multi-level model mediation analysis was performed controlling for sex and time squared. Response inhibition was shown to be associated with, and to mediate (B = -0.005, SD = 0.003, p = 0.005*) the relationship between sleep disruptions (B = -0.011, SD = 0.004, p < 0.001*) and psychotic-like experiences (B = 0.411, SD = 0.170, p = 0.005*). Spatial working memory deficits on a given year were associated with a higher frequency of psychotic-like experiences that same year (B = -0.046, SD = 0.018, p = 0.005*) and the following year (B = -0.051, SD = 0.023, p = 0.010*), but were not associated with sleep disturbances. No significant associations were found between our variables of interest and either delayed recall or perceptual reasoning at the within person level. Findings from this large longitudinal study provide evidence that the association between sleep disruptions and psychosis risk is specifically mediated by inhibitory rather than general cognitive impairments. The association of spatial working memory, response inhibition, and sleep disruptions with psychotic-like experiences suggests that these factors may represent potential targets for preventative interventions.

High peak drinking levels mediate the relation between impulsive personality and injury risk in emerging adults.

BACKGROUND: Alcohol-induced injury is one of the leading causes of preventable morbidity and mortality. We investigated the relationship between impulsive personality and physical injury (e.g. falls, sports), and whether peak drinking quantity specifically, and/or risky behaviour more generally, mediates the relationship between impulsivity and injury in undergraduates. METHOD: We used data from the winter 2021 UniVenture survey with 1316 first- and second-year undergraduate students aged 18-25 years (79.5% female) from five Canadian Universities. Students completed an online survey regarding their demographics, personality, alcohol use, risky behaviours, and injury experiences. Impulsivity was measured with the substance use risk profile scale, past 30-day peak alcohol use with the quantity-frequency-peak Alcohol Use Index, general risky behaviour with the risky behaviour questionnaire, and past 6-month injury experience with the World Health Organization's (2017) injury measurement questionnaire. RESULTS: Of 1316 total participants, 12.9% (n = 170) reported having sustained a physical injury in the past 6 months. Mean impulsivity, peak drinking quantity, and risky behaviour scores were significantly higher among those who reported vs. did not report injury. Impulsivity and peak drinking quantity, but not general risky behaviour, predicted injury in a multi-level generalized mixed model. Mediation analyses supported impulsivity as both a direct predictor of physical injury and an indirect predictor through increased peak drinking (both p < .05), but not through general risky behaviour. CONCLUSION: Results imply emerging adults with impulsive tendencies should be identified for selective injury prevention programs and suggest targeting their heavy drinking to decrease their risk for physical injury.

Do anxiety symptoms and coping motives serially mediate the association between psychotic-like experiences and cannabis-related problems in undergraduate recent cannabis users?

Many Canadian emerging adults (ages 18-25 years) use cannabis, with 60 % of past-three-month users experiencing one or more cannabis-related problems (i.e., adverse consequences of use). While psychotic-like experiences (PLEs) and cannabis problems overlap, little is known about the mechanisms explaining this link. One hypothesis is that PLEs are distressing and give rise to anxiety, with which emerging adults attempt to cope through increased cannabis use, in turn increasing their risk for cannabis-related problems. We tested a chained-mediational model to determine if anxiety and coping-with-anxiety motives for cannabis use sequentially mediated the link between PLEs and cannabis problems in emerging adult undergraduates; a conditional process model tested for moderation by sex. Emerging adult cannabis users (N = 413; mean [SD] age = 19.1 [1.5] years; 71.9 % female) from five Canadian universities provided cross-sectional, self-report survey data in fall 2021. Validated measures of PLEs, anxiety, cannabis coping-with-anxiety motives, and cannabis-related problems were administered. Path analyses supported the hypothesized chained mediational indirect effect (b = 0.027, 95 % bootstrap CI [0.012, 0.050]). No direct effect was found (p =.698), suggesting that the PLEs-to-cannabis problems association is fully explained by anxiety and cannabis coping-with-anxiety motives. Inconsistent with hypotheses, mediation did not depend on sex (95 % CIs crossed zero); therefore, anxiety and cannabis coping-with-anxiety motives explain the link between PLEs and cannabis problems in emerging adults regardless of their sex. Results highlight anxiety and cannabis coping-with-anxiety motives as potentially important intervention targets in cannabis-using emerging adults with PLEs, possibly preventing the development/worsening of cannabis-related problems.

Effect of a selective personality-targeted prevention program on 7-year illicit substance related outcomes: A secondary analysis of a cluster randomized controlled trial.

BACKGROUND: This study evaluated the efficacy of the selective personality-targeted PreVenture program in reducing cannabis and stimulant use over a 7-year period spanning adolescence and early adulthood. METHODS: A cluster randomized controlled trial was conducted in 14 Australian schools. Schools were randomized to PreVenture, a brief personality-targeted selective intervention, comprising two 90-minute facilitator-led sessions delivered one week apart, or a control group (health education as usual). Only students who scored highly on one of four personality traits (anxiety sensitivity, negative thinking, impulsivity, sensation seeking) were included. Students completed online self-report questionnaires between 2012 and 2019: at baseline; post-intervention; 1-, 2-, 3-, 5.5- and 7-years post-baseline. Outcomes were past 6-months cannabis use, stimulant use (MDMA, methamphetamine or amphetamine) and cannabis-related harms. RESULTS: The sample comprised 438 adolescents (Mage=13.4 years; SD=0.47) at baseline. Retention ranged from 51% to 79% over the 7-years. Compared to controls, the PreVenture group had significantly reduced odds of annual cannabis-related harms (OR=0.78, 95% CI=0.65-0.92). However, there were no significant group differences in the growth of cannabis use (OR=0.84, 95% CI=0.69-1.02) or stimulant use (OR=1.07, 95% CI=0.91-1.25) over the 7-year period. CONCLUSIONS: PreVenture was effective in slowing the growth of cannabis-related harms over time, however owing to missing data over the 7-year trial, replication trials may be warranted to better understand the impact of the PreVenture intervention on cannabis and stimulant use among young Australians. Alternative implementation strategies, such as delivering the intervention in later adolescence and/or providing booster sessions, may be beneficial.

Mega-analysis of the brain-age gap in substance use disorder: An ENIGMA Addiction working group study.

BACKGROUND AND AIMS: The brain age gap (BAG), calculated as the difference between a machine learning model-based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls. DESIGN: This was a cross-sectional study. SETTING: This is an Enhancing Neuro Imaging through Meta-Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites. PARTICIPANTS: This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls. MEASUREMENTS: This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls. FINDINGS: Alcohol use disorder (ß = -5.49, t = -5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine-type stimulant use disorder (ß = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance-specific models. CONCLUSIONS: People with alcohol use disorder appear to have a higher brain-age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain.

Neuroscience-informed classification of prevention interventions in substance use disorders: An RDoC-based approach.

Neuroscience has contributed to uncover the mechanisms underpinning substance use disorders (SUD). The next frontier is to leverage these mechanisms as active targets to create more effective interventions for SUD treatment and prevention. Recent large-scale cohort studies from early childhood are generating multiple levels of neuroscience-based information with the potential to inform the development and refinement of future preventive strategies. However, there are still no available well-recognized frameworks to guide the integration of these multi-level datasets into prevention interventions. The Research Domain Criteria (RDoC) provides a neuroscience-based multi-system framework that is well suited to facilitate translation of neurobiological mechanisms into behavioral domains amenable to preventative interventions. We propose a novel RDoC-based framework for prevention science and adapted the framework for the existing preventive interventions. From a systematic review of randomized controlled trials using a person-centered drug/alcohol preventive approach for adolescents, we identified 22 unique preventive interventions. By teasing apart these 22 interventions into the RDoC domains, we proposed distinct neurocognitive trajectories which have been recognized as precursors or risk factors for SUDs, to be targeted, engaged and modified for effective addiction prevention.

Cannabis Dependence is Associated with Reduced Hippocampal Subregion Volumes Independently of Sex: Findings from an ENIGMA Addiction Working Group Multi-Country Study.

Background: Males and females who consume cannabis can experience different mental health and cognitive problems. Neuroscientific theories of addiction postulate that dependence is underscored by neuroadaptations, but do not account for the contribution of distinct sexes. Further, there is little evidence for sex differences in the neurobiology of cannabis dependence as most neuroimaging studies have been conducted in largely male samples in which cannabis dependence, as opposed to use, is often not ascertained. Methods: We examined subregional hippocampus and amygdala volumetry in a sample of 206 people recruited from the ENIGMA Addiction Working Group. They included 59 people with cannabis dependence (17 females), 49 cannabis users without cannabis dependence (20 females), and 98 controls (33 females). Results: We found no group-by-sex effect on subregional volumetry. The left hippocampal cornu ammonis subfield 1 (CA1) volumes were lower in dependent cannabis users compared with non-dependent cannabis users (p<0.001, d=0.32) and with controls (p=0.022, d=0.18). Further, the left cornu ammonis subfield 3 (CA3) and left dentate gyrus volumes were lower in dependent versus non-dependent cannabis users but not versus controls (p=0.002, d=0.37, and p=0.002, d=0.31, respectively). All models controlled for age, intelligence quotient (IQ), alcohol and tobacco use, and intracranial volume. Amygdala volumetry was not affected by group or group-by-sex, but was smaller in females than males. Conclusions: Our findings suggest that the relationship between cannabis dependence and subregional volumetry was not moderated by sex. Specifically, dependent (rather than non-dependent) cannabis use may be associated with alterations in selected hippocampus subfields high in cannabinoid type 1 (CB1) receptors and implicated in addictive behavior. As these data are cross-sectional, it is plausible that differences predate cannabis dependence onset and contribute to the initiation of cannabis dependence. Longitudinal neuroimaging work is required to examine the time-course of the onset of subregional hippocampal alterations in cannabis dependence, and their progression as cannabis dependence exacerbates or recovers over time.

Hazardous Drinking Mediates the Relation Between Externalizing Personality and Reduced Adherence to COVID-19 Public Health Guidelines in University Students.

Using a multigroup path analysis, we examined if hazardous alcohol use mediated the relations between elevated externalizing personality traits (i.e., impulsivity or sensation seeking) and reduced adherence to COVID-19 public health guidelines. We hypothesized that those high in externalizing personality traits would demonstrate less adherence to public health guidelines and that hazardous alcohol use would mediate this relationship. First- and second-year undergraduates (N = 1232; ages 18-25) from five Canadian universities participated in a cross-sectional survey between January to April 2021. Individuals with higher levels of impulsive or sensation seeking personality traits demonstrated poorer adherence to COVID-19 public health guidelines and these relations were mediated by hazardous alcohol use. Results suggest that hazardous drinking is an important target for students high in impulsivity and sensation seeking to increase their adherence to public health guidelines and thereby help control viral spread.

Recalibrating single-study effect sizes using hierarchical Bayesian models.

Introduction: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance. Methods: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method. Results: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. Discussion: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.