cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15154/1528480
title: Social epidemiology of early adolescent problematic screen use in the United States.

description: To determine sociodemographic correlates of problematic screen use (social media, video games, mobile phones) among a racially/ethnically and socioeconomically diverse population-based sample of 10-14-year-old early adolescents.

We analyzed cross-sectional data from the Adolescent Brain Cognitive Development Study (Year 2, 2018-2020; N = 8753). Multiple linear regression analyses were used to estimate associations between sociodemographic factors (age, sex, race/ethnicity, primary language, household income, parental education) and adolescent-reported problematic video game (Video Game Addiction Questionnaire), social media (Social Media Addiction Questionnaire), and mobile phone use (Mobile Phone Involvement Questionnaire).

Boys reported higher problematic video game use while girls reported higher problematic social media and mobile phone use. Native American, black, and Latinx adolescents reported higher scores across all problematic screen measures compared to non-Latinx white adolescents. Having unmarried/unpartnered parents was associated with higher problematic social media use. Although higher household income was generally protective against problematic video game use, these associations were weaker for black than white adolescents (p for interaction <0.05).

Given the sociodemographic differences in problematic screen use, digital literacy education strategies can focus on at-risk populations, encourage targeted counseling by pediatricians, and adapt family media use plans for diverse backgrounds.

While sociodemographic differences in screen time are documented, we examined sociodemographic differences in problematic screen use in a large, diverse sample of early adolescents in the US. Boys reported higher problematic video game use while girls reported higher problematic social media and mobile phone use. Native American, black, and Latinx adolescents reported higher scores across all problematic screen measures compared to non-Latinx white adolescents. Although higher household income was generally protective against problematic video game use, these associations were weaker for black than white adolescents. Beyond time spent on screens, pediatricians, parents, and educators should be aware of sociodemographic differences in problematic screen use.
DOI: 10.15154/1528481
title: Replicability of the neuroanatomical correlates of impulsive personality traits in the ABCD study 

description: While the neuroanatomical correlates of impulsivity in youths have been examined, there is little research on whether those correlates are consistent across childhood/adolescence. The current study uses data from the age 11/12 (N = 7083) visit of the Adolescent Brain Cognitive Development (ABCD) Study to investigate the replicability of previous work (Owens et al, 2020) the neuroanatomical correlates of impulsive personality traits identified at age 9/10. Neuroanatomy was measured using structural and diffusion magnetic resonance imaging and impulsive personality was measured using the UPPS-P Impulsive Behavior Scale. Replicability was quantified using three Open Science Collaboration replication criteria, intraclass correlations, and elastic net regression modeling to make predictions across timepoints. Replicability was highly variable among traits: the neuroanatomical correlates of positive urgency showed substantial similarity between ages 9/10 and 11/12, negative urgency and sensation seeking showed moderate similarity across ages, and (lack of) premeditation and perseverance showed substantial dissimilarity across ages. In all cases, effect sizes between impulsive traits and brain variables were small. These findings suggest that, even for studies with large sample sizes and the same participant pool, the replicability of brain-behavior correlations across a two-year period cannot be assumed. This may be due to developmental changes across the two timepoints or replication failure. These results also highlight an array of neuroanatomical structures that may be important to impulsive personality traits across development from childhood into adolescence.
DOI: 10.15154/1528486
title: Overlapping brain correlates of superior cognition among children at genetic risk for Alzheimer’s disease and/ or major depressive disorder

description: Early life adversity (ELA) tends to accelerate neurobiological ageing, which, in turn, is thought to heighten vulnerability to both Major Depressive Disorder (MDD) and Alzheimer’s Disease (AD). The two conditions are putatively related, with MDD representing either a risk factor or early symptom of AD. Given the substantial environmental susceptibility of both disorders, timely identification of their neurocognitive markers could facilitate interventions to prevent clinical onset. To this end, we analysed multimodal data from the Adolescent Brain and Cognitive Development study (ages 9-10 years). To disentangle genetic from correlated genetic-environmental influences, while also probing gene-adversity interactions, we compared adoptees, a group generally exposed to substantial ELA, with children raised by their biological families via genetic risk scores (GRS) from genome-wide association studies. AD and MDD GRSs predicted overlapping and widespread neurodevelopmental alterations associated with superior fluid cognition. Specifically, among adoptees only, greater AD GRS were related to accelerated structural maturation (i.e., cortical thinning) and higher MDD GRS were linked to delayed functional neurodevelopment, as reflected in compensatory brain activation on an inhibitory control task. Our study identifies compensatory mechanisms linked to MDD risk and highlights the potential cognitive benefits of accelerated maturation linked to AD vulnerability in late childhood.
DOI: 10.15154/1528507
title: Uncovering and mitigating bias in large, automated MRI analyses of brain development

description:      Large, population-based adolescent MRI cohort studies promise transformational insights into neurodevelopment and mental illness risk.  However, MRI studies of young adolescents are especially susceptible to motion and other artifact. These artifacts may not be detected using automated quality control methods that are preferred in high-throughput imaging cohorts.  Whether larger sample size can offset unmeasured, artifact-related variance in brain-behavior relationships among children has remained unclear.  Here we demonstrate bias in structural MRI analyses of children due to inclusion of lower quality scans, as identified through rigorous visual quality control of 11,263 T1 MRI scans obtained at age 9-10 through the Adolescent Brain Cognitive Development (ABCD) Study.  Inclusion of lower quality scans introduced diffuse underestimates of cortical thickness and overestimates of cortical surface area (Cohen’s d 0.14-2.84).  In applied analyses, clinical-MRI relationships with stronger effect sizes were vulnerable to Type II error, while those with smaller effect sizes were vulnerable to Type I error.  These errors were somewhat mitigated by controlling for Euler number, an automated index of topological complexity that identified lower-quality scans with good specificity at Baseline (0.81-0.93) and in 1,000 Year 2 scans (0.88-1.00).  However, even among the highest quality scans, manual editing significantly changed cortical thickness across most of the cortex (d 0.15-0.92), a pattern replicated in a second adolescent cohort.  These findings demonstrate that large sample size can potentiate effects of inadequate quality control in structural MRI scans from children and adolescents, and that full mitigation of these errors requires manual quality control.
DOI: 10.15154/1528513
title: Modeling environment through a general exposome factor in two independent adolescent cohorts

description: Exposures to perinatal, familial, social, and physical environmental stimuli can have substantial effects on human development. We aimed to generate a single measure that capture’s the complex network structure of the environment (ie, exposome) using multi-level data (participant’s report, parent report, and geocoded measures) of environmental exposures (primarily from the psychosocial environment) in two independent adolescent cohorts: The Adolescent Brain Cognitive Development Study (ABCD Study, N = 11 235; mean age, 10.9 years; 47.7% females) and an age- and sex-matched sample from the Philadelphia Neurodevelopmental Cohort (PNC, N = 4993). We conducted a series of data-driven iterative factor analyses and bifactor modeling in the ABCD Study, reducing dimensionality from 348 variables tapping to environment to six orthogonal exposome subfactors and a general (adverse) exposome factor. The general exposome factor was associated with overall psychopathology (B = 0.28, 95% CI, 0.26-0.3) and key health-related outcomes: obesity (odds ratio [OR] , 1.4; 95% CI, 1.3-1.5) and advanced pubertal development (OR, 1.3; 95% CI, 1.2-1.5). A similar approach in PNC reduced dimensionality of environment from 29 variables to 4 exposome subfactors and a general exposome factor. PNC analyses yielded consistent associations of the general exposome factor with psychopathology (B = 0.15; 95% CI, 0.13-0.17), obesity (OR, 1.4; 95% CI, 1.3-1.6), and advanced pubertal development (OR, 1.3; 95% CI, 1-1.6). In both cohorts, inclusion of exposome factors greatly increased variance explained in overall psychopathology compared with models relying solely on demographics and parental education (from <4% to >38% in ABCD; from <4% to >18.5% in PNC). Findings suggest that a general exposome factor capturing multi-level environmental exposures can be derived and can consistently explain variance in youth’s mental and general health.
DOI: 10.15154/1528519
title: Strengthening associations between psychotic like experiences and suicidal ideation and behavior across middle childhood and early adolescence

description: Background: Understanding risk factors related to suicidal ideation (SI) and suicidal behaviors (SB) in youth is important for informing prevention and intervention efforts. While it appears that psychotic-like experiences (PLEs) are strongly associated with both SI and SB at different points across the lifespan, the longitudinal nature of this relationship in middle childhood and early adolescence is understudied.

Methods: The study used the unique longitudinal Adolescent Brain Cognitive Development Study data. Mixed effects linear models examined associations between PLEs and SI and SB over time using three time points of data from ages 9-13.

Results: First, analyses indicated that endorsement of SI and SB increased as youth grew older for those with increased distressing PLEs. Analyses found evidence of bidirectional relationships between PLEs with SI and SB, with evidence that PLEs at baseline were associated with worsening SI and SB over time, including a transition from SI to SB (β = 0.032, FDRp = 0.002). Exploratory analyses showed consistent evidence for strengthened associations over time for higher delusional ideation with both SI and SB (βs > 0.04, FDRps < 0.001), and for perceptual distortions with SB (βs = 0.046, FDRp < 0.001). When accounting for general psychopathology, for SB, the strengthened associations over time was significantly stronger for PLEs (β = 0.053, FDRp < 0.001) compared to general psychopathology (β = 0.022, FDRp = 0.01).

Conclusions: The present study indicates both SI and SB show strengthened associations with PLEs over time, and that baseline PLEs may predict worsening of suicidality over time. The findings are important clarifications about the nature of the associations between youth-reported PLEs and suicidality over time.
DOI: 10.15154/1528529
title: Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data

description: Many clinical and research studies of the human brain require an accurate structural MRI segmentation. While traditional atlas-based methods can be applied to volumes from any acquisition site, recent deep learning algorithms ensure very high accuracy only when tested on data from the same sites exploited in training (i.e., internal data). The performance degradation experienced on external data (i.e., unseen volumes from unseen sites) is due to the inter-site variabilities in intensity distributions induced by different MR scanner models, acquisition parameters, and unique artefacts. To mitigate this site-dependency, often referred to as the scanner effect, we propose LOD-Brain, a 3D convolutional neural network with progressive levels-of-detail (LOD) able to segment brain data from any site. Coarser network levels are responsible to learn a robust anatomical prior useful for identifying brain structures and their locations, while finer levels refine the model to handle site-specific intensity distributions and anatomical variations. We ensure robustness across sites by training the model on an unprecedented rich dataset aggregating data from open repositories: almost 27,000 T1w volumes from around 160 acquisition sites, at 1.5 - 3T, from a population spanning from 8 to 90 years old. Extensive tests demonstrate that LOD-Brain produces state-of-the-art results, with no significant difference in performance between internal and external sites, and robust to challenging anatomical variations. Its portability opens the way for large scale application across different healthcare institutions, patient populations, and imaging technology manufacturers. Code, model, and demo are available at the project website.
DOI: 10.15154/1528540
title: Modelling the Developing Connectome - Graph Representation Learning with Variational Autoencoders

description: The functional development of the human brain exhibits high inter-subject variability, which is influenced by factors such as genetic predisposition, environmental influences and individual learning experiences. A broadly accessible imaging modality for analysis of the brain’s functional development is found in resting state functional Magnetic Resonance Imaging (rs-fMRI), a non-invasive imaging technique covering the whole brain. However, analysis of rs-fMRI data in the context of developmental studies is challenging. Compared to adults, paediatric cohorts show higher in-scanner movement, which leads to erroneous intensity changes in the signal. Such artefacts, if not accounted for, lead to systematic errors in subsequent rs-fMRI analysis. Furthermore, approaches for developmental rs-
fMRI analysis must be able to capture general trends rather than individual differences due to inter-subject variability. In this work, a framework for analysis of the topological properties of the developing brain is presented. First, a paediatric preprocessing pipeline is developed and evaluated for its performance in removing in-scanner-movement-related artefacts. Second, an adaption of the graph-based Variational Autoencoder (VAE) is developed for representation learning on brain graphs derived from developmental rs- fMRI data in the age range of 7.5 to 18.5 years. Additionally, a graph-based conditional VAE is proposed, with the goal to enhance the VAE representation learning abilities. Furthermore, the VAEs are compared to a Bayesian regression baseline. The evaluation of the VAE-based approaches is threefold: First, the models’ brain graph reconstruction capabilities are evaluated in terms of the Mean Squared Error (MSE) and a correlation-based error measure. Second, the quality of datasets generated by the models is measured in terms of Maximum Mean Discrepancy (MMD) based on graph statistics distributions. Finally, the ability to capture developmental trajectories of a developmental dataset is investigated by statistical analysis using Network Based Statistic  (NBS). Results show that the developmental processes available in the used dataset cannot be modelled exactly by the presented modelling approaches. Additionally, extending the VAE with a condition does not improve its representation learning capabilities. However, trends, such as the continuous development of the Default Mode Network (DMN), are captured successfully by the graph-based VAE.
DOI: 10.15154/1528546
title: Brain-based predictions of psychiatric illness-linked behaviors across the sexes

description: Background: Individual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined. 
Methods: In this work, we used predictive modeling approaches to evaluate whether shared or unique resting-state functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors (measured using the Child Behavior Checklist) in males and females in data from the Adolescent Brain Cognitive Development study (n=5260; 2571 females). 
Results: We demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females, but only predict internalizing behaviors in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks including default, limbic, control, and dorsal attention networks are associated with internalizing and externalizing behaviors. 
Conclusions: Taken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. These results highlight the need to consider factors beyond just neurobiology in the diagnosis and treatment of psychiatric illnesses.
DOI: 10.15154/1528547
title: Mild Traumatic Brain Injury and Behavior and Sleep Among 9- and 10-Year Old Children: Initial Findings From the Adolescent Brain Cognitive Development (ABCD) Study

description: There has been concern about the potential sequelae of mild traumatic brain injury (mTBI) in children. This study used data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) study to investigate associations between mTBI and behavior and sleep in school-aged children. Generalized additive mixed models were run to examine the association between TBI and parent-reported Child Behavior Checklist and Sleep Disturbance Scale for Children scores. mTBI with or without loss of consciousness (LOC) in 9- and 10-year old children was associated with 1) higher internalizing, externalizing and total problems and 2) greater sleep disturbance scores on the CBCL. The study also demonstrated a higher incidence of mTBI with and without LOC in boys compared to girls. This study shows a statistically significant but modest association between mTBI and behavioral and sleep changes, suggesting that in a non-clinical, sociodemographically diverse community sample of school-aged children mTBI does not result in clinically significant behavioral or psychological sequelae.
DOI: 10.15154/1528585
title: Charting structural and functional development of the conNAcctome

description: Do structural connections carrying dopamine to the nucleus accumbens (NAcc) correlate with NAcc functional activity during reward anticipation? Previous research could characterize the white-matter tract projecting from the Ventral Tegmental Area (VTA) to the NAcc, and separate research has found greater NAcc activity during the anticipation of monetary rewards. However, no studies have linked the structural coherence of the VTA-NAcc tract to NAcc activity. To test this link from structure to function, we analyzed raw data of 4,944 subjects from the Adolescent Brain Cognitive Development Study (ABCD). Diffusion-weighted and Functional Magnetic Resonance Imaging (FMRI) data were screened for quality (<2 mm movement in 95% of acquired volumes; discovery sample n=87, mean age=10 years, 53% female). We identified regions to seed for tractography with the FreeSurfer subcortical segmentation of the NAcc and the Pauli atlas of the VTA. We then performed constrained spherical deconvolution-based probabilistic tractography with MRtrix to visualize the VTA-NAcc tract in each hemisphere of every subject, and measured Fractional Anisotropy (FA) of the tract along its trajectory. Functionally, we extracted raw activity timecourses (percent signal change) from the NAcc, aligned the timecourse with task trial phases, and targeted NAcc activity during reward anticipation. Replicating the functional literature, NAcc activity was greatest during the anticipation of large gains ($5>$0: left hemisphere: t(86)=2.97, p<0.01; right: t(86)=2.98, p<0.01). Structural coherence at the beginning of the VTA-NAcc tract was associated with greater NAcc activity during the anticipation of large gains in both hemispheres (left: r=0.25, p<0.05; right: r=0.34, p<0.01). The tract was not associated with NAcc activity during the anticipation of smaller gains and no reward. We then reproduce the analysis in randomly-sampled subsets of the full dataset based on a power analysis corrected for the highest observable correlation given test-retest reliability of the structural and functional measurements (n=461; given observable r=0.13 with 80% power at alpha=0.05). These findings suggest that structural tracts are associated with functional activity at tract endpoints, and further identify targets to study the reciprocal development of deep brain structure and function.
DOI: 10.15154/1528589
title: comparing EEG metrics during eyes closed versus eyes open rest in schizophrenia 

description: Understanding the complex relationship between brain dynamics and mental disorders has proved difficult. Sample sizes have often been small, and brain dynamics have often been evaluated in only one state. Here, data obtained from the NIMH data archive were used to create a sample of 800 individuals with both eyes open and eyes closed resting state EEG data. All data were submitted to a standard pipeline to extract power spectra, peak alpha frequency, the slope of the 1/f curve, multi scale sample entropy, phase amplitude coupling, and intersite phase clustering. These data along with the survey data collected at the time of data collection form a valuable resource for interogating the relationship between brain state changes and schizophrenia symptoms.
DOI: 10.15154/1528590
title: comparing EEG metrics during eyes closed versus eyes open rest in autism

description: Understanding the complex relationship between brain dynamics and mental disorders has proved difficult. Sample sizes have often been small, and brain dynamics have often been evaluated in only one state. Here, data obtained from the NIMH data archive were used to create a sample of 395 individuals with both eyes open and eyes closed resting state EEG data. All data were submitted to a standard pipeline to extract power spectra, peak alpha frequency, the slope of the 1/f curve, multi scale sample entropy, phase amplitude coupling, and intersite phase clustering. These data along with the survey data collected at the time of data collection form a valuable resource for interogating the relationship between brain state changes and autism diagnosis.
DOI: 10.15154/1528600
title: Development of EEG dynamics throughout the lifespan

description: Combining data from across several datasets available on the NIMH data repository, multiple metrics of EEG dynamics were examined in a large cross sectional sample of healthy participants from across the lifespan. The goal was to examine changes in brain dynamics that occur across development.