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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.

Background and aimsRecently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.Design and settingCross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years).CasesCases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.MeasurementsGraph theory metrics of segregation and integration were used to summarize SCN.FindingsMirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.ConclusionStructural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

Abstract 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

Abstract 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

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., Cohens 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.

Abstract Previous longitudinal studies found significant associations between screen time and increase in attention-deficit/hyperactivity disorder (ADHD) symptoms, but the mechanisms mediating this association remain understudied. Thus, we used data from a 5-year population-based longitudinal cohort of nearly 4000 Canadian high school students, modeled using multivariate multilevel mediation, to investigate the association of screen time (i.e., social media, television, video games, computer use) with ADHD symptoms via different potential behavioral and neuropsychological mediators (i.e. impulsivity, response inhibition, working memory). We studied direct and indirect between-person, concurrent within-person, and lagged-within-person effects of screens on ADHD symptoms. Results showed that increases in screen time in a given year were associated with an exacerbation of ADHD symptoms within that same year (within-person association), over and above potential common vulnerability (between-person association). Impulsivity proved to be the most robust mediator in the association of screen time with ADHD symptoms at both between and within-person levels. Only social media use displayed a significant lagged-within-person association with ADHD symptoms mediated by impulsivity, indicating an enduring influence on behavior, which was further shown to be mediated by chained changes in response inhibition on a Go/No-Go task. These findings provide clinical implications of screen time and should be an important focus in the management and prevention of ADHD symptoms among adolescents.

BackgroundNicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention.MethodsEleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence.ResultsThe cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p

To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored in a mega-analysis of previously published datasets from 2,034 AD and comparison participants spanning 27 sites curated by the ENIGMA Addiction Working Group. Data were grouped into a training set used for internal validation including 1,652 participants (692 AD, 24 sites), and a test set used for external validation with 382 participants (146 AD, 3 sites). An exploratory data analysis was first conducted, followed by an evolutionary search based feature selection to site generalizable and high performing subsets of brain measurements. Exploratory data analysis revealed that inclusion of case- and control-only sites led to the inadvertent learning of site-effects. Cross validation methods that do not properly account for site can drastically overestimate results. Evolutionary-based feature selection leveraging leave-one-site-out cross-validation, to combat unintentional learning, identified cortical thickness in the left superior frontal gyrus and right lateral orbitofrontal cortex, cortical surface area in the right transverse temporal gyrus, and left putamen volume as final features. Ridge regression restricted to these features yielded a test-set area under the receiver operating characteristic curve of 0.768. These findings evaluate strategies for handling multi-site data with varied underlying class distributions and identify potential biomarkers for individuals with current AD.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Brain asymmetry reflects left-right hemispheric differentiation, which is a quantitative brain phenotype that develops with age and can vary with psychiatric diagnoses. Previous studies have shown that substance dependence is associated with altered brain structure and function. However, it is unknown whether structural brain asymmetries are different in individuals with substance dependence compared with nondependent participants. Here, a mega-analysis was performed using a collection of 22 structural brain MRI datasets from the ENIGMA Addiction Working Group. Structural asymmetries of cortical and subcortical regions were compared between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis (n = 1,796) and nondependent participants (n = 996). Substance-general and substance-specific effects on structural asymmetry were examined using separate models. We found that substance dependence was significantly associated with differences in volume asymmetry of the nucleus accumbens (NAcc; less rightward; Cohen's d = 0.15). This effect was driven by differences from controls in individuals with alcohol dependence (less rightward; Cohen's d = 0.10) and nicotine dependence (less rightward; Cohen's d = 0.11). These findings suggest that disrupted structural asymmetry in the NAcc may be a characteristic of substance dependence.

IntroductionThere 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.MethodsWe 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.ResultsThe 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.DiscussionOur 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.

Background: Over 40 years of research implicates perfectionism in eating disorders in childhood and adolescence. However, the nature of this relationship remains understudied. To address this gap, we performed a systematic review and a meta-analysis to quantify the magnitude of the associations between perfectionism (i.e., unidimensional perfectionism, perfectionistic strivings, and perfectionistic concerns) and eating-related symptoms during childhood and adolescence. Methods: The literature search was conducted using five electronic databases in accordance with PRISMA guidelines: MEDLINE, Embase, CINAHL Complete, APA PsycINFO, and EMB Reviews. A total of 904 studies were identified; a total of 126 were included in the systematic review, and 65 in the meta-analysis (N = 29,268). Sensitivity analyses were also carried out to detect potential differences in age and clinical status. Results: All the associations we investigated were both significant and positive. Small effect sizes were found between eating global scores and unidimensional perfectionism, perfectionistic strivings, and perfectionistic concerns (res = 0.19, res = 0.21, res = 0.12, respectively) and remained significant in each age group in both clinical and community samples. Perfectionistic concerns were moderately associated with all eating measures, especially in community samples and samples with a mean age under 14. Conclusions: Psychological interventions specially designed to target perfectionistic concerns in the early stages of development may help prevent the onset or reduce the intensity of eating-related symptoms during childhood and adolescence.

While imaging studies have demonstrated volumetric differences in subcortical structures associated with dependence on various abused substances, findings to date have not been wholly consistent. Moreover, most studies have not compared brain morphology across those dependent on different substances of abuse to identify substance-specific and substance-general dependence effects. By pooling large multinational datasets from 33 imaging sites, this study examined subcortical surface morphology in 1628 nondependent controls and 2277 individuals with dependence on alcohol, nicotine, cocaine, methamphetamine, and/or cannabis. Subcortical structures were defined by FreeSurfer segmentation and converted to a mesh surface to extract two vertex-level metrics-the radial distance (RD) of the structure surface from a medial curve and the log of the Jacobian determinant (JD)-that, respectively, describe local thickness and surface area dilation/contraction. Mega-analyses were performed on measures of RD and JD to test for the main effect of substance dependence, controlling for age, sex, intracranial volume, and imaging site. Widespread differences between dependent users and nondependent controls were found across subcortical structures, driven primarily by users dependent on alcohol. Alcohol dependence was associated with localized lower RD and JD across most structures, with the strongest effects in the hippocampus, thalamus, putamen, and amygdala. Meanwhile, nicotine use was associated with greater RD and JD relative to nonsmokers in multiple regions, with the strongest effects in the bilateral hippocampus and right nucleus accumbens. By demonstrating subcortical morphological differences unique to alcohol and nicotine use, rather than dependence across all substances, results suggest substance-specific relationships with subcortical brain structures.

Macrocyclic imine phycotoxins are an emerging class of chemical compounds associated with harmful algal blooms and shellfish toxicity. Earlier binding and electrophysiology experiments on nAChR subtypes and their soluble AChBP surrogates evidenced common trends for substantial antagonism, binding affinities, and receptor-subtype selectivity. Earlier, complementary crystal structures of AChBP complexes showed that common determinants within the binding nest at each subunit interface confer high-affinity toxin binding, while distinctive determinants from the flexible loop C, and either capping the nest or extending toward peripheral subsites, dictate broad versus narrow receptor subtype selectivity. From these data, small spiroimine enantiomers mimicking the functional core motif of phycotoxins were chemically synthesized and characterized. Voltage-clamp analyses involving three nAChR subtypes revealed preserved antagonism for both enantiomers, despite lower subtype specificity and binding affinities associated with faster reversibility compared with their macrocyclic relatives. Binding and structural analyses involving two AChBPs pointed to modest affinities and positional variability of the spiroimines, along with a range of AChBP loop-C conformations denoting a prevalence of antagonistic properties. These data highlight the major contribution of the spiroimine core to binding within the nAChR nest and confirm the need for an extended interaction network as established by the macrocyclic toxins to define high affinities and marked subtype specificity. This study identifies a minimal set of functional pharmacophores and binding determinants as templates for designing new antagonists targeting disease-associated nAChR subtypes.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

We investigated whether anxiety sensitivity (AS) is associated with increased distress and adherence to public health guidelines during the COVID-19 pandemic among undergraduates, and whether increased distress mediates the relationship between AS and increased adherence. An online cross-sectional survey was conducted with 1318 first- and second-year undergraduates (mean age of 19.2 years; 79.5% females) from five Canadian universities. Relevant subscales of the Substance Use Risk Profile Scale (SURPS) and the Big Five Inventory-10 (BFI-10) were used to assess AS and neuroticism. Three measures tapped distress: the Patient Health Questionnaire-9 (PHQ-9) for depressive symptoms, the Generalized Anxiety Disorder-7 (GAD-7) for anxiety symptoms, and the Brief COVID-19 Stress Scales (CSS-B) for COVID-19-specific distress. The COVID-19 Adherence scale (CAD) assessed adherence to COVID-19 containment measures. AS was significantly independently associated with higher general distress (both anxiety and depressive symptoms) and higher COVID-19-specific distress, after controlling age, sex, study site, and neuroticism. Moreover, AS indirectly predicted greater adherence to COVID-19 preventive measures through higher COVID-19-specific distress. Interventions targeting higher AS might be helpful for decreasing both general and COVID-19-specific distress, whereas interventions targeting lower AS might be helpful for increasing adherence to public health containment strategies, in undergraduates.

Abstract Self-disturbances constitute a hallmark of psychosis, but it remains unclear whether these alterations are present in at-risk populations, and therefore their role in the development of psychosis has yet to be confirmed. The present study addressed this question by measuring neural correlates of self-other processing in youth belonging to three developmental trajectories of psychotic experiences. Eighty-six youths were recruited from a longitudinal cohort of over 3800 adolescents based on their trajectories of Psychotic-Like Experiences from 12 to 16 years of age. Participants underwent neuroimaging at 17 years of age (mean). A functional neuroimaging task evaluating self- and other-related trait judgments was used to measure whole-brain activation and connectivity. Youth who showed an increasing trajectory displayed hypoactivation of the dorsomedial prefrontal cortex and hypoconnectivity with the cerebellum. By contrast, youth who showed a decreasing trajectory displayed decreased activation of the superior temporal gyrus, the inferior frontal gyrus, and the middle occipital gyrus. These findings suggest that the increasing trajectory is associated with alterations that might erode distinctions between self and other, influencing the emergence of symptoms such as hallucinations. The decreasing trajectory, in comparison, was associated with hypoactivations in areas influencing attention and basic information processing more generally. These alterations might affect the trajectories’ susceptibilities to positive vs. negative symptoms, respectively.

BackgroundThe Four Factor Personality Vulnerability model identifies four specific personality traits (e.g., sensation seeking [SS], impulsivity [IMP], anxiety sensitivity [AS], and hopelessness [HOP]) as implicated in substance use behaviors, motives for substance use, and co-occurring psychiatric conditions. Although the relationship between these traits and polysubstance use in opioid agonist therapy (OAT) clients has been investigated quantitatively, no study has examined the qualitative expression of each trait using clients’ voice.MethodNineteen Methadone Maintenance Therapy (MMT) clients (68.4% male, 84.2% white, mean age[SD] = 42.71 [10.18]) scoring high on one of the four personality traits measured by the Substance Use Risk Profile Scale [SURPS] completed a semi-structured qualitative interview designed to explore their lived experience of their respective trait. Thematic analysis was used to derive themes, which were further quantified using content analysis.ResultsThemes emerging from interviews reflected (1) internalizing and externalizing symptoms, (2) adversity experiences, and (3) polysubstance use. Internalizing symptoms subthemes included symptoms of anxiety, fear, stress, depression, and avoidance coping. Externalizing subthemes included anger, disinhibited cognitions, and anti-social and risk-taking behaviors. Adverse experiences subthemes included poor health, poverty, homelessness, unemployment, trauma, and conflict. Finally, polysubstance use subthemes include substance types, methods of use, and motives. Differences emerged between personality profiles in the relative endorsement of various subthemes, including those pertaining to polysubstance use, that were largely as theoretically expected.ConclusionPersonality is associated with unique cognitive, affective, and behavioral lived experiences, suggesting that personality may be a novel intervention target in adjunctive psychosocial treatment for those undergoing OAT.

ObjectiveAlthough lower brain volume has been routinely observed in individuals with substance dependence compared with nondependent control subjects, the brain regions exhibiting lower volume have not been consistent across studies. In addition, it is not clear whether a common set of regions are involved in substance dependence regardless of the substance used or whether some brain volume effects are substance specific. Resolution of these issues may contribute to the identification of clinically relevant imaging biomarkers. Using pooled data from 14 countries, the authors sought to identify general and substance-specific associations between dependence and regional brain volumes.MethodBrain structure was examined in a mega-analysis of previously published data pooled from 23 laboratories, including 3,240 individuals, 2,140 of whom had substance dependence on one of five substances: alcohol, nicotine, cocaine, methamphetamine, or cannabis. Subcortical volume and cortical thickness in regions defined by FreeSurfer were compared with nondependent control subjects when all sampled substance categories were combined, as well as separately, while controlling for age, sex, imaging site, and total intracranial volume. Because of extensive associations with alcohol dependence, a secondary contrast was also performed for dependence on all substances except alcohol. An optimized split-half strategy was used to assess the reliability of the findings.ResultsLower volume or thickness was observed in many brain regions in individuals with substance dependence. The greatest effects were associated with alcohol use disorder. A set of affected regions related to dependence in general, regardless of the substance, included the insula and the medial orbitofrontal cortex. Furthermore, a support vector machine multivariate classification of regional brain volumes successfully classified individuals with substance dependence on alcohol or nicotine relative to nondependent control subjects.ConclusionsThe results indicate that dependence on a range of different substances shares a common neural substrate and that differential patterns of regional volume could serve as useful biomarkers of dependence on alcohol and nicotine.

Abstract Childhood maltreatment (CM) and genetic vulnerability are both risk factors for psychosis, but the relations between them are not fully understood. Guided by the recent identification of genetic risk to CM, this study investigates the hypothesis that genetic risk to schizophrenia also increases the risk of CM and thus impacts psychosis risk. The relationship between schizophrenia polygenetic risk, CM, and psychotic-like experiences (PLE) was investigated in participants from the Utrecht Cannabis Cohort (N = 1262) and replicated in the independent IMAGEN cohort (N = 1740). Schizophrenia polygenic risk score (SZ-PRS) were calculated from the most recent GWAS. The relationship between CM, PRS, and PLE was first investigated using multivariate linear regression. Next, mediation of CM in the pathway linking SZ-PRS and PLE was examined by structural equation modeling, while adjusting for a set of potential mediators including cannabis use, smoking, and neuroticism. In agreement with previous studies, PLE were strongly associated with SZ-PRS (B = 0.190, p = 0.009) and CM (B = 0.575, p

Abstract Paleoceanographic studies often rely on abundance changes in microfossil species, with little consideration for characteristics such as organism size, which may also be related to environmental changes. Using a tropical Indian Ocean (TIO) core‐top data set, we test the Optimum size‐hypothesis (OSH), investigating whether relative abundance or environmental variables are better descriptors of planktonic foraminifera species' optimum conditions. We also investigate the environmental drivers of whole‐assemblage planktonic foraminiferal test size variation in the TIO. We use an automated imaging and sorting system (MiSo) to identify planktonic foraminiferal species, analyze their morphology, and quantify fragmentation rate using machine learning techniques. Machine model accuracy is confirmed by comparison with human classifiers (97% accuracy). Data for 33 environmental parameters were extracted from modern databases and, through exploratory factor analysis and regression models, we explore relationships between planktonic foraminiferal size and oceanographic parameters in the TIO. Results show that the size frequency distribution of most planktonic foraminifera species is unimodal, with some larger species showing multimodal distributions. Assemblage size95/5 (95th percentile size) increases with increasing species diversity, and this is attributed to vertical niche separation induced by thermal stratification. Our test for the OSH reveals that relative abundance is not a good predictor of species' optima and within‐species size95/5 response to environmental parameters is species‐specific, with parameters related to carbonate ion concentration, temperature, and salinity being primary drivers. At the species and assemblage levels, our analyses indicate that carbonate ion concentration and temperature play important roles in determining size trends in TIO planktonic foraminifera.

BackgroundPeer victimization (PV) is associated with alterations in neural responses in regions subserving emotional regulatory processes and with increased risk of psychopathology during adolescence. The present study examined the longitudinal mediating effects of resting-state functional connectivity (rsFC) between adolescent PV and subsequent internalizing (depression and anxiety), and externalizing (conduct and hyperactivity/inattention) symptoms.Methods151 adolescents (baseline mean age 12–14; 54% males) were assessed and imaged three times during a five-year period. We focused on rsFC of a priori determined Regions-of-Interest (ROIs) guided by the literature (i.e., amygdala, anterior and posterior insula, anterior cingulate cortex, and medial prefrontal cortex). Multilevel mediation (MLM) analyses simultaneously examined the between-person, concurrent within-person, and lagged within-person associations between PV and internalizing/externalizing symptoms through changes in couplings of the amygdala with the other four ROIs. All models controlled for the effects of self-reported childhood maltreatment and sex differences.ResultsAn increased rsFC of the amygdala-posterior insula significantly mediated the lagged within-person association of PV and internalizing symptoms (β = 0.144; 95% CI [0.018, 0.332]). This effect was significant regardless of childhood maltreatment, concurrent externalizing symptoms, and sex differences. The rsFC did not mediate the relationship between PV and externalizing symptoms.ConclusionsResults of this study suggest that adolescent PV may lead to long-lasting maladaptive neural communication between emotional response and sensory perception of pain (i.e., bottom-up emotion regulation) and that these neural responses may serve as unique markers for increased internalizing symptoms that appear in later adolescence in peer-victimized youth. These findings have implications for interventions targeting internalizing symptoms in victimized adolescents.

Sleep is an important contributor for neural maturation and emotion regulation during adolescence, with long-term effects on a range of white matter tracts implicated in affective processing in at-risk populations. We investigated the effects of adolescent sleep patterns on longitudinal changes in white matter development and whether this is related to the emergence of emotional (internalizing) problems. Sleep patterns and internalizing problems were assessed using self-report questionnaires in adolescents recruited in the general population followed up from age 14–19 years (N = 111 White matter structure was measured using diffusion tensor imaging (DTI) and estimated using fractional anisotropy (FA). We found that longitudinal increases in time in bed (TIB) on weekends and increases in TIB-variability between weekdays to weekend, were associated with an increase in FA in various interhemispheric and cortico-striatal tracts. Extracted FA values from left superior longitudinal fasciculus mediated the relationship between increases in TIB on weekends and a decrease in internalizing problems. These results imply that while insufficient sleep might have potentially harmful effects on long-term white matter development and internalizing problems, longer sleep duration on weekends (catch-up sleep) might be a natural counteractive and protective strategy.

Abstract The aim of this study was to investigate the neural bases of facial emotion processing before the onset of clinical psychotic symptoms in youth belonging to well-defined developmental trajectories of psychotic-like experiences (PLEs). A unique sample of 86 youths was recruited from a population-based sample of over 3800 adolescents who had been followed from 13 to 17 years of age. Three groups were identified based on validated developmental trajectories: a control trajectory with low and decreasing PLEs, and two atypical trajectories with moderate to elevated baseline PLEs that subsequently decreased or increased. All had functional magnetic resonance imaging data collected during a facial emotion processing task. Functional activation and connectivity data were analyzed for different contrasts. The increasing PLE trajectory displayed more positive psychotic symptoms while the decreasing trajectory exhibited more negative symptoms relative to the control group. During face processing, both atypical trajectories displayed decreased activations of the right inferior frontal gyrus (IFG), while the increasing trajectory displayed a negative signal in the precentral gyrus. The increasing PLE trajectory also displayed impaired connectivity between the amygdala, ventromedial prefrontal cortex, and cerebellum, and between the IFG, precuneus, and temporal regions, while the decreasing trajectory exhibited reduced connectivity between the amygdala and visual regions during emotion processing. Both atypical PLE trajectories displayed alterations in brain regions involved in attention salience. While the increasing trajectory with more positive symptoms exhibited dysconnectivity in areas that influence emotion salience and face perception, the decreasing trajectory with more negative symptoms had impairments in visual information integration areas. These group-specific features might account for the differential symptom expression.

Abstract Males and females show different patterns of cannabis use and related psychosocial outcomes. However, the neuroanatomical substrates underlying such differences are poorly understood. The aim of this study was to map sex differences in the neurobiology (as indexed by brain volumes) of dependent and recreational cannabis use. We compared the volume of a priori regions of interest (i.e., amygdala, hippocampus, nucleus accumbens, insula, orbitofrontal cortex (OFC), anterior cingulate cortex and cerebellum) between 129 regular cannabis users (of whom 70 were recreational users and 59 cannabis dependent) and 114 controls recruited from the ENIGMA Addiction Working Group, accounting for intracranial volume, age, IQ, and alcohol and tobacco use. Dependent cannabis users, particularly females, had (marginally significant) smaller volumes of the lateral OFC and cerebellar white matter than recreational users and controls. In dependent (but not recreational) cannabis users, there was a significant association between female sex and smaller volumes of the cerebellar white matter and OFC. Volume of the OFC was also predicted by monthly standard drinks. No significant effects emerged the other brain regions of interest. Our findings warrant future multimodal studies that examine if sex and cannabis dependence are specific key drivers of neurobiological alterations in cannabis users. This, in turn, could help to identify neural pathways specifically involved in vulnerable cannabis users (e.g., females with cannabis dependence) and inform individually tailored neurobiological targets for treatment.

The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders.

In 2013, illegal drug use was responsible for 1.8% of years of life lost in the European Union, alcohol was responsible for 8.2% and tobacco for 18.2%, imposing economic burdens in excess of 2.5% of GDP. No single European country has optimal governance structures for reducing the harm done by nicotine, illegal drugs and alcohol, and existing ones are poorly designed, fragmented, and sometimes cause harm. Reporting the main science and policy conclusions of a transdisciplinary five-year analysis of the place of addictions in Europe, researchers from 67 scientific institutions addressed these problems by reframing an understanding of addictions.  A new paradigm needs to account for evolutionary evidence which suggests that humans are biologically predisposed to seek out drugs, and that, today, individuals face availability of high drug doses, consequently increasing the risk of harm.  New definitions need to acknowledge that the defining element of addictive drugs is 'heavy use over time', a concept that could replace the diagnostic artefact captured by the clinical term 'substance use disorder', thus opening the door for new substances to be considered such as sugar. Tools of quantitative risk assessment that recognize drugs as toxins could be further deployed to assess regulatory approaches to reducing harm. Re-designed governance of drugs requires embedding policy within a comprehensive societal well-being frame that encompasses a range of domains of well-being, including quality of life, material living conditions and sustainability over time; such a frame adds arguments to the inappropriateness of policies that criminalize individuals for using drugs and that continue to categorize certain drugs as illegal. A health footprint, modelled on the carbon footprint, and using quantitative measures such as years of life lost due to death or disability, could serve as the accountability tool that apportions responsibility for who and what causes drug-related harm.

In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.

ABSTRACT Our group has previously used laboratory and commercially developed assays to understand the IgG responses to SARS-CoV-2 antigens, including nucleocapsid (N), spike (S), and receptor binding domain (RBD), in Canadian blood donors. In this current study, we analyzed 17,428 available and previously characterized retention samples collected from April 2020 to March 2021. The analysis compared the characteristics of the Abbott SARS-CoV-2 IgG II Quant assay (Abbott anti-spike [S], Abbott, Chicago, IL) against four other IgG assays. The Abbott anti-S assay has a qualitative threshold of 50 AU/mL. The four comparator assays were the Abbott anti-nucleocapsid (N) assay and three commonly used Canadian in-house IgG enzyme-linked immunosorbent assays (ELISAs) recognizing distinct recombinant viral antigens, full-length spike glycoprotein, glycoprotein RBD, and nucleocapsid. The strongest qualitative relationship was between Sinai RBD and the Abbott anti-S assay (kappa, 0.707; standard error [SE] of kappa, 0.018; 95% confidence interval, 0.671 to 0.743). We then scored each previously characterized specimen as positive when two anti-SARS-COV-2 assays identified anti-SARS-CoV-2 IgG in the specimen. Using this composite reference standard approach, the sensitivity of the Abbott anti-S assay was 95.96% (95% confidence interval [CI], 93.27 to 97.63%). The specificity of the Abbott anti-S assay was 99.35% (95% CI, 99.21 to 99.46%). Our study provides context on the use of commonly used SARS-CoV-2 serologies in Canada and identifies how these assays qualitatively compare to newer commercial assays. Our next steps are to assess how well the Abbott anti-S assays quantitatively detect wild-type and SARS-CoV-2 variants of concern. IMPORTANCE We describe the qualitative test characteristics of the Abbott SARS-CoV-2 IgG II Quant assay against four other anti-SARS-CoV-2 IgG assays commonly used in Canada. Although there is no gold standard for identifying anti-SARS-CoV-2 seropositivity, aggregate standards can be used to assess seropositivity. In this study, we used a specimen bank of previously well-characterized specimens collected between April 2020 and March 2021. The Abbott anti-S assay showed the strongest qualitative relationship with a widely used laboratory-developed IgG assay for the SARS-CoV-2 receptor binding domain. Using the composite reference standard approach, we also showed that the Abbott anti-S assay was highly sensitive and specific. As new anti-SARS-CoV-2 assays are developed, it is important to compare their test characteristics against other assays that have been extensively used in prior research.

Abstract Background Alcohol use is a major public health challenge in France, where at the age of 17 half the young people report an episode of severe alcohol intoxication in the month preceding the survey. Numerous prevention programmes have a general objective, but in adolescence individual vulnerabilities towards addictions differ significantly with personality traits. Prevention targeting personality traits enables work on risk factors for addictive behaviours, and has shown genuine efficacy. Among existing programmes, PREVENTURE has shown an effect on the reduction in alcohol consumption by targeting four personality traits: impulsivity, sensation-seeking, negative thoughts and anxiety. This programme has been tested on samples recruited in adolescent populations in school environments, identifying adolescents at risk, but it has not been tested on a more targeted recruitment of adolescents seen in consultation. Methods The main hypothesis of this study is that the targeted prevention programme PREVENTURE will have an impact on the prevalence of binge-drinking episodes. The secondary hypotheses explore other factors such as associated substance use, anxiety and depression, as well as the acceptability of the programme. This article presents the study protocol of “PREVADO” study. We intend to assess the impact of the targeted intervention programme PREVENTURE on the prevalence of binge-drinking episodes among adolescents aged 14 to 17 years consulting in one of the participating centres or referred by a school doctor. The study will be prospective, randomised, controlled and open-label, and will comprise an intervention group and a control group. The adolescents will then be followed and assessed 1, 3, 6 and 12 months after the intervention. The study needs to include 700 subjects in order to reach 340 adolescents randomised, 170 in each group. It will concern 33 centres. Discussion This project could favour the targeting of addictive behaviours among vulnerable adolescents, and its application on a larger scale could be envisaged. Trial registration The Trial registration number is NCT04599270 , and it was registered on the ClinicalTrials.gov public website.

Abstract Males and females with alcohol dependence have distinct mental health and cognitive problems. Animal models of addiction postulate that the underlying neurobiological mechanisms are partially distinct, but there is little evidence of sex differences in humans with alcohol dependence as most neuroimaging studies have been conducted in males. We examined hippocampal and amygdala subregions in a large sample of 966 people from the ENIGMA Addiction Working Group. This comprised 643 people with alcohol dependence (225 females), and a comparison group of 323 people without alcohol dependence (98 females). Males with alcohol dependence had smaller volumes of the total amygdala and its basolateral nucleus than male controls, that exacerbated with alcohol dose. Alcohol dependence was also associated with smaller volumes of the hippocampus and its CA1 and subiculum subfield volumes in both males and females. In summary, hippocampal and amygdalar subregions may be sensitive to both shared and distinct mechanisms in alcohol-dependent males and females.

Since the sample size of a typical neuroimaging study lacks sufficient statistical power to explore unknown genomic associations with brain phenotypes, several international genetic imaging consortia have been organized in recent years to pool data across sites. The challenges and achievements of these consortia are considered here with the goal of leveraging these resources to study addiction. The authors of this review have joined together to form an Addiction working group within the framework of the ENIGMA project, a meta-analytic approach to multisite genetic imaging data. Collectively, the Addiction working group possesses neuroimaging and genomic data obtained from over 10,000 subjects. The deadline for contributing data to the first round of analyses occurred at the beginning of May 2015. The studies performed on this data should significantly impact our understanding of the genetic and neurobiological basis of addiction.

Juan Gabriel Vásquez: une archéologie du passé colombien récent es el primer volumen sobre la obra de Vásquez en su conjunto, el cual surgió después del homenaje internacional que se le rindió en la Universidad Paul-Valéry en Montpellier en 2015. Cuenta con 23 contribuciones en español y en francés de especialistas de horizontes diferentes, que siguen la cronología de las publicaciones del autor, con un ensayo del propio Vásquez, así como una entrevista inédita.