cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3687342
title: [2019 QSM Reconstruction Challenge] Submissions Stage 1

description: This repository contains the original, unaltered files submitted to Stage 1 of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The data provided to applicants of the challenge along with the scripts used to obtain the evaluation metrics are available here. Information about the submitted solutions and resulting analysis metrics are available here. The results of the challenge are fully reported in the journal article "QSM Reconstruction Challenge 2.0: Design and Report of Results".
DOI: 10.5281/zenodo.3687341
title: [2019 QSM Reconstruction Challenge] Submissions Stage 1

description: This repository contains the original, unaltered files submitted to Stage 1 of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge. The data provided to applicants of the challenge along with the scripts used to obtain the evaluation metrics are available here. Information about the submitted solutions and resulting analysis metrics are available here. The results of the challenge are fully reported in the journal article "QSM Reconstruction Challenge 2.0: Design and Report of Results".
DOI: 10.15154/1527843
title: Understanding Associations between Race/Ethnicity, Experiences of Discrimination, and Psychotic-like Experiences in Middle Childhood

description: Objective: The present study aimed to examine factors that may account for race/ethnicity differences in psychotic-like experiences (PLEs) in a middle childhood sample, including evidence for experiences of discrimination as a psychosocial mediator of these differences. 
Method: Within a sample of 10,839 9 to 10-year-olds from the Adolescent Brain Cognitive Development℠ study, we compared PLEs across racial/ethnic groups. We also examined whether experiences of discrimination indirectly linked racial/ethnic identity and PLEs, and whether social support moderated this indirect association.
Results: Results indicated differences between racial/ethnic groups in the endorsement of PLEs, such that Black and Hispanic participants endorsed higher levels of PLEs as compared to Asian, Multiracial/Multiethnic, and White individuals. We found these differences were accounted for in part by experiences of discrimination, an indirect effect that was in turn attenuated by increased social support. 
Conclusion: This is the first study to suggest that the experience of discrimination may indirectly link the association between racial/ethnic differences and endorsement of PLEs using the PQ-BC, and additionally that social support may act as a moderator of this mediation. Results provide evidence that social inequities such as racial discrimination may contribute to increases in psychotic-like experiences. These findings shed further light on a possible mechanism linking structural racism and mental health inequities for people in minoritized groups.
DOI: 10.15154/1527862
title: Clouding up cognition?: Secondhand cannabis and tobacco exposure related to cognitive functioning in youth

description: Background
Increasing legalization of cannabis, in addition to longstanding rates of tobacco use, raises concerns for possible cognitive decrements from secondhand smoke or environmental exposure, although little research exists. We investigate the relation between cognition and secondhand and environmental cannabis and tobacco exposure in youth.

Methods
The Adolescent Brain Cognitive Development (ABCD) Study year 2 follow-up (N = 5580; 48% female) cognitive performance and secondhand or environmental cannabis or tobacco exposure data were used. Principal components analysis identified a global cognition factor. Linear mixed-effects models assessed global cognition and individual cognitive task performance by cannabis and/or tobacco environmental exposure. Sociodemographics and other potential confounds were examined. p values were adjusted using the false discovery rate method.

Results
Global cognition was not related to any exposure group after testing corrections and considering confounds. Beyond covariates and family- and site-level factors, secondhand tobacco was related to poorer visual memory (p = .02), and environmental tobacco was associated with poorer visuospatial (p = .02) and language (p = .008) skills. Secondhand cannabis was related to cognition, but not after controlling for potential confounders (p > .05). Environmental cannabis was related to better oral reading (p = .01). Including covariates attenuated effect sizes.

Conclusions
Secondhand tobacco exposure was associated with poorer visual memory, while environmental tobacco exposure was related to poorer language and visuospatial skills. Secondhand cannabis was not related to cognition after controlling for sociodemographic factors, but environmental cannabis exposure was related to better reading. Because, to our knowledge, this is the first known study of its kind and thus preliminary, secondhand cannabis should continue to be investigated to confirm results.
DOI: 10.15154/1527868
title: Using synthetic MR images for field map-less distortion correction

description: Functional MRI (fMRI) data acquired using echo-planar imaging (EPI) are highly distorted by magnetic field inhomogeneities. Distortion combined with underlying differences in image contrast between EPI and T1-weighted and T2-weighted (T1w/T2w) structural images makes the alignment of functional and anatomical images a challenge. Typically, separately acquired field map data are used to correct fMRI distortions and a flexible cost function insensitive to cross-modal differences in image contrast and intensity is used for aligning fMRI and anatomical images. The quality of alignment achieved with this approach can vary greatly and depends on the quality of field map data. In addition, many publicly available datasets lack field map data entirely. To address this issue, we developed Synth, a software package for distortion correction and cross-modal image registration that does not require separately acquired field map data. Synth combines information from T1w and T2w anatomical images to construct an idealized undistorted synthetic image that has similar contrast properties to fMRI data. The undistorted synthetic image then serves as an effective reference for individual-specific nonlinear unwarping to correct fMRI distortions. We demonstrate, in both pediatric (ABCD: Adolescent Brain Cognitive Development) and adult (MSC: Midnight Scan Club) data that Synth performs comparably well to other leading distortion correction approaches that utilize field map data, and often outperforms them. Field map-less distortion correction with Synth allows accurate and precise registration of fMRI data with missing or corrupted field map information.
DOI: 10.15154/1527870
title: Reproducible brain-wide association studies require thousands of individuals

description: Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (e.g., lesion studies) and functions (e.g., task functional MRI [fMRI])1–3. Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure/function and complex cognitive/mental health phenotypes (brain-wide association studies [BWAS]). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping (median neuroimaging study n ~ 254), but potentially too small for capturing reproducible brain-behavioral phenotype associations5,6. Here, we used the three largest neuroimaging datasets with n ~ 50,000 individuals to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes, and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. Relatively more robust BWAS effects were detected for functional MRI (vs. structural), cognitive tests (vs. mental health questionnaires) and multivariate methods (vs. univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (e.g., lesions, interventions, within-person), BWAS reproducibility requires samples with thousands of individuals.
DOI: 10.15154/1527877
title: A cross-cohort replicable and heritable latent dimension linking behaviour to multi-featured brain structure

description: Identifying the complex association between inter-individual variability in brain structure and in behaviour is challenging, requiring large cohorts, multivariate methods, out-of-sample validation and, ideally, out-of-cohort replication. Moreover, the influence of nature vs nurture on such associations could be better understood through heritability analysis. Here, we analysed associations between brain structure and behaviour using regularized canonical correlation analysis and a recently proposed machine learning framework that tests the generalisability of such associations. We linked behaviour (spanning cognition, emotion, and alertness) and multi-featured brain structure (grey matter volume, cortical thickness, and surface area). The replicability of such brain-behaviour associations was assessed in two large and independent cohorts. The load of genetic factors on these latent dimensions was analysed with heritability and genetic correlation. We found one heritable and replicable latent dimension. This latent dimension was positively associated with cognitive-control/executive-functions and positive affect, and negatively associated with impulsivity and negative affect. This behavioural profile was related to brain structural variability in areas typically associated with higher cognitive functions, as well as with areas typically associated with sensorimotor functions. These results revealed a major axis of interindividual variability in behaviour that links to a whole-brain structural pattern.
DOI: 10.15154/1527881
title: Brain structural covariation linked to screen media activity and externalizing behaviors in children

description: Background and Aims
Screen media activity (SMA) may impact neurodevelopment in youth. Cross-sectionally, SMA has been linked to brain structural patterns including cortical thinning in children. However, it remains unclear whether specific brain structural co-variation patterns are related to SMA and other clinically relevant measures such as psychopathology, cognition and sleep in children.

Methods
Adolescent Brain Cognitive Development (ABCD) participants with useable baseline structural imaging (N = 10,691; 5,107 girls) were analyzed. We first used the Joint and Individual Variation Explained (JIVE) approach to identify cortical and subcortical covariation pattern(s) among a set of 221 brain features (i.e., surface area, thickness, or cortical and subcortical gray matter (GM) volumes). Then, the identified structural covariation pattern was used as a predictor in linear mixed-effect models to investigate its associations with SMA, psychopathology, and cognitive and sleep measures.

Results
A thalamus-prefrontal cortex (PFC)-brainstem structural co-variation pattern (circuit) was identified. The pattern suggests brainstem and bilateral thalamus proper GM volumes covary more strongly with GM volume and/or surface area in bilateral superior frontal gyral, rostral middle frontal, inferior parietal, and inferior temporal regions. This covariation pattern highly resembled one previously linked to alcohol use initiation prior to adulthood and was consistent in girls and boys. Subsequent regression analyses showed that this co-variation pattern associated with SMA (β = 0.107, P = 0.002) and externalizing psychopathology (β = 0.117, P = 0.002), respectively.

Discussion and Conclusions
Findings linking SMA-related structural covariation to externalizing psychopathology in youth resonate with prior studies of alcohol-use initiation and suggest a potential neurodevelopmental mechanism underlying addiction vulnerability.
DOI: 10.15154/1527892
title: Effect of maternal hypertensive disorder on their children’s neurocognitive functioning

description: Objective: The aim of the study was to examine the effect of prenatal exposure to maternal HDP on brain structure and NCF in singleton children aged between 9-10 years the baseline wave of the Adolescent Brain and Cognitive Development (ABCD) Study.
Methods: The ABCD Study® interviewed each child (and their parents), measured NCF, and performed neuroimaging. Exposure to maternal high blood pressure (HBP) and preeclampsia or eclampsia (PE/EL) were extracted from the developmental history questionnaire. Differences in cortical thickness (CTh) and five cognitive abilities (executive function, working and episodic memory, processing speed, and language abilities) between exposed and unexposed children were examined using generalized linear models. The mediating effects of CTh, birthweight, and BMI on the relationship between maternal HDP on NCF were also examined.
Result: A total of 584-children exposed to HBP, 387-children exposed to PE/EL, and 5,877 unexposed children were included in the analysis. Neither CTh nor NCF differed between the exposed and unexposed children with or without adjusting for the confounders including the child’s age, sex, race, education, and birth histories. The whole-brain CTh did not mediate any of the relationships between HDP and NCF. However, the relationship between HDP and most of the NCF was mediated by birthweight and BMI.
Conclusions: Our results do not support maternal HDP, in comparison to other perinatal risk factors, as a direct risk factor for later-life cognitive functions. Prospective longitudinal studies, following up from infancy, are needed to further delineate the effect of HDP on children’s cognitive abilities.
DOI: 10.15154/1527896
title: Longitudinal assessment of brain structure and behavior in youth with rapid weight gain: Potential contributing causes and consequences

description: Objective: Independent of weight status, rapid weight gain has been associated with underlying brain structure variation in regions associated with food intake and impulsivity among pre-adolescents. Yet, we lack clarity on how developmental maturation coincides with rapid weight gain and weight stability. 

Methods: We identified brain predictors of two-year rapid weight gain and its longitudinal effects on brain structure and impulsivity in the Adolescent Brain Cognitive DevelopmentSM Study®. Youth were categorized as Healthy Weight/Weight Stable (WSHW, n=527) or Weight Gainers (WG, n=221, >38lbs); 63% of the WG group were healthy weight at 9-to-10-years-old. 

Results: A five-fold cross-validated logistic elastic-net regression revealed that rapid weight gain was associated with structural variation amongst 39 brain features at 9-to-10-years-old in regions involved with executive functioning, appetitive control, and reward sensitivity. Two years later, WG youth showed differences in change over time in several of these regions and performed worse on measures of impulsivity. 

Conclusions: These findings suggest that brain structure in pre-adolescence may predispose some to rapid weight gain and that weight gain itself may alter maturational brain change in regions important for food intake and impulsivity. Behavioral interventions that target inhibitory control may improve trajectories of brain maturation and facilitate healthier behaviors.
DOI: 10.15154/1527897
title: Leveraging artificial intelligence to develop novel tools for studying infant brain development

description:  Little is known about the growth trajectories of brain development across the first 24-months of life because of insufficient approaches to analyze infant MRI scans especially brain segmentation. This proposal leverages artificial intelligence to address unmet technical challenges in infant brain segmentation, to delineate the growth trajectories of brain structure/function and measure their relationship with neuropsychological functions, and to predict the developmental outcomes. Results from this study will yield a crucial new tool for studying developmental neuroscience and improve our capacity to efficiently measure and identify relevant infant brain structures, function, and their role in long-term development.
DOI: 10.15154/1527902
title: Social problems and brain structure trajectories following childhood mild traumatic brain injury (Release 4.0)

description: Childhood mild traumatic brain injury (mTBI) is associated with elevated risk of developing social problems, which may be underpinned by changes in the structural developmental trajectory of the social brain, a network of cortical regions supporting social cognition and behavior. However, limited sample sizes and cross-sectional designs generally used in neuroimaging studies of pediatric TBI have prevented explorations of this hypothesis. This longitudinal retrospective study examined the development of parent-reported social problems and cortical thickness in social brain regions following childhood mTBI using data from the large population-based Adolescent Brain Cognitive Development (ABCD) Study. Two-group latent change score models revealed different developmental trajectories from ages 10 and 12 years in social problems between children with (n=345) and without (n=7,089) mTBI. Children with mTBI showed higher levels of social problems than controls at age 10. Then, social problems decreased over 2 years, but still remained higher than in controls in which they stayed stable. Both groups showed similar decrease in the social brain cortical thickness between ages 10 and 12 years. Further studies providing detailed information on the injury mechanism and acute symptoms are needed to better understand individual differences in social impairment and brain development in pediatric TBI.
DOI: 10.15154/1527914
title:  Relating neighborhood deprivation to childhood obesity in the ABCD Study®: evidence for theories of neuroinflammation and neuronal stress

description: Objective: We evaluated whether relationships between area deprivation (ADI), body mass index (BMI) and brain structure (e.g., cortical thickness, subcortical volume) during pre-adolescence supported the immunologic model of self-regulation failure (NI) and/or neuronal stress (NS) theories of overeating. The NI theory proposes that ADI causes structural alteration in the brain due to the neuroinflammatory effects of overeating unhealthy foods. The NS theory proposes that ADI-related stress negatively impacts brain structure, which causes stress-related overeating and subsequent obesity. 
Methods: Data were gathered from the Adolescent Brain Cognitive DevelopmentSM Study® (9-12-years-old; n=3,087, 51% male). Linear mixed-effects models identified brain regions that were associated with both ADI and BMI; longitudinal associations were evaluated with mediation models. The NI model included ADI and BMI at 9/10-years-old and brain data at 11/12-years-old. The NS model included ADI and brain data at 9/10-years-old and BMI at 11/12-years-old.  
Results: BMI at 9/10-years-old partially mediated the relationship between ADI and Ventral DC volume at 11/12-years-old. Additionally, the Ventral DC at 9/10-years-old partially mediated the relationship between ADI and BMI at 11/12-years-old, even in youth who at baseline, were of a healthy weight.  Results were unchanged when controlling for differences in brain structure and weight across the two-years. 
Conclusion: Greater area deprivation may indicate fewer access to resources that support healthy development, like nutritious food and nonstressful environments. Our findings provide evidence in support of the NI and NS theories of overeating, specifically, with greater ADI influencing health outcomes of obesity via brain structure alterations.
DOI: 10.15154/1527939
title: Joint attention in infants at high familial risk for autism spectrum disorder and the association with thalamic and hippocampal macrostructure 

description: Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder. Infants diagnosed with ASD can show impairments in spontaneous gaze-following and will seldom engage in joint attention (JA). The ability to initiate JA (IJA) can be more significantly impaired than the ability to respond to JA (RJA). In a longitudinal study, 101 infants who had a familial risk for ASD were enrolled (62% males). Participants completed magnetic resonance imaging scans at 4 or 6 months of age. Subcortical volumes (thalamus, hippocampus, amygdala, basal ganglia, ventral diencephalon, and cerebellum) were automatically extracted. Early gaze and JA behaviors were assessed with standardized measures. The majority of infants were IJA nonresponders (n = 93, 92%), and over half were RJA nonresponders (n = 50, 52%). In the nonresponder groups, models testing the association of subcortical volumes with later ASD diagnosis accounted for age, sex, and cerebral volumes. In the nonresponder IJA group, using regression method, the left hippocampus (B = -0.009, aOR = 0.991, P = 0.025), the right thalamus (B = -0.016, aOR = 0.984, P = 0.026), as well as the left thalamus (B = 0.015, aOR = 1.015, P = 0.019), predicted later ASD diagnosis. Alterations in thalamic and hippocampal macrostructure in at-risk infants who do not engage in IJA may reflect an enhanced vulnerability and may be the key predictors of later ASD development.
DOI: 10.15154/1527952
title: Intermediately Synchronised Brain States optimise trade-off between Subject Identifiability and Predictive Capacity

description: Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain
areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance
imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series
(ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co-
fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences.
However, it remains unclear to what degree different time points actually contribute to brain-behaviour
associations. Here, we systematically evaluate this question by assessing the predictive utility of FC
estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate
that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity
as well as highest predictive capacity of individual-level phenotypes.