Qi L et al. Differing impact of the COVID-19 pandemic on youth mental health: combined population and clinical study. BJPsych Open (2023).
In the population cohort, depression and eating disorder symptoms increased during the pandemic, respectively. By contrast, these remained high over time in the clinical cohort. Conversely, trajectories of alcohol misuse were similar in both cohorts, decreasing continuously during the pandemic. Pre-pandemic symptom severity predicted the observed mental health trajectories in the population cohort. Surprisingly, being relatively healthy predicted increases in depression and eating disorder symptoms and in body mass index. By contrast, those initially at higher risk for depression or eating disorders reported a lasting decrease. Thus, healthier young people may be at greater risk of developing depressive or eating disorder symptoms during the COVID-19 pandemic.

Vaidya N et al. Neurocognitive Analysis of Low-level Arsenic Exposure and Executive Function Mediated by Brain Anomalies Among Children, Adolescents, and Young Adults in India. JAMA Network Open. (2023)

Xu J et al. Effects of urban living environments on mental health in adults. Nature Medicine. (2023)

Xie C et al. A shared neural basis underlying psychiatric comorbidity. Nature Medicine. (2023)
We identify a reproducible and general neural basis underlying symptoms of multiple mental health disorders, bridging multidimensional evidence from behavioral, neuroimaging and genetic substrates. These findings may help to develop new therapeutic interventions for psychiatric comorbidities.

Xu J et al. Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing. Nature Human Behaviour. (2023)

Zuo Zhang et al. Development of Disordered Eating Behaviors and Comorbid Depressive Symptoms in Adolescence: Neural and Psychopathological Predictors. Biological Psychiatry (2021).
Our findings suggest that alterations in frontal brain circuits are part of the shared etiology among eating disorders, attention-deficit/hyperactivity disorder, conduct disorder, and depression. We highlight the importance of a transdiagnostic approach to treating these conditions.

Jia et. al.  'Neurobehavioural characterisation of reinforcement-related behaviour', Nature Human Behaviour (2020)
We describe the identification of stratification markers of externalising symptoms based on functional brain activity during reinforcement processes. Neural network underlying hyperactivity and inattention of ADHD while similar during reward anticipation, were distinct during motor inhibition, suggesting different neural mechanisms underlying distinct ADHD behaviours.

Robinson L et al. Association of Genetic and Phenotypic Assessments With Onset of Disordered Eating Behaviors and Comorbid Mental Health Problems Among Adolescents. JAMA Network Open (2020).
The findings of this study delineate temporal associations and shared etiologies among disordered eating behaviour and other mental health disorders. We emphasize the potential of genetic and phenotypical assessments of obesity, behavioral disorders, and neuroticism to improve early and differential diagnosis of eating disorders.

Alex Ing et. al. 'Identifying neurobehavioural symptom groups based on shared brain mechanisms', Nature Human Behaviour (2019).
We discovered symptom clusters with shared biology. This paper describes a new method to find relations between behavioral symptoms, and neuroimaging measures of brain structure and function. By characterising behavioral symptom groups based on shared neural mechanisms, the results provide a framework for developing a classification system for psychiatric illness, which is based on quantitative neurobehavioural measures.

Evangelou et al., 'Novel alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders', Nature Human Behaviour (2019).
In a large GWAS meta-analysis we investigated 480.842 cases participants to decipher the genetic architecture of alcohol intake. The study identified genetic pathways associated with alcohol consumption and suggested shared genetic mechanisms with neuropsychiatric disorders including schizophrenia.

Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain networks to parse the heterogeneity of depressive symptomatology in a large adolescent sample.

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.

In genetics, aggregation of many loci with small effect sizes into a single score improved prediction. Nevertheless, studies applying easily replicable weighted scores to neuroimaging data are lacking. Our aim was to assess the reliability and validity of the Neuroimaging Association Score (NAS), which combines information from structural brain features previously linked to mental disorders. Participants were 726 youth (aged 6–14) from two cities in Brazil who underwent MRI and psychopathology assessment at baseline and 387 at 3-year follow-up

Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol.

Personality traits have been associated with eating disorders (EDs) and comorbidities. However, it is unclear which personality profiles are premorbid risk rather than diagnostic markers.

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.

Few studies have investigated the link between putative biomarkers of attention-deficit/hyperactivity disorder (ADHD) symptomatology and genetic risk for ADHD. To address this, we investigate the degree to which ADHD symptomatology is associated with white matter microstructure and cerebral cortical thickness in a large population-based sample of adolescents. Critically, we then test the extent to which multimodal correlates of ADHD symptomatology are related to ADHD polygenic risk score (PRS). Neuroimaging, genetic, and behavioral data were obtained from the IMAGEN study. A dimensional ADHD composite score was derived from multi-informant ratings of ADHD symptomatology. Using tract-based spatial statistics, whole brain voxel-wise regressions between fractional anisotropy (FA) and ADHD composite score were calculated.

Question  Is there any genetic variant associated with adolescent brain development that can inform psychopathology of schizophrenia?

Findings  In this imaging genetics study of brain structure, a significant association between a missense mutation in SLC39A8 (a gene previously associated with schizophrenia) and gray matter volume in putamen was discovered and replicated using 10 411 healthy participants from 5 independent studies. Compared with healthy control individuals, such association was significantly weakened in both patients with schizophrenia and unaffected siblings.

Meaning  Common genetic variant indicates an involvement of neuronal ion transport in both pathophysiology of schizophrenia and structural development of putamen.

ile 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 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.

Background Alzheimer’s disease (AD)-related neuropathological changes can occur decades before clinical symptoms. We aimed to investigate whether neurodevelopment and/or neurodegeneration affects the risk of AD, through reducing structural brain reserve and/or increasing brain atrophy, respectively.

Methods We used bidirectional two-sample Mendelian randomisation to estimate the effects between genetic liability to AD and global and regional cortical thickness, estimated total intracranial volume, volume of subcortical structures and total white matter in 37 680 participants aged 8–81 years across 5 independent cohorts (Adolescent Brain Cognitive Development, Generation R, IMAGEN, Avon Longitudinal Study of Parents and Children and UK Biobank). We also examined the effects of global and regional cortical thickness and subcortical volumes from the Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA) Consortium on AD risk in up to 37 741 participants.

Results Our findings show that AD risk alleles have an age-dependent effect on a range of cortical and subcortical brain measures that starts in mid-life, in non-clinical populations. Evidence for such effects across childhood and young adulthood is weak. Some of the identified structures are not typically implicated in AD, such as those in the striatum (eg, thalamus), with consistent effects from childhood to late adulthood. There was little evidence to suggest brain morphology alters AD risk.

Conclusions Genetic liability to AD is likely to affect risk of AD primarily through mechanisms affecting indicators of brain morphology in later life, rather than structural brain reserve. Future studies with repeated measures are required for a better understanding and certainty of the mechanisms at play.

Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages and the neurobiological basis underlying individual heterogeneity remains poorly understood. Using structural magnetic resonance imaging from the IMAGEN cohort (n=1,543), we show that adolescents can be clustered into three groups defined by distinct developmental patterns of whole-brain gray matter volume (GMV). Genetic and epigenetic determinants of group clustering and long-term impacts of neurodevelopment in mid-to-late adulthood were investigated using data from the ABCD, IMAGEN and UK Biobank cohorts. Group 1, characterized by continuously decreasing GMV, showed generally the best neurocognitive performances during adolescence. Compared to Group 1, Group 2 exhibited a slower rate of GMV decrease and worsened neurocognitive development, which was associated with epigenetic changes and greater environmental burden. Further, Group 3 showed increasing GMV and delayed neurocognitive development during adolescence due to a genetic variation, while these disadvantages were attenuated in mid-to-late adulthood. In summary, our study revealed novel clusters of adolescent structural neurodevelopment and suggested that genetically-predicted delayed neurodevelopment has limited long-term effects on mental well-being and socio-economic outcomes later in life. Our results could inform future research on policy interventions aimed at reducing the financial and emotional burden of mental illness.