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Abstract Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of the variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

Abstract Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to limited large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages, and the neurobiological basis underlying individual heterogeneity remains poorly understood. Here we identify, using the IMAGEN adolescent cohort followed up over 9 years (14–23 y), three groups of adolescents characterized by distinct developmental patterns of whole-brain gray matter volume (GMV). Group 1 show continuously decreasing GMV associated with higher neurocognitive performances than the other two groups during adolescence. Group 2 exhibit a slower rate of GMV decrease and lower neurocognitive performances compared with Group 1, which was associated with epigenetic differences and greater environmental burden. Group 3 show increasing GMV and lower baseline neurocognitive performances due to a genetic variation. Using the UK Biobank, we show these differences may be attenuated in mid-to-late adulthood. Our study reveals clusters of adolescent neurodevelopment based on GMV and the potential long-term impact.

Adolescence is a crucial period for physical and psychological development. The impact of negative life events represents a risk factor for the onset of neuropsychiatric disorders. This study aims to investigate the relationship between negative life events and structural brain connectivity, considering both graph theory and connectivity strength. A group (n = 487) of adolescents from the IMAGEN Consortium was divided into Low and High Stress groups. Brain networks were extracted at an individual level, based on morphological similarity between grey matter regions with regions defined using an atlas-based region of interest (ROI) approach. Between-group comparisons were performed with global and local graph theory measures in a range of sparsity levels. The analysis was also performed in a larger sample of adolescents (n = 976) to examine linear correlations between stress level and network measures. Connectivity strength differences were investigated with network-based statistics. Negative life events were not found to be a factor influencing global network measures at any sparsity level. At local network level, between-group differences were found in centrality measures of the left somato-motor network (a decrease of betweenness centrality was seen at sparsity 5%), of the bilateral central visual and the left dorsal attention network (increase of degree at sparsity 10% at sparsity 30% respectively). Network-based statistics analysis showed an increase in connectivity strength in the High stress group in edges connecting the dorsal attention, limbic and salience networks. This study suggests negative life events alone do not alter structural connectivity globally, but they are associated to connectivity properties in areas involved in emotion and attention.

Introduction:A growing literature has shown that exposure to adverse life events during childhood or adolescence is associated with the presence of psychotic-like experiences (PLEs), which is in turn associated with the risk of psychotic outcomes. Ruminative thinking, i.e., the tendency to dwell on particular issues or ideas, may affect the perceived aversiveness and ability to cope with adverse life events. However, the role that rumination plays in the relationship between adverse life events and the presence of PLEs remains unclear. The purpose of this study is to assess the association between adverse life events and PLEs in a longitudinal sample of young adults and adolescents, and to investigate whether this relationship is mediated by ruminative thinking.MethodsWe used a longitudinal naturalistic sample of 706 volunteers assessed at ages 18 and 22 years, within the Imagen consortium. Lifetime occurrence of adverse life events (i.e., events perceived as strongly negative by participants) was investigated using the Life Events Questionnaire. The Community Assessment of Psychic Experience (CAPE-42) served to assess the presence of PLEs, while ruminative thinking was investigated through the Ruminative Response Scale.ResultsResults showed that both frequency of PLEs and their persistence over time were associated with greater adverse life events exposure (r = 0.32, p < 0.001 and F1 = 9.8; p < 0.001, respectively) and greater ruminative response (r = 0.66, p < 0.001 and F1 = 94.9; p < 0.001, respectively). Mediation analyses showed that relationship between adverse life events and PLEs frequency was partially mediated by rumination (direct effect Z: 5.4, p < 0.001; indirect effect Z: 6.9, p < 0.001; total effect Z: 5.9, p < 0.001). Considering changes between the two assessment timepoints, relationship between PLEs variation between 18 and 22 years and adverse life events occurred during the same period was partially mediated by changes in rumination (direct effect Z: 2.8, p < 0.005; indirect effect Z: 4.3, p < 0.001; total effect Z: 4.3; p < 0.001).DiscussionOverall, our findings confirm that the presence of adverse life events may increase the risk of experiencing PLEs in healthy individuals and suggest that dysfunctional coping strategies, such as ruminative thinking, may be related to psychosis proneness. Results do not disentangle whether individuals with greater risk for psychosis tend to ruminate more or whether rumination exacerbates psychosis risk.

Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the ‘last in, first out’ mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.

Substance use, including cigarettes and cannabis, is associated with poorer sustained attention in late adolescence and early adulthood. Previous studies were predominantly cross-sectional or under-powered and could not indicate if impairment in sustained attention was a predictor of substance use or a marker of the inclination to engage in such behavior. This study explored the relationship between sustained attention and substance use across a longitudinal span from ages 14 to 23 in over 1000 participants. Behaviors and brain connectivity associated with diminished sustained attention at age 14 predicted subsequent increases in cannabis and cigarette smoking, establishing sustained attention as a robust biomarker for vulnerability to substance use. Individual differences in network strength relevant to sustained attention were preserved across developmental stages and sustained attention networks generalized to participants in an external dataset. In summary, brain networks of sustained attention are robust, consistent, and able to predict aspects of later substance use.

Abstract Background Early negative life events (NLE) have long‐lasting influences on neurodevelopment and psychopathology. Reduced orbitofrontal cortex (OFC) thickness was frequently associated with NLE and depressive symptoms. OFC thinning might mediate the effect of NLE on depressive symptoms, although few longitudinal studies exist. Using a complete longitudinal design with four time points, we examined whether NLE during childhood and early adolescence predict depressive symptoms in young adulthood through accelerated OFC thinning across adolescence. Methods We acquired structural MRI from 321 participants at two sites across four time points from ages 14 to 22. We measured NLE with the Life Events Questionnaire at the first time point and depressive symptoms with the Center for Epidemiologic Studies Depression Scale at the fourth time point. Modeling latent growth curves, we tested whether OFC thinning mediates the effect of NLE on depressive symptoms. Results A higher burden of NLE, a thicker OFC at the age of 14, and an accelerated OFC thinning across adolescence predicted young adults' depressive symptoms. We did not identify an effect of NLE on OFC thickness nor OFC thickness mediating effects of NLE on depressive symptoms. Conclusions Using a complete longitudinal design with four waves, we show that NLE in childhood and early adolescence predict depressive symptoms in the long term. Results indicate that an accelerated OFC thinning may precede depressive symptoms. Assessment of early additionally to acute NLEs and neurodevelopment may be warranted in clinical settings to identify risk factors for depression.

Summary: During late adolescence, the brain undergoes ontogenic organization altering subcortical-cortical circuitry. This includes regions implicated in pain chronicity, and thus alterations in the adolescent ontogenic organization could predispose to pain chronicity in adulthood - however, evidence is lacking. Using resting-state functional magnetic resonance imaging from a large European longitudinal adolescent cohort and an adult cohort with and without chronic pain, we examined links between painful symptoms and brain connectivity. During late adolescence, thalamo-, caudate-, and red nucleus-cortical connectivity were positively and subthalamo-cortical connectivity negatively associated with painful symptoms. Thalamo-cortical connectivity, but also subthalamo-cortical connectivity, was increased in adults with chronic pain compared to healthy controls. Our results indicate a shared basis in basothalamo-cortical circuitries between adolescent painful symptomatology and adult pain chronicity, with the subthalamic pathway being differentially involved, potentially due to a hyperconnected thalamo-cortical pathway in chronic pain and ontogeny-driven organization. This can inform neuromodulation-based prevention and early intervention.

Although many studies of the adolescent brain identified positive associations between cognitive abilities and cortical thickness, little is known about mechanisms underlying such brain-behavior relationships. With experience-induced plasticity playing an important role in shaping the cerebral cortex throughout life, it is likely that some of the inter-individual variations in cortical thickness could be explained by genetic variations in relevant molecular processes, as indexed by a polygenic score of neuronal plasticity (PGS-NP). Here, we studied associations between PGS-NP, cognitive abilities, and thickness of the cerebral cortex, estimated from magnetic resonance images, in the Saguenay Youth Study (SYS, 533 females, 496 males: age=15.0 ± 1.8 years of age; cross-sectional), and the IMAGEN Study (566 females, 556 males; between 14 and 19 years; longitudinal). Using Gene Ontology, we first identified 199 genes implicated in neuronal plasticity, which mapped to 155,600 single nucleotide polymorphisms (SNPs). Second, we estimated their effect sizes from an educational attainment meta-GWAS to build a PGS-NP. Third, we examined a possible moderating role of PGS-NP in the relationship between performance intelligence quotient (PIQ), and its subtests, and the thickness of 34 cortical regions.In SYS, we observed a significant interaction between PGS-NP and object assembly vis-à-vis thickness in male adolescents (p = 0.026). A median-split analysis showed that, in males with a ‘high’ PGS-NP, stronger associations between object assembly and thickness were found in regions with larger age-related changes in thickness (r = 0.55, p = 0.00075). Although the interaction between PIQ and PGS-NP was non-significant (p = 0.064), we performed a similar median-split analysis. Again, in the high PGS-NP males, positive associations between PIQ and thickness were observed in regions with larger age-related changes in thickness (r = 0.40, p = 0.018). In the IMAGEN cohort, we did not replicate the first set of results (interaction between PGS-NP and cognitive abilities via-a-vis cortical thickness) while we did observe the same relationship between the brain-behaviour relationship and (longitudinal) changes in cortical thickness (Matrix reasoning: r = 0.63, p = 6.5e-05). No statistically significant results were observed in female adolescents in either cohort. Overall, these cross-sectional and longitudinal results suggest that molecular mechanisms involved in neuronal plasticity may contribute to inter-individual variations of cortical thickness related to cognitive abilities during adolescence in a sex-specific manner.

Abstract While there is substantial evidence that cannabis use is associated with differences in human brain development, most of this evidence is correlational in nature. Bayesian causal network (BCN) modeling attempts to identify probable causal relationships in correlational data using conditional probabilities to estimate directional associations between a set of interrelated variables. In this study, we employed BCN modeling in 637 adolescents from the IMAGEN study who were cannabis naïve at age 14 to provide evidence that the accelerated prefrontal cortical thinning found previously in adolescent cannabis users by Albaugh et al. [1] is a result of cannabis use causally affecting neurodevelopment. BCNs incorporated data on cannabis use, prefrontal cortical thickness, and other factors related to both brain development and cannabis use, including demographics, psychopathology, childhood adversity, and other substance use. All BCN algorithms strongly suggested a directional relationship from adolescent cannabis use to accelerated cortical thinning. While BCN modeling alone does not prove a causal relationship, these results are consistent with a body of animal and human research suggesting that adolescent cannabis use adversely affects brain development.

BackgroundWhether alteration in regional brain volumes can be detected in Type A alcoholics both at baseline and after a long follow-up remains to be confirmed. Therefore, we examined volume alterations at baseline, and longitudinal changes in a small follow-up subsample.MethodsIn total of 26 patients and 24 healthy controls were assessed at baseline using magnetic resonance imaging and voxel-based morphometry, among which 17 patients and 6 controls were re-evaluated 7 years later. At baseline, regional cerebral volumes of patients were compared to controls. At follow-up, three groups were compared: abstainers (n = 11, more than 2 years of abstinence), relapsers (n = 6,

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.

Binge drinking behavior in early adulthood can be predicted from brain structure during early adolescence with an accuracy of above 70%. We investigated whether this accurate prospective prediction of alcohol misuse behavior can be explained by psychometric variables such as personality traits or mental health comorbidities in a data-driven approach.We analyzed a subset of adolescents who did not have any prior binge drinking experience at age 14 (IMAGEN dataset, n = 555, 52.61% female). Participants underwent structural magnetic resonance imaging at age 14, binge drinking assessments at ages 14 and 22, and psychometric questionnaire assessments at ages 14 and 22. We derived structural brain features from T1-weighted magnetic resonance and diffusion tensor imaging. Using Machine Learning (ML), we predicted binge drinking (age 22) from brain structure (age 14) and used counterbalancing with oversampling to systematically control for 110 + variables from a wide range of social, personality, and other psychometric characteristics potentially associated with binge drinking. We evaluated if controlling for any variable resulted in a significant reduction in ML prediction accuracy.Sensation-seeking (-13.98 ± 1.68%), assessed via the Substance Use Risk Profile Scale at age 14, and uncontrolled eating (-13.98 ± 3.28%), assessed via the Three-Factor-Eating-Questionnaire at age 22, led to significant reductions in mean balanced prediction accuracy upon controlling for them. Thus, sensation-seeking and binge eating could partially explain the prediction of future binge drinking from adolescent brain structure.Our findings suggest that binge drinking and binge eating at age 22 share common neurobiological precursors discovered by the ML model. These neurobiological precursors seem to be associated with sensation-seeking at age 14. Our results facilitate early detection of increased risk for binge drinking and inform future clinical research in trans-diagnostic prevention approaches for adolescent alcohol misuse.

It has been recognized that the efficacy of TMS-based modulation may depend on the network profile of the stimulated regions throughout the brain. However, what profile of this stimulation network optimally benefits treatment outcomes is yet to be addressed. The answer to the question is crucial for informing network-based optimization of stimulation parameters, such as coil placement, in TMS treatments. In this study, we aimed to investigate the feasibility of taking a disease-specific network as the target of stimulation network for guiding individualized coil placement in TMS treatments. We present here a novel network-based model for TMS targeting of the pathological network. First, combining E-field modeling and resting-state functional connectivity, stimulation networks were modeled from locations and orientations of the TMS coil. Second, the spatial anti-correlation between the stimulation network and the pathological network of a given disease was hypothesized to predict the treatment outcome. The proposed model was validated to predict treatment efficacy from the position and orientation of TMS coils in two depression cohorts and one schizophrenia cohort with auditory verbal hallucinations. We further demonstrate the utility of the proposed model in guiding individualized TMS treatment for psychiatric disorders. In this proof-of-concept study, we demonstrated the feasibility of the novel network-based targeting strategy that uses the whole-brain, system-level abnormity of a specific psychiatric disease as a target. Results based on empirical data suggest that the strategy may potentially be utilized to identify individualized coil parameters for maximal therapeutic effects.

Abstract Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions.

Alcohol misuse during adolescence (AAM) has been associated with disruptive development of adolescent brains. In this longitudinal machine learning (ML) study, we could predict AAM significantly from brain structure (T1-weighted imaging and DTI) with accuracies of 73 -78% in the IMAGEN dataset (n∼1182). Our results not only show that structural differences in brain can predict AAM, but also suggests that such differences might precede AAM behavior in the data. We predicted 10 phenotypes of AAM at age 22 using brain MRI features at ages 14, 19, and 22. Binge drinking was found to be the most predictable phenotype. The most informative brain features were located in the ventricular CSF, and in white matter tracts of the corpus callosum, internal capsule, and brain stem. In the cortex, they were spread across the occipital, frontal, and temporal lobes and in the cingulate cortex. We also experimented with four different ML models and several confound control techniques. Support Vector Machine (SVM) with rbf kernel and Gradient Boosting consistently performed better than the linear models, linear SVM and Logistic Regression. Our study also demonstrates how the choice of the predicted phenotype, ML model, and confound correction technique are all crucial decisions in an explorative ML study analyzing psychiatric disorders with small effect sizes such as AAM.

The ability to extract cues from faces is fundamental for social animals, including humans. An individual's profile of functional connectivity across a face network can be shaped by common organizing principles, stable individual traits, and time-varying mental states. In the present study, we used data obtained with functional magnetic resonance imaging in two cohorts, IMAGEN (N = 534) and ALSPAC (N = 465), to investigate - both at group and individual levels - the consistency of the regional profile of functional connectivity across populations (IMAGEN, ALSPAC) and time (Visits 1 to 3 in IMAGEN; age 14 to 22 years). At the group level, we found a robust canonical profile of connectivity both across populations and time. At the individual level, connectivity profiles deviated from the canonical profile, and the magnitude of this deviation related to the presence of psychopathology. These findings suggest that the brain processes faces in a highly stereotypical manner, and that the deviations from this normative pattern may be related to the risk of mental illness.

Mixed findings exist in studies comparing brain responses to reward in adolescents and adults. Here we examined the trajectories of brain response, functional connectivity and task-modulated network properties during reward processing with a large-sample longitudinal design. Participants from the IMAGEN study performed a Monetary Incentive Delay task during fMRI at timepoint 1 (T1; n = 1304, mean age=14.44 years old) and timepoint 2 (T2; n = 1241, mean age=19.09 years). The Alcohol Use Disorders Identification Test (AUDIT) was administrated at both T1 and T2 to assess a participant’s alcohol use during the past year. Voxel-wise linear mixed effect models were used to compare whole brain response as well as functional connectivity of the ventral striatum (VS) during reward anticipation (large reward vs no-reward cue) between T1 and T2. In addition, task-modulated networks were constructed using generalized psychophysiological interaction analysis and summarized with graph theory metrics. To explore alcohol use in relation to development, participants with no/low alcohol use at T1 but increased alcohol use to hazardous use level at T2 (i.e., participants with AUDIT≤2 at T1 and ≥8 at T2) were compared against those with consistently low scores (i.e., participants with AUDIT≤2 at T1 and ≤7 at T2). Across the whole sample, lower brain response during reward anticipation was observed at T2 compared with T1 in bilateral caudate nucleus, VS, thalamus, midbrain, dorsal anterior cingulate as well as left precentral and postcentral gyrus. Conversely, greater response was observed bilaterally in the inferior and middle frontal gyrus and right precentral and postcentral gyrus at T2 (vs. T1). Increased functional connectivity with VS was found in frontal, temporal, parietal and occipital regions at T2. Graph theory metrics of the task-modulated network showed higher inter-regional connectivity and topological efficiency at T2. Interactive effects between time (T1 vs. T2) and alcohol use group (low vs. high) on the functional connectivity were observed between left middle temporal gyrus and right VS and the characteristic shortest path length of the task-modulated networks. Collectively, these results demonstrate the utility of the MID task as a probe of typical brain response and network properties during development and of differences in these features related to adolescent drinking, a reward-related behaviour associated with heightened risk for future negative health outcomes.

Adolescence is a period of vulnerability for the maturation of gray matter (GM) and also for the onset of psychiatric disorders such as major depressive disorder (MDD), bipolar disorder and schizophrenia. Chronic neuroinflammation is considered to play a role in the etiology of these illnesses. However, the involvement of neuroinflammation in the observed link between regional GM volume reductions and psychiatric symptoms is not established yet. Here, we investigated a possible common immune-related genetic link between these two phenomena in european adolescents recruited from the community. Hippocampal and medial prefrontal cortex (mPFC) were defined a priori as regions of interest (ROIs). Their GM volumes were extracted in 1,563 14-year-olds from the IMAGEN database. We found a set of 26 SNPs that correlated with the hippocampal volumes and 29 with the mPFC volumes at age 14. We formed two ROI-Related Immune-gene scores (RRI) with the inflammation SNPs that correlated to hippocampal GM volume and to mPFC GM volume. The predictive ability of both RRIs with regards to the presence of psychiatric symptoms at age 18 was investigated by correlating the RRIs with psychometric questionnaires obtained at age 18. The RRIs (but not control scores constructed with random SNPs) correlated with the presence of depressive symptoms, positive psychotic symptoms, and externalizing symptoms in later adolescence. In addition, the effect of childhood maltreatment, one of the major environmental risk factors for depression and other mental disorders, interacted with the RRI effect. We next sought to validate this finding by investigating our set of inflammatory genes in a translational animal model of early life adversity. Mice were subjected to a protocol of maternal separation at an early post-natal age. We evaluated depressive behaviors in separated and non-separated mice at adolescence and their correlations with the concomitant expression of our genes in whole blood samples. We show that in mice, early life adversity affected the expression of our set of genes in peripheral blood, and that levels of expression correlated with symptoms of negative affect in adolescence. Overall, our translational findings in adolescent mice and humans provide a novel validated gene-set of immune-related genes for further research in the early stages of mood disorders.

Background: Autistic traits are commonly viewed as dimensional in nature, and as continuously distributed in the general population. In this respect, the identification of predictive values of markers such as subtle autism-related alterations in brain morphology for parameter values of autistic traits could increase our understanding of this dimensional occasion. However, currently, very little is known about how these traits correspond to alterations in brain morphology in typically developing individuals, particularly during a time period where changes due to brain development processes do not provide a bias. Therefore, in the present study, we analyzed brain volume, cortical thickness (CT) and surface area (SA) in a cohort of 14–15-year-old adolescents (N = 285, female: N = 162) and tested their predictive value for autistic traits, assessed with the social responsiveness scale (SRS) two years later at the age of 16–17 years, using a regression-based approach. We found that autistic traits were significantly predicted by volumetric changes in the amygdala (r = 0.181), cerebellum (r = 0.128) and hippocampus (r = −0.181, r = −0.203), both in boys and girls. Moreover, the CT of the superior frontal region was negatively correlated (r = −0.144) with SRS scores. Furthermore, we observed a significant association between the SRS total score and smaller left putamen volume, specifically in boys (r = −0.217), but not in girls. Our findings suggest that neural correlates of autistic traits also seem to lie on a continuum in the general population, are determined by limbic–striatal neuroanatomical brain areas, and are partly dependent on sex. As we imaged adolescents from a large population-based cohort within a small age range, these data may help to increase the understanding of autistic-like occasions in otherwise typically developing individuals.

Changing sleep rhythms in adolescents often lead to sleep deficits and a delay in sleep timing between weekdays and weekends. The adolescent brain, and in particular the rapidly developing structures involved in emotional control, are vulnerable to external and internal factors. In our previous study in adolescents at age 14, we observed a strong relationship between weekend sleep schedules and regional medial prefrontal cortex grey matter volumes. Here, we aimed to assess whether this relationship remained in this group of adolescents of the general population at the age of 16 (n = 101; mean age 16.8 years; 55% girls). We further examined grey matter volumes in the hippocampi and the amygdalae, calculated with voxel-based morphometry. In addition, we investigated the relationships between sleep habits, assessed with self-reports, and regional grey matter volumes, and psychological functioning, assessed with the Strengths and Difficulties Questionnaire and tests on working memory and impulsivity. Later weekend wake-up times were associated with smaller grey matter volumes in the medial prefrontal cortex and the amygdalae, and greater weekend delays in wake-up time were associated with smaller grey matter volumes in the right hippocampus and amygdala. The medial prefrontal cortex region mediated the correlation between weekend wake up time and externalising symptoms. Paying attention to regular sleep habits during adolescence could act as a protective factor against the emergence of psychopathology via enabling favourable brain development.

Replicating results (i.e. obtaining consistent results using a new independent dataset) is an essential part of good science. As replicability has consequences for theories derived from empirical studies, it is of utmost importance to better understand the underlying mechanisms influencing it. A popular tool for non-invasive neuroimaging studies is functional magnetic resonance imaging (fMRI). While the effect of underpowered studies is well documented, the empirical assessment of the interplay between sample size and replicability of results for task-based fMRI studies remains limited. In this work, we extend existing work on this assessment in two ways. Firstly, we use a large database of 1400 subjects performing four types of tasks from the IMAGEN project to subsample a series of independent samples of increasing size. Secondly, replicability is evaluated using a multi-dimensional framework consisting of 3 different measures: (un)conditional test-retest reliability, coherence and stability. We demonstrate not only a positive effect of sample size, but also a trade-off between spatial resolution and replicability. When replicability is assessed voxelwise or when observing small areas of activation, a larger sample size than typically used in fMRI is required to replicate results. On the other hand, when focussing on clusters of voxels, we observe a higher replicability. In addition, we observe variability in the size of clusters of activation between experimental paradigms or contrasts of parameter estimates within these.

Though adolescence is a time of emerging sex differences in emotions, sex-related differences in the anatomy of the maturing brain has been under-explored over this period. The aim of this study was to investigate whether puberty and sexual differentiation in brain maturation could explain emotional differences between girls and boys during adolescence. We adapted a dedicated longitudinal pipeline to process structural and diffusion images from 335 typically developing adolescents between 14 and 16 years. We used voxel-based and Regions of Interest approaches to explore sex and puberty effects on brain and behavioral changes during adolescence. Sexual differences in brain maturation were characterized by amygdala and hippocampal volume increase in boys and decrease in girls. These changes were mediating the sexual differences in positive emotional regulation as illustrated by positive attributes increase in boys and decrease in girls. Moreover, the differential maturation rates between the limbic system and the prefrontal cortex highlighted the delayed maturation in boys compared to girls. This is the first study to show the sex effects on the differential cortico/subcortical maturation rates and the interaction between sex and puberty in the limbic system maturation related to positive attributes, reported as being protective from emotional disorders.

Incomplete hippocampal inversion (IHI), also called hippocampal malrotation, is an atypical presentation of the hippocampus present in about 20% of healthy individuals. Here we conducted the first genome-wide association study (GWAS) in IHI to elucidate the genetic underpinnings that may contribute to the incomplete inversion during brain development. A total of 1381 subjects contributed to the discovery cohort obtained from the IMAGEN database. The incidence rate of IHI was 26.1%. Loci with P

In a group of 831 participants from the general population in the Human Connectome Project, smokers exhibited low overall functional connectivity, and more specifically of the lateral orbitofrontal cortex which is associated with non-reward mechanisms, the adjacent inferior frontal gyrus, and the precuneus. Participants who drank a high amount had overall increases in resting state functional connectivity, and specific increases in reward-related systems including the medial orbitofrontal cortex and the cingulate cortex. Increased impulsivity was found in smokers, associated with decreased functional connectivity of the non-reward-related lateral orbitofrontal cortex; and increased impulsivity was found in high amount drinkers, associated with increased functional connectivity of the reward-related medial orbitofrontal cortex. The main findings were cross-validated in an independent longitudinal dataset with 1176 participants, IMAGEN. Further, the functional connectivities in 14-year-old non-smokers (and also in female low-drinkers) were related to who would smoke or drink at age 19. An implication is that these differences in brain functional connectivities play a role in smoking and drinking, together with other factors.

Cannabis use in adolescence may be characterized by differences in the neural basis of affective processing. In this study, we used an fMRI affective face processing task to compare a large group (n = 70) of 14-year olds with a history of cannabis use to a group (n = 70) of never-using controls matched on numerous characteristics including IQ, SES, alcohol and cigarette use. The task contained short movies displaying angry and neutral faces. Results indicated that cannabis users had greater reactivity in the bilateral amygdalae to angry faces than neutral faces, an effect that was not observed in their abstinent peers. In contrast, activity levels in the cannabis users in cortical areas including the right temporal-parietal junction and bilateral dorsolateral prefrontal cortex did not discriminate between the two face conditions, but did differ in controls. Results did not change after excluding subjects with any psychiatric symptomology. Given the high density of cannabinoid receptors in the amygdala, our findings suggest cannabis use in early adolescence is associated with hypersensitivity to signals of threat. Hypersensitivity to negative affect in adolescence may place the subject at-risk for mood disorders in adulthood.

ObjectiveTo investigate the role of personality factors and attentional biases towards emotional faces, in establishing concurrent and prospective risk for mental disorder diagnosis in adolescence.MethodData were obtained as part of the IMAGEN study, conducted across 8 European sites, with a community sample of 2257 adolescents. At 14 years, participants completed an emotional variant of the dot-probe task, as well two personality measures, namely the Substance Use Risk Profile Scale and the revised NEO Personality Inventory. At 14 and 16 years, participants and their parents were interviewed to determine symptoms of mental disorders.ResultsPersonality traits were general and specific risk indicators for mental disorders at 14 years. Increased specificity was obtained when investigating the likelihood of mental disorders over a 2-year period, with the Substance Use Risk Profile Scale showing incremental validity over the NEO Personality Inventory. Attentional biases to emotional faces did not characterise or predict mental disorders examined in the current sample.DiscussionPersonality traits can indicate concurrent and prospective risk for mental disorders in a community youth sample, and identify at-risk youth beyond the impact of baseline symptoms. This study does not support the hypothesis that attentional biases mediate the relationship between personality and psychopathology in a community sample. Task and sample characteristics that contribute to differing results among studies are discussed.

Playing video games is a common recreational activity of adolescents. Recent research associated frequent video game playing with improvements in cognitive functions. Improvements in cognition have been related to grey matter changes in prefrontal cortex. However, a fine-grained analysis of human brain structure in relation to video gaming is lacking. In magnetic resonance imaging scans of 152 14-year old adolescents, FreeSurfer was used to estimate cortical thickness. Cortical thickness across the whole cortical surface was correlated with self-reported duration of video gaming (hours per week). A robust positive association between cortical thickness and video gaming duration was observed in left dorsolateral prefrontal cortex (DLPFC) and left frontal eye fields (FEFs). No regions showed cortical thinning in association with video gaming frequency. DLPFC is the core correlate of executive control and strategic planning which in turn are essential cognitive domains for successful video gaming. The FEFs are a key region involved in visuo-motor integration important for programming and execution of eye movements and allocation of visuo-spatial attention, processes engaged extensively in video games. The results may represent the biological basis of previously reported cognitive improvements due to video game play. Whether or not these results represent a-priori characteristics or consequences of video gaming should be studied in future longitudinal investigations.