cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15154/1522607
title: Semantic Network Modeling

description: Although it is well documented that children with ASD are slower to develop their lexicons, we still have a limited understanding of the structure of early lexical knowledge in children with ASD. The current study uses network analysis to examine the structure of semantic knowledge, which may provide insight into the learning processes that influence early word learning.
DOI: 10.15154/1522853
title: Brain structural associations with depression in a large early adolescent sample (the ABCD Study)

description: Background: Depression is the leading cause of disability worldwide with >50% of cases emerging before the age of 25 years. Large-scale neuroimaging studies in depression implicate robust structural brain differences in the disorder. However most studies have been conducted in adults and therefore, the temporal origins of depression-related imaging features remain largely unknown. This has important implications for understanding aetiology and informing timings of potential intervention.
 
Methods: Here, we examine associations between brain structure (cortical metrics and white matter microstructural integrity) and depression ratings (from caregiver and child), in a large sample of early adolescents from the Adolescent Brain and Cognitive Development (ABCD) Study (N=9981, 9-11-year-olds). 

Findings: We report significantly decreased global cortical and white matter metrics, and regionally in frontal, limbic and temporal areas in adolescent depression (Cohen’s d = -0.018 to -0041, β = -0·019 to -0.057). Further, we report consistently stronger imaging associations for caregiver-reported compared to child-reported depression ratings. Divergences between reports (caregiver vs child) were found to significantly relate to negative socio-environmental factors (e.g., family conflict, absolute β=0.048 to 0.169).

Interpretation: Depression ratings in early adolescence were associated with similar imaging findings to those seen in adult depression samples, suggesting neuroanatomical abnormalities may be present early in the disease course, arguing for the importance of early intervention. Associations between socio-environmental factors and reporter discrepancy warrant further consideration, both in the wider context of the assessment of adolescent psychopathology, and in relation to their role in aetiology. 

Funding: Wellcome Trust, Mental Health Research UK.
DOI: 10.15154/1522906
title: Contemporary screen time usage among children 9-10-years-old is associated with higher body mass index percentile at 1-year follow-up: A prospective cohort study

description: Objective: There is a paucity of prospective research exploring the relationship among contemporary screen time modalities (e.g., video streaming, video chatting, texting and social networking) and body mass index (BMI) percentile. The objective of this study was to determine the prospective associations between screen time behaviours in a large and demographically diverse population-based cohort of 9-10-year-old children and BMI percentile at 1-year follow-up.

Methods: We analyzed prospective cohort data from the Adolescent Brain Cognitive Development (ABCD) Study (N = 11 066). Multiple linear regression analyses were conducted to estimate associations between baseline screen time behaviours (exposure) and BMI percentile at 1-year follow-up, adjusting for race/ethnicity, sex, household income, parent education, depression, binge-eating disorder and baseline BMI percentile.

Results: Each additional hour of total screen time per week was prospectively associated with a 0.22 higher BMI percentile at 1-year follow-up (95% CI 0.10-0.34) after adjusting for covariates. When examining specific screen time behaviours, each additional hour of texting (B = 0.92, 95% CI 0.29-1.55), video chat (B = 0.72, 95% CI 0.09-1.36) and video games (B = 0.42, 95% CI 0.06-0.78) was significantly prospectively associated with higher BMI percentile.

Conclusions: Screen time is prospectively associated with a higher BMI percentile 1 year later among children 9-10 years old.
DOI: 10.15154/1522913
title: Obesogenic Environment

description: Ecological theories suggest environmental, social, and individual factors interact to cause obesity. Yet, many analytic techniques, such as multilevel modeling, require manual specification of interacting factors, making them inept in their ability to search for interactions. This paper shows evidence that an explainable artificial intelligence approach, commonly employed in genomics research, can address this problem. The method entails using random intersection trees to decode interactions learned by random forest models. Here, this approach is used to extract interactions between features of a multi-level environment from random forest models of waist-to-height ratios using 11,112 participants from the Adolescent Brain Cognitive Development study. This study shows that methods used to discover interactions between genes can also discover interacting features of the environment that impact obesity. This new approach to modeling ecosystems may help shine a spotlight on combinations of environmental features that are important to obesity, as well as other health outcomes.
DOI: 10.15154/1523058
title: Attention-Deficit/Hyperactivity Disorder symptoms and Brain Morphology: Examining Confounding Bias

description: Background: Associations between attention-deficit/hyperactivity disorder (ADHD) and brain morphology have been reported, although with several inconsistencies. These may partly stem from confounding bias, which could distort associations and limit generalizability. We examined how associations between brain morphology and ADHD symptoms change with adjustments for potential confounders typically overlooked in the literature (aim 1), and for IQ and head motion, which are typically corrected for but play ambiguous roles (aim 2).
Methods: Participants were 10-year-old children from the Adolescent Brain Cognitive Development (N=7,722) and Generation R (N=2,531) studies. Cortical area, volume, and thickness were measured with MRI and ADHD symptoms with the Child Behavior Checklist. Surface-based cross-sectional analyses were run. 
Results: ADHD symptoms related to widespread cortical regions when solely adjusting for demographic factors. Additional adjustments for socioeconomic and maternal behavioral confounders (aim 1) generally attenuated associations, as cluster sizes halved and effect sizes substantially reduced. Cluster sizes further changed when including IQ and head motion (aim 2), however, we argue that adjustments might have introduced bias. 
Conclusions: Careful confounder selection and control can help identify more robust and specific regions of associations for ADHD symptoms, across two cohorts. We provided guidance to minimizing confounding bias in psychiatric neuroimaging.
DOI: 10.15154/1523116
title: Effect of exposure to maternal diabetes during pregnancy on offspring’s brain cortical thickness and neurocognitive functioning

description: OBJECTIVE: Maternal diabetes may affect the developing brain of the fetus, which may adversely affect the neurocognitive functioning (NCF) of diabetes-exposed children. We examined the effect of prenatal exposure to maternal diabetes (DP) on brain structure and neurocognition in preadolescent children, ages 9-10.  
RESEARCH DESIGN AND METHODS: This secondary data analysis study used cross-sectional structural neuroimaging and NCF data from the  Adolescent Brain and Cognitive Development (ABCD) study (N=9,963). Differences in brain cortical thickness (CTh) and five cognitive abilities (executive function, working and episodic memory, processing speed, and language abilities) between diabetes-exposed and unexposed children were examined. Generalized linear models were used to adjust for the effect of confounding variables. Indirect effect of CTh into the relationship between maternal DP and NCF were also examined.
RESULTS: The average age of the children was 9.9 years (SD 7.5); half of them were male and non-Hispanic White. Diabetes-exposed children (n=714) had lower CTh of the whole-brain (2.744mm VS 2.756mm; p 0.008) and lower scores in processing speed task (85.97 VS 87.28; p=0.021 compared to unexposed children (n=9249) after adjusting for demographic and other confounding variables. Diabetes-exposed children also had lower score in fluid intelligence [β (95%CI): -0.837 (-1.604, -0.171)]) and total cognition [β (95%CI): -0.728 (-1.338,-0.119)]. CTh partially mediated [Direct effect=β (95%CI): -3.239 (-5.834, -0.644); indirect effect=β (95%CI): -3.239 (-5.834, -0.644)] the effect of maternal DP on offspring’s processing speed.
CONCLUSION: Diabetes-exposed children have reduced CTh and NCF during preadolescence age, which may have implications for psychomotor development during later life. Prospective studies are needed to confirm our findings
DOI: 10.15154/1523126
title: The role of neurobiology in the association between pubertal timing and depression risk in early adolescence: A registered report 

description: Background
Earlier pubertal timing (PT) is associated with higher rates of depressive disorders in adolescence. Neuroimaging studies report brain structural associations with both pubertal timing and depression. However, whether brain structure mediates the relationship between PT and depression remains unclear. 

Methods
The current registered report examined associations between PT (indexed via perceived pubertal development), brain structure (cortical and subcortical metrics, and white matter microstructure) and depressive symptoms (DS) in a large sample (N = ~5,000) of young adolescents (aged 9-13 years) from the Adolescent Brain Cognitive Development (ABCD) Study. We used three waves of follow-up data when the youth were aged 10-11 years, 11-12 years, and 12-13 years, respectively. We used generalised linear-mixed models (H1) and structural equation modelling (H2 & H3) to test our hypotheses. 

Hypotheses
We hypothesised that earlier PT at Year 1 would be associated with increased DS at Year 3 (H1), and that this relationship would be mediated by global (H2a-b) and regional (H3a-g) brain structural measures at Year 2. Global measures included reduced cortical volume, thickness, surface area and sulcal depth. Regional measures included reduced cortical thickness and volume in temporal and fronto-parietal areas, increased cortical volume in the ventral diencephalon, increased sulcal depth in the pars orbitalis, and reduced fractional anisotropy in the cortico-striatal tract and corpus callosum. These regions of interest were informed by our pilot analyses using baseline ABCD data when the youth were aged 9-10 years. 

Results
Earlier pubertal timing was associated with increased depressive symptoms two years later. The magnitude of effect was stronger in female youth and the association remained significant when controlling for parental depression, family income, and BMI in females but not in male youth. On the other hand, our hypothesised brain structural measures did not mediate the association between earlier pubertal timing and later depressive symptoms. 

Conclusion
The present results demonstrate that youth, particularly females, who begin puberty ahead of their peers are at an increased risk for adolescent-onset depression. Future work should explore additional biological and socio-environmental factors that may affect this association so that we can identify targets for intervention to help these at-risk youth.
DOI: 10.15154/1524262
title: Reliability of Subsequent Memory Effects in Children and Adults: The Good, the Bad, and the Hopeful

description: Functional MRI (fMRI) is a key tool for investigating neural underpinnings of cognitive development. Yet, in recent years, the reliability of fMRI effects has come into question and with it, the feasibility of using task-based fMRI to identify developmental changes related to cognition. Here, we investigated the reliability of task-based fMRI activations with a widely used subsequent memory paradigm using two developmental samples: a cross-sectional sample (n=85, age 8 to 25 years) and a test-retest sample (n=24, one-month follow up, age 8 to 20 years). In the large cross-sectional sample, we found good to excellent group-level reliability when assessing activation patterns related to the encoding task and subsequent memory effects. In the test-retest sample, while group-level reliability was excellent, the consistency of activation patterns within individuals was low, particularly for subsequent memory effects. We observed consistent activation patterns in frontal, parietal, and occipital cortices, but comparatively lower test-retest reliability in subcortical regions and the hippocampus. Together, these findings highlight the limitations of interpreting task-based fMRI effects and the importance of incorporating reliability analyses in developmental studies. Leveraging larger and densely collected longitudinal data may help contribute to increased reproducibility and the accumulation of knowledge in developmental sciences.
DOI: 10.15154/1524424
title: A fully convolutional neural network for explainable classification of attention deficit hyperactivity disorder

description: Attention deficit/hyperactivity disorder (ADHD) is characterized by symptoms of inattention, hyperactivity, and impulsivity, which affects an estimated 10.2% of children and adolescents in the United States. However, correct diagnosis of the condition can be challenging, with failure rates up to 20%. Machine learning models making use of magnetic resonance imaging (MRI) have the potential to serve as a clinical decision support system to aid in the diagnosis of ADHD in youth to improve diagnostic validity. The purpose of this study was to develop and evaluate an explainable deep learning model for automatic ADHD classification. 254 T1-weighted brain MRI of youth aged 9-11 were obtained from the Adolescent Brain Cognitive Development (ABCD) Study, and the Child Behaviour Checklist DSM-Oriented ADHD Scale was used to partition subjects into ADHD and non-ADHD classes. A fully convolutional neural network (CNN) adapted from a state-of-the-art adult brain age regression model was trained to distinguish between the neurologically normal children and children with ADHD. Saliency voxel attribution maps were generated to identify brain regions relevant for the classification tasks. The proposed model achieved an accuracy of 71.1%, sensitivity of 68.4%, and specificity of 73.7%. Saliency maps highlighted the orbitofrontal cortex, entorhinal cortex, and amygdala as important regions for the classification, which is consistent with previous literature linking these regions to significant structural differences in youth with ADHD. To the best of our knowledge, this is the first study applying explainability methods such as saliency maps to the classification of ADHD using a deep learning model. The proposed deep learning classification model has the potential to aid clinical diagnosis of ADHD while providing interpretable results.
DOI: 10.15154/1524653
title: Social Determinants of Health and Child Mental health during COVID-19 

description: Using ABCD main study and COVID-19 RRR cohorts to examine the impact of SDoH on changes in child mental health trajectories during COVID-19.
DOI: 10.15154/1524682
title: Puberty differentially predicts brain maturation in males and females during early adolescence: A longitudinal ABCD Study

description: Background:
Research has demonstrated associations between pubertal development and brain maturation. However, existing studies have been limited by small samples, cross-sectional designs, and inconclusive findings regarding coupling directionality and sex differences.

Methods:
We examined the longitudinal temporal coupling of puberty status assessed using the Pubertal Development Scale (PDS) and magnetic resonance imaging (MRI)-based brain grey and white matter structure. Our sample consisted of 8,896 children and adolescents at baseline (mean age = 9.9) and 6,099 at follow-up (mean age = 11.9) from the Adolescent Brain and Cognitive Development (ABCD) Study.

Results:
Applying multi-group Bivariate Latent Change Score (BLCS) models, we found that baseline pubertal status predicted the rate of change in cortical thickness among females only, while it predicted rate of change in cortical surface area for both males and females, with a significantly stronger association for females. For cortical surface area, we also found a correlation between baseline pubertal status and area and co-occurring changes over time, with both associations present in males only. Diffusion tensor imaging (DTI) analysis revealed correlated change between pubertal status and fractional anisotropy (FA) for both males and females, but no significant associations for mean diffusivity (MD).

Conclusions:
Our results suggest that pubertal status in early adolescence predicts cortical maturation, and that the strength of the associations differ between sex. Further research is needed to understand how the associations between puberty and brain maturation are related to environmental and lifestyle factors, and the impact on mental health.
DOI: 10.15154/1524692
title: Prenatal Selective Serotonin Reuptake Inhibitor Exposure, Depression, and Brain Morphology in Middle Childhood: Results from the ABCD Study

description: Background: Prenatal selective serotonin reuptake inhibitor (SSRI) exposure has been inconsistently linked to depression and little is known about neural correlates. We examined whether prenatal SSRI exposure is associated with depressive symptoms and brain structure during middle childhood.
Methods: Prenatal SSRI exposure (retrospective caregiver-report), depressive symptoms (caregiver-reported Child Behavior Checklist) and brain structure (MRI-derived subcortical volume; cortical thickness and surface area) were assessed in children (analytic ns=5,420-7,528; 235 with prenatal SSRI exposure; 9-10 years old) who completed the baseline Adolescent Brain and Cognitive Development Study session. Linear mixed effects models nested data. Covariates included familial, pregnancy, and child variables. Matrix spectral decomposition adjusted for multiple testing.
Results: Prenatal SSRI exposure was not independently associated with depression after accounting for recent maternal depressive symptoms. Prenatal SSRI exposure was associated with greater left superior parietal surface area (b=145.3 mm2, p=0.00038) and lateral occipital cortical thickness (b=0.0272 mm, p=0.0000079); neither was associated with child depressive symptoms. Child depression was associated with smaller global brain structure.  
Conclusions: Our findings, combined with adverse outcomes of exposure to maternal depression and the utility of SSRIs for treating depression, suggest that risk for depression during middle childhood should not discourage SSRI use during pregnancy. Associations between prenatal SSRI exposure and brain structure were small in magnitude and not associated with depression. It will be important for future work to examine associations between prenatal SSRI exposure and depression through young adulthood, when risk for depression increases.
DOI: 10.15154/1524714
title: Individual variability in the nonlinear development of the corpus callosum during infancy and toddlerhood

description: The human brain spends several years bootstrapping itself through intrinsic and extrinsic modulation, thus gradually developing both spatial organization and functions. Based on previous studies on developmental patterns and inter-individual variability of the corpus callosum (CC), we hypothesized that inherent variations of CC shape among infants emerge, depending on the position within the CC, along the developmental timeline. Here we used longitudinal magnetic resonance imaging data from infancy to toddlerhood and investigated the area, thickness, and shape of the midsagittal plane of the CC by applying multilevel modeling. The shape characteristics were extracted using the Procrustes method. We found nonlinearity, region- dependency, and inter-individual variability, as well as intra-individual consistencies, in CC development. Overall, the growth rate is faster in the first year than in the second year, and the trajectory differs between infants; the direction of CC formation in individual infants was determined within six months and maintained to two years. The anterior and posterior subregions increase in area and thickness faster than other subregions. Moreover, we clarified that the growth rate of the middle part of the CC is faster in the second year than in the first year in some individuals. Since the division of regions exhibiting different tendencies coincides with previously reported divisions based on the diameter of axons that make up the region, our results suggest that subregion-dependent individual variability occurs due to the increase in the diameter of the axon caliber, myelination partly due to experience and axon elimination during the early developmental period.
DOI: 10.15154/1524721
title: The molecular genetics of Malformations of Cortical Development

description: In this study, whole-exome sequencing (WES) was used to detect potential somatic mutations causing Malformations of Cortical Development, such as Focal Cortical Dysplasia (FCD), Hemimegalencephaly (HME), Tuberous sclerosis complex (TSC). WES from to the affected brain samples fro surgery and control samples (blood or saliva) were subjected to state-of-the-art detection software and somatic mutations putative to those disorders were detected.
DOI: 10.15154/1524751
title: Social problems and brain structure trajectories following childhood mild traumatic brain injury

description: Childhood traumatic brain injury (pTBI), even mild (mTBI), is associated with risk of developing social problems, including aggressive behaviors and social cognitive deficits, which may profoundly impact the child’s quality of life [Zamani et al., 2019]. The maturation of the neural bases of social behaviors and cognition, sometimes referred to as “the social brain”, is protracted, continuing throughout childhood and adolescence, and correlates with increasing capacity for social information processing [Blakemore, 2012]. Therefore, pTBI may disrupt the expected developmental trajectory of the social brain structure that could manifest as development of social problems. However, limited sample sizes and cross-sectional designs generally used in the neuroimaging study of pTBI so far [Zamani et al., 2019], prevented from exploring developmental trajectories following pTBI. The present study aims to examine the longitudinal development of social problems and cortical thickness in social brain regions following pediatric mTBI using data from the large population-based cohort study Adolescent Brain Cognitive Development (ABCD) [Casey et al., 2018]).
DOI: 10.15154/1524758
title: Correction to Moore et al. (2020)

description: Reports an error in "Criterion validity and relationships between alternative hierarchical dimensional models of general and specific psychopathology" by Tyler M. Moore, Antonia N. Kaczkurkin, E. Leighton Durham, Hee Jung Jeong, Malerie G. McDowell, Randolph M. Dupont, Brooks Applegate, Jennifer L. Tackett, Carlos Cardenas-Iniguez, Omid Kardan, Gaby N. Akcelik, Andrew J. Stier, Monica D. Rosenberg, Donald Hedeker, Marc G. Berman and Benjamin B. Lahey (Journal of Abnormal Psychology, Advanced Online Publication, Jul 16, 2020, np). In the article (http://dx.doi.org/10.1037/abn0000601), an acknowledgment is missing from the author note. The missing acknowledgement is included in the erratum.