OBJECTIVE: Previous work suggests that cognitive and environmental risk factors may predict conversion to psychosis in individuals at clinical high risk (CHRs) for the disorder. Less clear, however, is whether these same factors are also associated with the initial emergence of the high risk state in individuals who do not meet current threshold criteria for being considered high risk.
METHODS: Here, using data from the Adolescent Brain Cognitive Development (ABCD) study, we examined associations between factors previously demonstrated to predict conversion to psychosis in CHRs with transition to a \"high risk\" state, here defined as having a distress score between 2 and 5 on any unusual thought content question in the Prodromal Questionnaire-Brief Child version. Of a sample of 5237 children (ages 11-12) studied at baseline, 470 transitioned to the high-risk state the following year. A logistic regression model was evaluated using age, cognition, negative and traumatic experiences, decline in school performance, and family history of psychosis as predictors.
RESULTS: The overall model was significant (χ2 = 100.89, R2 = 0.042, p < .001). Significant predictors included number of negative life events, decline in school performance, number of trauma types, and verbal learning task performance.
CONCLUSIONS: These results suggest that factors that predict conversion in CHR teenagers are also associated with initial emergence of a \"high-risk\" state in preadolescents. Limitations regarding the degree to which model factors and outcome in this study parallel those used in previous work involving psychosis risk in older teenagers are discussed.

OBJECTIVE: Cross-sectional studies in adults have demonstrated associations between early life adversity (ELA) and reduced hippocampal volume, but the timing of these effects is not clear. The present study sought to examine whether ELA predicts changes in hippocampal volume over time in a large sample of early adolescents.
METHODS: The Adolescent Brain Cognitive Development Study provides a large dataset of tabulated neuroimaging, youth-reported adverse experiences, and parent-reported financial adversity from a sample of children around the United States. Linear mixed effects modeling was used to determine the relationship between ELA and hippocampal volume change within youth (n = 7036) from ages 9-10 to 11-12 years.
RESULTS: Results of the models indicated that the number of early adverse events predicted bilateral hippocampal volume change (β = -0.02, t = -2.02, p < .05). Higher adversity was associated with lower hippocampal volume at Baseline (t = 5.55, p < .01) and at Year 2 (t = 6.14, p < .001).
CONCLUSIONS: These findings suggest that ELA may affect hippocampal development during early adolescence. Prevention and early intervention are needed to alter the course of this trajectory. Future work should examine associations between ELA, hippocampal development, and educational and socioemotional outcomes.

OBJECTIVE: A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growths can vary in appearance. On the other hand, malignant skin lesions correspond to skin cancer, which happens to be the most prevalent form of cancer in the United States. Skin cancer involves the unusual proliferation of skin cells anywhere on the body. The conventional method for detecting skin cancer is relatively more painful.
METHODS: This work involves the automated prediction of skin cancer and its types using two stage Convolutional Neural Network (CNN). The first stage of CNN extracts low level features and second stage extracts high level features. Feature selection is done using these two CNN and ABCD (Asymmetry, Border irregularity, Colour variation, and Diameter) technique. The features extracted from the two CNNs are fused with ABCD features and fed into classifiers for the final prediction. The classifiers employed in this work include ensemble learning methods such as gradient boosting and XG boost, as well as machine learning classifiers like decision trees and logistic regression. This methodology is evaluated using the International Skin Imaging Collaboration (ISIC) 2018 and 2019 dataset.
RESULTS: As a result, the first stage CNN which is used for creation of new dataset achieved an accuracy of 97.92%. Second stage CNN which is used for feature selection achieved an accuracy of 98.86%. Classification results are obtained for both with and without fusion of features.
CONCLUSIONS: Therefore, two stage prediction model achieved better results with feature fusion.

Academic performance plays a crucial role in long-term educational attainment and occupational function. Chronotype refers to an individual\'s daily tendencies for times for waking, activity, and sleep. Social jetlag reflects the mismatch between an individual\'s chronotype and their social schedule. Because school typically starts early in the morning, later chronotype is often associated with daytime sleepiness, insufficient sleep, and poor academic performance. However, the relationship between academic performance, chronotype, and social jetlag has not been extensively examined in large samples like the Adolescent Brain Cognitive Development (ABCD) study. We hypothesized that greater social jetlag would predict poorer cognitive and academic performance. Year 2 (ages 11-14) cross-sectional data from the ABCD cohort (n = 6,890 adolescents) were used to evaluate academic performance (i.e. self-reported past year grades), NIH Toolbox cognitive performance measures, chronotype, and social jetlag from the Munich Chronotype Questionnaire. We found that later chronotype and greater social jetlag predicted poorer cognitive and academic performance with small effect sizes. Our findings emphasize the importance of individual differences in chronotype and social jetlag when designing class schedules, as aligning school activities with student optimal sleep-wake times may contribute to improved academic performance.

Genetic variants linked to autism are thought to change cognition and behaviour by altering the structure and function of the brain. Although a substantial body of literature has identified structural brain differences in autism, it is unknown whether autism-associated common genetic variants are linked to changes in cortical macro- and micro-structure. We investigated this using neuroimaging and genetic data from adults (UK Biobank, N = 31,748) and children (ABCD, N = 4,928). Using polygenic scores and genetic correlations we observe a robust negative association between common variants for autism and a magnetic resonance imaging derived phenotype for neurite density (intracellular volume fraction) in the general population. This result is consistent across both children and adults, in both the cortex and in white matter tracts, and confirmed using polygenic scores and genetic correlations. There were no sex differences in this association. Mendelian randomisation analyses provide no evidence for a causal relationship between autism and intracellular volume fraction, although this should be revisited using better powered instruments. Overall, this study provides evidence for shared common variant genetics between autism and cortical neurite density.

OBJECTIVE: Parents play a notable role in the development of child psychopathology. In this study, we investigated the role of parent psychopathology and behaviors on child brain-symptom networks to understand the role of intergenerational transmission of psychopathology. Few studies have documented the interaction of child psychopathology, parent psychopathology, and child neuroimaging.
METHODS: We used the baseline cohort of the Adolescent Brain Cognitive Development Study (N = 7,151, female-at-birth = 3,619, aged 9-11 years) to derive brain-symptom networks using sparse canonical correlation analysis with the Child Behavior Checklist and resting-state functional magnetic resonance imaging. We then correlated parent psychopathology symptoms and parental behaviors with child brain-symptom networks. Finally, we used the significant correlations to understand, using the mediation R package, whether parent behaviors mediated the effect of parent psychopathology on child brain connectivity.
RESULTS: We observed 3 brain-symptom networks correlated with externalizing (r = 0.19, internalizing (r = 0.17), and neurodevelopmental symptoms (r = 0.18). These corresponded to differences in connectivity between the default mode-default mode, default mode-control, and visual-visual canonical networks. We further detected aspects of parental psychopathology, including personal strength, thought problems, and rule-breaking symptoms to be associated with child brain connectivity. Finally, we found that parental behaviors and symptoms mediate each other\'s relationship to child brain connectivity.
CONCLUSIONS: The current study suggests that positive parental behaviors can relieve potentially detrimental effects of parental psychopathology, and vice versa, on symptom-correlated child brain connectivity. Altogether, these results provide a framework for future research and potential targets for parents who experience mental health symptoms to help mitigate potential intergenerational transmission of mental illness.

There has been an increase in the number of women using marijuana whilst pregnant. Previous studies have shown that children with prenatal marijuana exposure have developmental deficits in memory and decreased attentiveness. In this study, we assess whether prenatal marijuana exposure is associated with alterations in brain regional morphometry and functional and structural connectivity in adolescents. We downloaded behavioural scores and subject image files from the Adolescent Brain Cognitive DevelopmentSM Study. A total of 178 anatomical and diffusion magnetic resonance imaging files (88 prenatal marijuana exposure and 90 age- and gender-matched controls) and 152 resting-state functional magnetic resonance imaging files (76 prenatal marijuana exposure and 76 controls) were obtained. Behavioural metrics based on the parent-reported child behavioural checklist were also obtained for each subject. The associations of prenatal marijuana exposure with 17 subscales of the child behavioural checklist were calculated. We assessed differences in brain morphometry based on voxel-based and surface-based morphometry in adolescents with prenatal marijuana exposure versus controls. We also evaluated group differences in structural and functional connectivity in adolescents for region-to-region connectivity and graph theoretical metrics. Interactions of prenatal marijuana exposure and graph networks were assessed for impact on behavioural scores. Multiple comparison correction was performed as appropriate. Adolescents with prenatal marijuana exposure had greater abnormal or borderline child behavioural checklist scores in 9 out of 17 subscales. There were no significant differences in voxel- or surface-based morphometry, structural connectivity or functional connectivity between prenatal marijuana exposure and controls. However, there were significant differences in prenatal marijuana exposure-graph network interactions with respect to behavioural scores. There were three structural prenatal marijuana exposure-graph network interactions and seven functional prenatal marijuana exposure-graph network interactions that were significantly associated with behavioural scores. Whilst this study was not able to confirm anatomical or functional differences between prenatal marijuana exposure and unexposed pre-adolescent children, there were prenatal marijuana exposure-brain structural and functional graph network interactions that were significantly associated with behavioural scores. This suggests that altered brain networks may underlie behavioural outcomes in adolescents with prenatal marijuana exposure. More work needs to be conducted to better understand the prognostic value of brain structural and functional network measures in prenatal marijuana exposure.

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

BACKGROUND: Preterm infants with low birth weight are at heightened risk of developmental sequelae, including neurological and cognitive dysfunction that can persist into adolescence or adulthood. In addition, preterm birth and low birth weight can provoke changes in endocrine and metabolic processes that likely impact brain health throughout development. However, few studies have examined associations among birth weight, pubertal endocrine processes, and long-term neurological and cognitive development.
METHODS: We investigated the associations between birth weight and brain morphometry, cognitive function, and onset of adrenarche assessed 9 to 11 years later in 3571 preterm and full-term children using the ABCD (Adolescent Brain Cognitive Development) Study dataset.
RESULTS: The preterm children showed lower birth weight and early adrenarche, as expected. Birth weight was positively associated with cognitive function (all Cohen\'s d > 0.154, p < .005), global brain volumes (all Cohen\'s d > 0.170, p < .008), and regional volumes in frontal, temporal, and parietal cortices in preterm and full-term children (all Cohen\'s d > 0.170, p < .0007); cortical volume in the lateral orbitofrontal cortex partially mediated the effect of low birth weight on cognitive function in preterm children. In addition, adrenal score and cortical volume in the lateral orbitofrontal cortex mediated the associations between birth weight and cognitive function only in preterm children.
CONCLUSIONS: These findings highlight the impact of low birth weight on long-term brain structural and cognitive function development and show important associations with early onset of adrenarche during the puberty. This understanding may help with prevention and treatment.

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.