The IMAGEN study: a decade of imaging genetics in adolescents

The HEALthy Brain and Child Development Study (HBCD): NIH collaboration to understand the impacts of prenatal and early life experiences on brain development

The human brain undergoes rapid development during the first years of life. Beginning in utero, a wide array of biological, social, and environmental factors can have lasting impacts on brain structure and function. To understand how prenatal and early life experiences alter neurodevelopmental trajectories and shape health outcomes, several NIH Institutes, Centers, and Offices collaborated to support and launch the HEALthy Brain and Child Development (HBCD) Study. The HBCD Study is a multi-site prospective longitudinal cohort study, that will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Influenced by the success of the ongoing Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) and in partnership with the NIH Helping to End Addiction Long-term® Initiative, or NIH HEAL Initiative®, the HBCD Study aims to establish a diverse cohort of over 7000 pregnant participants to understand how early life experiences, including prenatal exposure to addictive substances and adverse social environments as well as their interactions with an individual's genes, can affect neurodevelopmental trajectories and outcomes. Knowledge gained from the HBCD Study will help identify targets for early interventions and inform policies that promote resilience and mitigate the neurodevelopmental effects of adverse childhood experiences and environments.

Investigating grey matter volumetric trajectories through the lifespan at the individual level

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.

Coupled changes between ruminating thoughts and resting-state brain networks during the transition into adulthood

Perseverative negative thoughts, known as rumination, might arise from emotional challenges and preclude mental health when transitioning into adulthood. Due to its multifaceted nature, rumination can take several ruminative response styles, that diverge in manifestations, severity, and mental health outcomes. Still, prospective ruminative phenotypes remain elusive insofar. Longitudinal study designs are ideal for stratifying ruminative response styles, especially with resting-state functional MRI whose setup naturally elicits people's ruminative traits. Here, we considered self-rated questionnaires on rumination and psychopathology, along with resting-state functional MRI data in 595 individuals assessed at age 18 and 22 from the IMAGEN cohort. We conducted independent component analysis to characterize eight single static resting-state functional networks in each subject and session and furthermore conducted a dynamic analysis, tackling the time variations of functional networks during the entire scanning time. We then investigated their longitudinal mediation role between changes in three ruminative response styles (reflective pondering, brooding, and depressive rumination) and changes in internalizing and co-morbid externalizing symptoms. Four static and two dynamic networks longitudinally differentiated these ruminative styles and showed complemental sensitivity to internalizing and co-morbid externalizing symptoms. Among these networks, the right frontoparietal network covaried with all ruminative styles but did not play any mediation role towards psychopathology. The default mode, the salience, and the limbic networks prospectively stratified these ruminative styles, suggesting that maladaptive ruminative styles are associated with altered corticolimbic function. For static measures, only the salience network played a longitudinal causal role between brooding rumination and internalizing symptoms. Dynamic measures highlighted the default-mode mediation role between the other ruminative styles and co-morbid externalizing symptoms. In conclusion, we identified the ruminative styles' psychometric and neural outcome specificities, supporting their translation into applied research on young adult mental healthcare.

Exploring the research needs, barriers and facilitators to the collection of biological data in adolescence for mental health research: a scoping review protocol paper

INTRODUCTION

While research into adolescent mental health has developed a considerable understanding of environmental and psychosocial risk factors, equivalent biological evidence is lacking and is not representative of economic, social and ethnic diversity in the adolescent population. It is important to understand the possible barriers and facilitators to conduct this research. This will then allow us to improve our understanding of how biology interacts with environmental and psychosocial risk factors during adolescence. The objective of this scoping review is to identify and understand the needs, barriers and facilitators related to the collection of biological data in adolescent mental health research.

METHODS AND ANALYSIS

Reviewers will conduct a systematic search of PubMed, Medline, Scopus, Cochrane, ERIC, EMBASE, ProQuest, EBSCO Global Health electronic databases, relevant publications and reference lists to identify studies published in the English language at any time. This scoping review will identify published studies exploring mental health/psychopathology outcomes, with biological measures, in participants between the ages of 11 and 18 and examine the reported methodology used for data collection. Data will be summarised in tabular form with narrative synthesis and will use the methodology of Levac et al, supplemented by subsequent recommendations from the Joanna Briggs Institute Scoping Review Methodology.

ETHICS AND DISSEMINATION

Ethical approval is not required for this scoping review. The scoping review will be conducted with input from patient and public involvement, specifically including young people involved in our study ('Co-producing a framework of guiding principles for Engaging representative and diverse cohorts of young peopLE in Biological ReseArch in menTal hEalth'-www.celebrateproject.co.uk) Youth Expert Working Group. Dissemination will include publication in peer-reviewed journals, academic presentations and on the project website.

Cross-continental environmental and genome-wide association study on children and adolescent anxiety and depression

Anxiety and depression in children and adolescents warrant special attention as a public health concern given their devastating and long-term effects on development and mental health. Multiple factors, ranging from genetic vulnerabilities to environmental stressors, influence the risk for the disorders. This study aimed to understand how environmental factors and genomics affect children and adolescents anxiety and depression across three cohorts: Adolescent Brain and Cognitive Development Study (US, age of 9-10; N=11,875), Consortium on Vulnerability to Externalizing Disorders and Addictions (INDIA, age of 6-17; N=4,326) and IMAGEN (EUROPE, age of 14; N=1888). We performed data harmonization and identified the environmental impact on anxiety/depression using a linear mixed-effect model, recursive feature elimination regression, and the LASSO regression model. Subsequently, genome-wide association analyses with consideration of significant environmental factors were performed for all three cohorts by mega-analysis and meta-analysis, followed by functional annotations. The results showed that multiple environmental factors contributed to the risk of anxiety and depression during development, where early life stress and school support index had the most significant and consistent impact across all three cohorts. In both meta, and mega-analysis, SNP rs79878474 in chr11p15 emerged as a particularly promising candidate associated with anxiety and depression, despite not reaching genomic significance. Gene set analysis on the common genes mapped from top promising SNPs of both meta and mega analyses found significant enrichment in regions of chr11p15 and chr3q26, in the function of potassium channels and insulin secretion, in particular Kv3, Kir-6.2, SUR potassium channels encoded by the KCNC1, KCNJ11, and ABCCC8 genes respectively, in chr11p15. Tissue enrichment analysis showed significant enrichment in the small intestine, and a trend of enrichment in the cerebellum. Our findings provide evidences of consistent environmental impact from early life stress and school support index on anxiety and depression during development and also highlight the genetic association between mutations in potassium channels, which support the stress-depression connection via hypothalamic-pituitary-adrenal axis, along with the potential modulating role of potassium channels.

[The German Center for Mental Health : Innovative translational research to promote prevention, targeted intervention and resilience]

BACKGROUND

Due to the high disease burden, the early onset and often long-term trajectories mental disorders are among the most widespread diseases with growing significance. The German Center for Mental Health (DZPG) was established to enhance research conditions and expedite the translation of clinically relevant findings into practice.

OBJECTIVE

The aim of the DZPG is to optimize mental healthcare in Germany, influence modifiable social causes and to develop best practice models of care for vulnerable groups. It seeks to promote mental health and resilience, combat the stigmatization associated with mental disorders, and contribute to the enhancement of treatment across all age groups.

MATERIAL AND METHODS

The DZPG employs a translational research program that accelerates the translation of basic research findings into clinical studies and general practice. University hospitals and outpatient departments, other university disciplines, and extramural research institutions are working together to establish a collaboratively coordinated infrastructure for accelerated translation and innovation.

RESEARCH PRIORITIES

The research areas encompass 1) the interaction of somatic and mental risk and resilience factors and disorders across the lifespan, 2) influencing relevant modifiable environmental factors and 3) based on this personalized prevention and intervention.

CONCLUSION

The DZPG aims to develop innovative preventive and therapeutic tools that enable an improvement in care for individuals with mental disorders. It involves a comprehensive integration of experts with experience at all levels of decision-making and employs trilogue and participatory approaches in all research projects.

The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders

Despite its crucial role in the regulation of vital metabolic and neurological functions, the genetic architecture of the hypothalamus remains unknown. Here we conducted multivariate genome-wide association studies (GWAS) using hypothalamic imaging data from 32,956 individuals to uncover the genetic underpinnings of the hypothalamus and its involvement in neuropsychiatric traits. There were 23 significant loci associated with the whole hypothalamus and its subunits, with functional enrichment for genes involved in intracellular trafficking systems and metabolic processes of steroid-related compounds. The hypothalamus exhibited substantial genetic associations with limbic system structures and neuropsychiatric traits including chronotype, risky behaviour, cognition, satiety and sympathetic-parasympathetic activity. The strongest signal in the primary GWAS, the ADAMTS8 locus, was replicated in three independent datasets (N = 1,685-4,321) and was strengthened after meta-analysis. Exome-wide association analyses added evidence to the association for ADAMTS8, and Mendelian randomization showed lower ADAMTS8 expression with larger hypothalamic volumes. The current study advances our understanding of complex structure-function relationships of the hypothalamus and provides insights into the molecular mechanisms that underlie hypothalamic formation.

Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource

We mapped functional and structural brain networks for more than 40,000 UK Biobank participants. Structural connectivity was estimated with tractography and diffusion MRI. Resting-state functional MRI was used to infer regional functional connectivity. We provide high-quality structural and functional connectomes for multiple parcellation granularities, several alternative measures of interregional connectivity, and a variety of common data pre-processing techniques, yielding more than one million connectomes in total and requiring more than 200,000 h of compute time. For a single subject, we provide 28 out-of-the-box versions of structural and functional brain networks, allowing users to select, e.g., the parcellation and connectivity measure that best suit their research goals. Furthermore, we provide code and intermediate data for the time-efficient reconstruction of more than 1000 different versions of a subject's connectome based on an array of methodological choices. All connectomes are available via the UK Biobank data-sharing platform and our connectome mapping pipelines are openly available. In this report, we describe our connectome resource in detail for users, outline key considerations in developing an efficient pipeline to map an unprecedented number of connectomes, and report on the quality control procedures that were completed to ensure connectome reliability and accuracy. We demonstrate that our structural and functional connectivity matrices meet a number of quality control checks and replicate previously established findings in network neuroscience. We envisage that our resource will enable new studies of the human connectome in health, disease, and aging at an unprecedented scale.

Uncontrolled eating and sensation-seeking partially explain the prediction of future binge drinking from adolescent brain structure

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.

Transmit Radiant Individuality to Offspring (TRIO) study: investigating intergenerational transmission effects on brain development

Intergenerational transmission is a crucial aspect of human development. Although prior studies have demonstrated the continuity of psychopathology and maladaptive upbringing environments between parents and offspring, the underlying neurobiological mechanisms remain unclear. We have begun a novel neuroimaging research project, the Transmit Radiant Individuality to Offspring (TRIO) study, which focuses on biological parent-offspring trios. The participants of the TRIO study were Japanese parent-offspring trios consisting of offspring aged 10-40 and their biological mother and father. Structural and functional brain images of all participants were acquired using magnetic resonance imaging (MRI). Saliva samples were collected for DNA analysis. We obtained psychosocial information, such as intelligence, mental health problems, personality traits, and experiences during the developmental period from each parent and offspring in the same manner as much as possible. By April 2023, we completed data acquisition from 174 trios consisting of fathers, mothers, and offspring. The target sample size was 310 trios. However, we plan to conduct genetic and epigenetic analyses, and the sample size is expected to be expanded further while developing this project into a multi-site collaborative study in the future. The TRIO study can challenge the elucidation of the mechanism of intergenerational transmission effects on human development by collecting diverse information from parents and offspring at the molecular, neural, and behavioral levels. Our study provides interdisciplinary insights into how individuals' lives are involved in the construction of the lives of their descendants in the subsequent generation.

Structural neurodevelopment at the individual level - a life-course investigation using ABCD, IMAGEN and UK Biobank data

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.

Whole Person Modeling: a transdisciplinary approach to mental health research

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

Association between vmPFC gray matter volume and smoking initiation in adolescents

Smoking of cigarettes among young adolescents is a pressing public health issue. However, the neural mechanisms underlying smoking initiation and sustenance during adolescence, especially the potential causal interactions between altered brain development and smoking behaviour, remain elusive. Here, using large longitudinal adolescence imaging genetic cohorts, we identify associations between left ventromedial prefrontal cortex (vmPFC) gray matter volume (GMV) and subsequent self-reported smoking initiation, and between right vmPFC GMV and the maintenance of smoking behaviour. Rule-breaking behaviour mediates the association between smaller left vmPFC GMV and smoking behaviour based on longitudinal cross-lagged analysis and Mendelian randomisation. In contrast, smoking behaviour associated longitudinal covariation of right vmPFC GMV and sensation seeking (especially hedonic experience) highlights a potential reward-based mechanism for sustaining addictive behaviour. Taken together, our findings reveal vmPFC GMV as a possible biomarker for the early stages of nicotine addiction, with implications for its prevention and treatment.

Statistical Learning Methods for Neuroimaging Data Analysis with Applications

The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.

A Rare but Aggressive Malignancy: A Case Report of a Gastrointestinal Neuroectodermal Tumor (GNET)

Gastrointestinal neuroectodermal tumors (GNETs) are extremely rare and intriguing malignancies originating from neural crest cells in the digestive tract. The digestive tract's neural crest cells can give rise to incredibly unusual and interesting gastrointestinal neuroectodermal tumors (GNETs). GNETs present considerable hurdles in diagnosis and management because of their rarity and varied expression. In this case report, a 45-year-old male patient is described who had signs of GNET, such as exhaustion, weight loss, and abdominal pain. A 7-cm jejunum tumor and related thickening of the gut wall were discovered using imaging investigations. The diagnosis of malignant GNET was confirmed by surgical resection, and adjuvant treatment was given. A recurring tumor required a second surgical procedure despite an initial disease-free period. The report emphasizes the difficulties involved in the diagnosis, treatment, and long-term effects of GNETs. The rarity of GNETs necessitates the development of standardized treatment protocols as well as additional research to enhance diagnostic precision and explore novel therapeutic approaches for this aggressive malignancy.

Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images

Cardiovascular health interacts with cognitive and mental health in complex ways, yet little is known about the phenotypic and genetic links of heart-brain systems. We quantified heart-brain connections using multiorgan magnetic resonance imaging (MRI) data from more than 40,000 subjects. Heart MRI traits displayed numerous association patterns with brain gray matter morphometry, white matter microstructure, and functional networks. We identified 80 associated genomic loci (P < 6.09 × 10-10) for heart MRI traits, which shared genetic influences with cardiovascular and brain diseases. Genetic correlations were observed between heart MRI traits and brain-related traits and disorders. Mendelian randomization suggests that heart conditions may causally contribute to brain disorders. Our results advance a multiorgan perspective on human health by revealing heart-brain connections and shared genetic influences.

Brief mock-scan training reduces head motion during real scanning for children: A growth curve study

Pediatric neuroimaging datasets are rapidly increasing in scales. Despite strict protocols in data collection and preprocessing focused on improving data quality, the presence of head motion still impedes our understanding of neurodevelopmental mechanisms. Large head motion can lead to severe noise and artifacts in magnetic resonance imaging (MRI) studies, inflating correlations between adjacent brain areas and decreasing correlations between spatial distant territories, especially in children and adolescents. Here, by leveraging mock-scans of 123 Chinese children and adolescents, we demonstrated the presence of increased head motion in younger participants. Critically, a 5.5-minute training session in an MRI mock scanner was found to effectively suppress the head motion in the children and adolescents. Therefore, we suggest that mock scanner training should be part of the quality assurance routine prior to formal MRI data collection, particularly in large-scale population-level neuroimaging initiatives for pediatrics.

Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population

In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with subject compliance, equipment failure or signal error. In large databases, such as the Human Connectome Projects, over half of the respiratory recordings may be unusable. As a result, the direct removal of low frequency respiratory variations from the blood oxygen level-dependent (BOLD) signal time series is not possible. This study proposes a deep learning-based method for reconstruction of respiratory variation (RV) waveforms directly from BOLD fMRI data in pediatric participants (aged 5 to 21 years old), and does not require any respiratory measurement device. To do this, the Lifespan Human Connectome Project in Development (HCP-D) dataset, which includes respiratory measurements, was used to both train a convolutional neural network (CNN) and evaluate its performance. Results show that a CNN can capture informative features from the BOLD signal time course and reconstruct accurate RV timeseries, especially when the subject has a prominent respiratory event. This work advances the use of direct estimation of physiological parameters from fMRI, which will eventually lead to reduced complexity and decrease the burden on participants because they may not be required to wear a respiratory bellows.

Associations of DNA Methylation With Behavioral Problems, Gray Matter Volumes, and Negative Life Events Across Adolescence: Evidence From the Longitudinal IMAGEN Study

BACKGROUND

Negative life events (NLEs) increase the risk for externalizing behaviors (EBs) and internalizing behaviors (IBs) in adolescence and adult psychopathology. DNA methylation associated with behavioral problems may reflect this risk and long-lasting effects of NLEs.

METHODS

To identify consistent associations between blood DNA methylation and EBs or IBs across adolescence, we conducted longitudinal epigenome-wide association studies (EWASs) using data from the IMAGEN cohort, collected at ages 14 and 19 years (n = 506). Significant findings were validated in a separate subsample (n = 823). Methylation risk scores were generated by 10-fold cross-validation and further tested for their associations with gray matter volumes and NLEs.

RESULTS

No significant findings were obtained for the IB-EWAS. The EB-EWAS identified a genome-wide significant locus in a gene linked to attention-deficit/hyperactivity disorder (ADHD) (IQSEC1, cg01460382; p = 1.26 × 10-8). Other most significant CpG sites were near ADHD-related genes and enriched for genes regulating tumor necrosis factor and interferon-γ signaling, highlighting the relevance of EB-EWAS findings for ADHD. Analyses with the EB methylation risk scores suggested that it partly reflected comorbidity with IBs in late adolescence. Specific to EBs, EB methylation risk scores correlated with smaller gray matter volumes in medial orbitofrontal and anterior/middle cingulate cortices, brain regions known to associate with ADHD and conduct problems. Longitudinal mediation analyses indicated that EB-related DNA methylation were more likely the outcomes of problematic behaviors accentuated by NLEs, and less likely the epigenetic bases of such behaviors.

CONCLUSIONS

Our findings suggest that novel epigenetic mechanisms through which NLEs exert short and longer-term effects on behavior may contribute to ADHD.

Cross-continental environmental and genome-wide association study on children and adolescent anxiety and depression

Anxiety and depression in children and adolescents warrant special attention as a public health issue given their devastating and long-term effects on development and mental health. Multiple factors, ranging from genetic vulnerabilities to environmental stressors, influence the risk for the disorders. This study aimed to understand how environmental factors and genomics affect children and adolescents anxiety and depression across three cohorts: Adolescent Brain and Cognitive Development Study (US, age of 9-10), Consortium on Vulnerability to Externalizing Disorders and Addictions (INDIA, age of 6-17) and IMAGEN (EUROPE, age of 14). We performed data harmonization and identified the environmental impact on anxiety/depression using a linear mixed-effect model, recursive feature elimination regression, and the LASSO regression model. Subsequently, genome-wide association analyses with consideration of significant environmental factors were performed for all three cohorts by mega-analysis and meta-analysis, followed by functional annotations. The results showed that multiple environmental factors contributed to the risk of anxiety and depression during development, where early life stress and school risk had the most significant and consistent impact across all three cohorts. Both meta and mega-analysis identified a novel SNP rs79878474 in chr11p15 to be the most promising SNP associated with anxiety and depression. Gene set analysis on the common genes mapped from top promising SNPs of both meta and mega analyses found significant enrichment in regions of chr11p15 and chr3q26, in the function of potassium channels and insulin secretion, in particular Kv3, Kir-6.2, SUR potassium channels encoded by the KCNC1, KCNJ11, and ABCCC8 genes respectively, in chr11p15. Tissue enrichment analysis showed significant enrichment in the small intestine and a trend of enrichment in the cerebellum. Our findings provide evidence of consistent environmental impact from early life stress and school risks on anxiety and depression during development and also highlight the genetic association between mutations in potassium channels along with the potential role of the cerebellum region, which are worthy of further investigation.

The Genetically Informed Neurobiology of Addiction (GINA) model

Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.

Regional gene expression patterns are associated with task-specific brain activation during reward and emotion processing measured with functional MRI

The exploration of the spatial relationship between gene expression profiles and task-evoked response patterns known to be altered in neuropsychiatric disorders, for example depression, can guide the development of more targeted therapies. Here, we estimated the correlation between human transcriptome data and two different brain activation maps measured with functional magnetic resonance imaging (fMRI) in healthy subjects. Whole-brain activation patterns evoked during an emotional face recognition task were associated with topological mRNA expression of genes involved in cellular transport. In contrast, fMRI activation patterns related to the acceptance of monetary rewards were associated with genes implicated in cellular localization processes, metabolism, translation, and synapse regulation. An overlap of these genes with risk genes from major depressive disorder genome-wide association studies revealed the involvement of the master regulators TCF4 and PAX6 in emotion and reward processing. Overall, the identification of stable relationships between spatial gene expression profiles and fMRI data may reshape the prospects for imaging transcriptomics studies.

A Developmental Perspective on Facets of Impulsivity and Brain Activity Correlates From Adolescence to Adulthood

BACKGROUND

On a theoretical level, impulsivity represents a multidimensional construct associated with acting without foresight, inefficient inhibitory response control, and alterations in reward processing. On an empirical level, relationships and changes in associations between different measures of impulsivity from adolescence into young adulthood and their relation to neural activity during inhibitory control and reward anticipation have not been fully understood.

METHODS

We used data from IMAGEN, a longitudinal multicenter, population-based cohort study in which 2034 healthy adolescents were investigated at age 14, and 1383 were reassessed as young adults at age 19. We measured the construct of trait impulsivity using self-report questionnaires and neurocognitive indices of decisional impulsivity. With functional magnetic resonance imaging, we assessed brain activity during inhibition error processing using the stop signal task and during reward anticipation in the monetary incentive delay task. Correlations were analyzed, and mixed-effect models were fitted to explore developmental and predictive effects.

RESULTS

All self-report and neurocognitive measures of impulsivity proved to be correlated during adolescence and young adulthood. Further, pre-supplementary motor area and inferior frontal gyrus activity during inhibition error processing was associated with trait impulsivity in adolescence, whereas in young adulthood, a trend-level association with reward anticipation activity in the ventral striatum was found. For adult delay discounting, a trend-level predictive effect of adolescent neural activity during inhibition error processing emerged.

CONCLUSIONS

Our findings help to inform theories of impulsivity about the development of its multidimensional nature and associated brain activity patterns and highlight the need for taking functional brain development into account when evaluating neuromarker candidates.

Key considerations for child and adolescent MRI data collection

Cognitive neuroimaging researchers' ability to infer accurate statistical conclusions from neuroimaging depends greatly on the quality of the data analyzed. This need for quality control is never more evident than when conducting neuroimaging studies with children and adolescents. Developmental neuroimaging requires patience, flexibility, adaptability, extra time, and effort. It also provides us a unique, non-invasive way to understand the development of cognitive processes, individual differences, and the changing relations between brain and behavior over the lifespan. In this discussion, we focus on collecting magnetic resonance imaging (MRI) data, as it is one of the more complex protocols used with children and youth. Through our extensive experience collecting MRI datasets with children and families, as well as a review of current best practices, we will cover three main topics to help neuroimaging researchers collect high-quality datasets. First, we review key recruitment and retention techniques, and note the importance for consistency and inclusion across groups. Second, we discuss ways to reduce scan anxiety for families and ways to increase scan success by describing the pre-screening process, use of a scanner simulator, and the need to focus on participant and family comfort. Finally, we outline several important design considerations in developmental neuroimaging such as asking a developmentally appropriate question, minimizing data loss, and the applicability of public datasets. Altogether, we hope this article serves as a useful tool for those wishing to enter or learn more about developmental cognitive neuroscience.

Differential association between the GLP1R gene variants and brain functional connectivity according to the severity of alcohol use

Growing evidence suggests that the glucagon-like peptide-1 (GLP-1) system is involved in mechanisms underlying alcohol seeking and consumption. Accordingly, the GLP-1 receptor (GLP-1R) has begun to be studied as a potential pharmacotherapeutic target for alcohol use disorder (AUD). The aim of this study was to investigate the association between genetic variation at the GLP-1R and brain functional connectivity, according to the severity of alcohol use. Participants were 181 individuals categorized as high-risk (n = 96) and low-risk (n = 85) alcohol use, according to their AUD identification test (AUDIT) score. Two uncommon single nucleotide polymorphisms (SNPs), rs6923761 and rs1042044, were selected a priori for this study because they encode amino-acid substitutions with putative functional consequences on GLP-1R activity. Genotype groups were based on the presence of the variant allele for each of the two GLP-1R SNPs of interest [rs6923761: AA + AG (n = 65), GG (n = 116); rs1042044: AA + AC (n = 114), CC (n = 67)]. Resting-state functional MRI data were acquired for 10 min and independent component (IC) analysis was conducted. Multivariate analyses of covariance (MANCOVA) examined the interaction between GLP-1R genotype group and AUDIT group on within- and between-network connectivity. For rs6923761, three ICs showed significant genotype × AUDIT interaction effects on within-network connectivity: two were mapped onto the anterior salience network and one was mapped onto the visuospatial network. For rs1042044, four ICs showed significant interaction effects on within-network connectivity: three were mapped onto the dorsal default mode network and one was mapped onto the basal ganglia network. For both SNPs, post-hoc analyses showed that in the group carrying the variant allele, high versus low AUDIT was associated with stronger within-network connectivity. No significant effects on between-network connectivity were found. In conclusion, genetic variation at the GLP-1R was differentially associated with brain functional connectivity in individuals with low versus high severity of alcohol use. Significant findings in the salience and default mode networks are particularly relevant, given their role in the neurobiology of AUD and addictive behaviors.

Intersection between social inequality and emotion regulation on emerging adult cannabis use

Emerging adulthood (EA; ages 18-25) is characterized by socioemotional and neurodevelopmental challenges. Cannabis is a widely used substance among EAs, and hazardous use may increase risk for sustained use patterns and related health consequences. Research shows differential increases in hazardous use by objective as well as subjective measures of social inequality, with more concerning trajectories for youth with greater experiences of social inequality. Learning how to flexibly monitor and modify emotions in proactive ways (i.e., emotion regulation) is a central developmental task navigated during the EA window. Challenges to and with emotion regulation processes can contribute to the emergence of mental health symptoms during EA, including hazardous cannabis use. In this perspective, we highlight emotion dysregulation and social inequality as two critical factors that interact to either buffer against or exacerbate cannabis use during the EA period, noting critical gaps in the literature that merit additional research. We recommend novel methods and longitudinal designs to help clarify how dynamic cognition-emotion interplay predicts trajectories of negative emotional experiences and cannabis use in EA.

Structural differences in adolescent brains can predict alcohol misuse

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.

Characterizing reward system neural trajectories from adolescence to young adulthood

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.

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Imaging data during reward anticipation at T1 (age 14) and T2 (age 19) was compared.

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Brain response decreased in subcortical areas and increased in cortical areas at T2.

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Functional connectivity (FC) with the ventral striatum increased at T2.

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Topological efficiency of task-modulated network increased at T2.

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The developmental pattern was altered in those who increased drinking most at T2.

Neuroimaging brain growth charts: A road to mental health

Mental disorders are common health concerns and contribute to a heavy global burden on our modern society. It is challenging to identify and treat them timely. Neuroimaging evidence suggests the incidence of various psychiatric and behavioral disorders is closely related to the atypical development of brain structure and function. The identification and understanding of atypical brain development provide chances for clinicians to detect mental disorders earlier, perhaps even prior to onset, and treat them more precisely. An invaluable and necessary method in identifying and monitoring atypical brain development are growth charts of typically developing individuals in the population. The brain growth charts can offer a series of standard references on typical neurodevelopment, representing an important resource for the scientific and medical communities. In the present paper, we review the relationship between mental disorders and atypical brain development from a perspective of normative brain development by surveying the recent progress in the development of brain growth charts, including four aspects on growth chart utility: 1) cohorts, 2) measures, 3) mechanisms, and 4) clinical translations. In doing so, we seek to clarify the challenges and opportunities in charting brain growth, and to promote the application of brain growth charts in clinical practice.

Emerging ethical issues raised by highly portable MRI research in remote and resource-limited international settings

Smaller, more affordable, and more portable MRI brain scanners offer exciting opportunities to address unmet research needs and long-standing health inequities in remote and resource-limited international settings. Field-based neuroimaging research in low- and middle-income countries (LMICs) can improve local capacity to conduct both structural and functional neuroscience studies, expand knowledge of brain injury and neuropsychiatric and neurodevelopmental disorders, and ultimately improve the timeliness and quality of clinical diagnosis and treatment around the globe. Facilitating MRI research in remote settings can also diversify reference databases in neuroscience, improve understanding of brain development and degeneration across the lifespan in diverse populations, and help to create reliable measurements of infant and child development. These deeper understandings can lead to new strategies for collaborating with communities to mitigate and hopefully overcome challenges that negatively impact brain development and quality of life. Despite the potential importance of research using highly portable MRI in remote and resource-limited settings, there is little analysis of the attendant ethical, legal, and social issues (ELSI). To begin addressing this gap, this paper presents findings from the first phase of an envisioned multi-staged and iterative approach for creating ethical and legal guidance in a complex global landscape. Section 1 provides a brief introduction to the emerging technology for field-based MRI research. Section 2 presents our methodology for generating plausible use cases for MRI research in remote and resource-limited settings and identifying associated ELSI issues. Section 3 analyzes core ELSI issues in designing and conducting field-based MRI research in remote, resource-limited settings and offers recommendations. We argue that a guiding principle for field-based MRI research in these contexts should be including local communities and research participants throughout the research process in order to create sustained local value. Section 4 presents a recommended path for the next phase of work that could further adapt these use cases, address ethical and legal issues, and co-develop guidance in partnership with local communities.

Strengths and challenges of longitudinal non-human primate neuroimaging

Longitudinal non-human primate neuroimaging has the potential to greatly enhance our understanding of primate brain structure and function. Here we describe its specific strengths, compared to both cross-sectional non-human primate neuroimaging and longitudinal human neuroimaging, but also its associated challenges. We elaborate on factors guiding the use of different analytical tools, subject-specific versus age-specific templates for analyses, and issues related to statistical power.

Rich Club Characteristics of Alcohol-Naïve Functional Brain Networks Predict Future Drinking Phenotypes in Rhesus Macaques

Purpose: A fundamental question for Alcohol use disorder (AUD) is how and when naïve brain networks are reorganized in response to alcohol consumption. The current study aimed to determine the progression of alcohol’s effect on functional brain networks during transition from the naïve state to chronic consumption.

Procedures: Resting-state brain networks of six female rhesus macaque (Macaca mulatta) monkeys were acquired using magnetoencephalography (MEG) prior to alcohol exposure and after free-access to alcohol using a well-established model of chronic heavy alcohol consumption. Functional brain network metrics were derived at each time point.

Results: The average connection frequency (p < 0.024) and membership of the Rich Club (p < 0.022) changed significantly over time. Metrics describing network topology remained relatively stable from baseline to free-access drinking. The minimum degree of the Rich Club prior to alcohol exposure was significantly predictive of future free-access drinking (r = −0.88, p < 0.001).

Conclusions: Results suggest naïve brain network characteristics may be used to predict future alcohol consumption, and that alcohol consumption alters functional brain networks, shifting hubs and Rich Club membership away from previous regions in a non-systematic manner. Further work to refine these relationships may lead to the identification of a high-risk drinking phenotype.

An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization

Adolescence is a critical time for the continued maturation of brain networks. Here, we assessed structural connectome development in a large longitudinal sample ranging from childhood to young adulthood. By projecting high-dimensional connectomes into compact manifold spaces, we identified a marked expansion of structural connectomes, with strongest effects in transmodal regions during adolescence. Findings reflected increased within-module connectivity together with increased segregation, indicating increasing differentiation of higher-order association networks from the rest of the brain. Projection of subcortico-cortical connectivity patterns into these manifolds showed parallel alterations in pathways centered on the caudate and thalamus. Connectome findings were contextualized via spatial transcriptome association analysis, highlighting genes enriched in cortex, thalamus, and striatum. Statistical learning of cortical and subcortical manifold features at baseline and their maturational change predicted measures of intelligence at follow-up. Our findings demonstrate that connectome manifold learning can bridge the conceptual and empirical gaps between macroscale network reconfigurations, microscale processes, and cognitive outcomes in adolescent development.

Neuroimaging in attention-deficit/hyperactivity disorder

PURPOSE OF REVIEW

Neuroimaging research on attention-deficit/hyperactivity disorder (ADHD) continues growing in extent and complexity, although it has yet to become clinically meaningful. We review recent MRI research on ADHD, to identify robust findings, current trends and challenges.

RECENT FINDINGS

We identified 40 publications between January 2019 and September 2020 reporting or reviewing MRI research on ADHD. Four meta-analyses have presented conflicting results regarding across-study convergence of functional and resting-state functional (fMRI and R-fMRI) studies on ADHD. On the other hand, the Enhancing NeuroImaging Genetics Through Meta-Analysis international consortium has identified statistically robust albeit small differences in structural brain cortical and subcortical indices in children with ADHD versus typically developing controls. Other international consortia are harnessing open-science efforts and multimodal data (imaging, genetics, phenotypic) to shed light on the complex interplay of genetics, environment, and development in the pathophysiology of ADHD. We note growing research in 'prediction' science, which applies machine-learning analysis to identify biomarkers of disease based on big data.

SUMMARY

Neuroimaging in ADHD is still far from informing clinical practice. Current large-scale, multimodal, and open-science initiatives represent promising paths toward untangling the neurobiology of ADHD.

[The IMAGEN cohort: perspectives and problems of longitudinal research]

BACKGROUND

Genetic risk factors for major mental disorders identified in psychiatric research show a substantial overlap. Therefore, it has been suggested that neurobiological research should focus on intermediate phenotypes that reflect shared aspects of different mental disorders due to overlapping genetic effects and environmental factors. Longitudinal studies are required to assess the interaction between genetic variability and modifying environmental factors and to investigate the effects on intermediate phenotypes and (mediated by them) on the expression of individual mental disorders.

OBJECTIVE

Discussion of the possibilities and limitations of longitudinal cohort studies using the IMAGEN study as an example.

MATERIAL AND METHODS

The results of the European IMAGEN study are presented with a focus on addiction.

RESULTS

The longitudinal assessments of the IMAGEN cohort revealed that neuroimaging data indicating a low activation of the dopaminergic reinforcement system detected at the age of 14 years are predictive for increased drug use. In addition to genetic factors, environmental influences such as maternal smoking during pregnancy were correlated with this low activation.

CONCLUSION

Longitudinal neurobiological basic research can validate the effects of candidate genes and reveal relevant environmental factors. Relevant modifiable factors indicated by the IMAGEN study and related datasets include drug use during pregnancy, trauma and other experiences of violence, social disadvantage and exclusion.

Machine learning for precision medicine

Precision medicine is an emerging approach to clinical research and patient care that focuses on understanding and treating disease by integrating multi-modal or multi-omics data from an individual to make patient-tailored decisions. With the large and complex datasets generated using precision medicine diagnostic approaches, novel techniques to process and understand these complex data were needed. At the same time, computer science has progressed rapidly to develop techniques that enable the storage, processing, and analysis of these complex datasets, a feat that traditional statistics and early computing technologies could not accomplish. Machine learning, a branch of artificial intelligence, is a computer science methodology that aims to identify complex patterns in data that can be used to make predictions or classifications on new unseen data or for advanced exploratory data analysis. Machine learning analysis of precision medicine's multi-modal data allows for broad analysis of large datasets and ultimately a greater understanding of human health and disease. This review focuses on machine learning utilization for precision medicine's "big data", in the context of genetics, genomics, and beyond.

Study protocol of comprehensive risk evaluation for anorexia nervosa in twins (CREAT): a study of discordant monozygotic twins with anorexia nervosa

BACKGROUND

Anorexia nervosa (AN) is a severe disorder, for which genetic evidence suggests psychiatric as well as metabolic origins. AN has high somatic and psychiatric comorbidities, broad impact on quality of life, and elevated mortality. Risk factor studies of AN have focused on differences between acutely ill and recovered individuals. Such comparisons often yield ambiguous conclusions, as alterations could reflect different effects depending on the comparison. Whereas differences found in acutely ill patients could reflect state effects that are due to acute starvation or acute disease-specific factors, they could also reflect underlying traits. Observations in recovered individuals could reflect either an underlying trait or a "scar" due to lasting effects of sustained undernutrition and illness. The co-twin control design (i.e., monozygotic [MZ] twins who are discordant for AN and MZ concordant control twin pairs) affords at least partial disambiguation of these effects.

METHODS

Comprehensive Risk Evaluation for Anorexia nervosa in Twins (CREAT) will be the largest and most comprehensive investigation of twins who are discordant for AN to date. CREAT utilizes a co-twin control design that includes endocrinological, neurocognitive, neuroimaging, genomic, and multi-omic approaches coupled with an experimental component that explores the impact of an overnight fast on most measured parameters.

DISCUSSION

The multimodal longitudinal twin assessment of the CREAT study will help to disambiguate state, trait, and "scar" effects, and thereby enable a deeper understanding of the contribution of genetics, epigenetics, cognitive functions, brain structure and function, metabolism, endocrinology, microbiology, and immunology to the etiology and maintenance of AN.

The Developmental Chronnecto-Genomics (Dev-CoG) study: A multimodal study on the developing brain

Brain development has largely been studied through unimodal analysis of neuroimaging data, providing independent results for structural and functional data. However, structure clearly impacts function and vice versa, pointing to the need for performing multimodal data collection and analysis to improve our understanding of brain development, and to further inform models of typical and atypical brain development across the lifespan. Ultimately, such models should also incorporate genetic and epigenetic mechanisms underlying brain structure and function, although currently this area is poorly specified. To this end, we are reporting here a multi-site, multi-modal dataset that captures cognitive function, brain structure and function, and genetic and epigenetic measures to better quantify the factors that influence brain development in children originally aged 9-14 years. Data collection for the Developmental Chronnecto-Genomics (Dev-CoG) study (http://devcog.mrn.org/) includes cognitive, emotional, and social performance scales, structural and functional MRI, diffusion MRI, magnetoencephalography (MEG), and saliva collection for DNA analysis of single nucleotide polymorphisms (SNPs) and DNA methylation patterns. Across two sites (The Mind Research Network and the University of Nebraska Medical Center), data from over 200 participants were collected and these children were re-tested annually for at least 3 years. The data collection protocol, sample demographics, and data quality measures for the dataset are presented here. The sample will be made freely available through the collaborative informatics and neuroimaging suite (COINS) database at the conclusion of the study.