We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

BACKGROUND AND PURPOSE:The hippocampus is a frequent focus of quantitative neuroimaging research, and structural hippocampal alterations are related to multiple neurocognitive disorders. An increasing number of neuroimaging studies are focusing on hippocampal subfield regional involvement in these disorders using various automated segmentation approaches. Direct comparisons among these approaches are limited. The purpose of this study was to compare the agreement between two automated hippocampal segmentation algorithms in an adult population. MATERIALS AND METHODS:We compared the results of 2 automated segmentation algorithms for hippocampal subfields (FreeSurfer v6.0 and volBrain) within a single imaging data set from adults (n ¼ 176, 89 women) across a wide age range (20-79 years). Brain MR imaging was acquired on a single 3T scanner as part of the IXI Brain Development Dataset and included T1-and T2-weighted MR images. We also examined subfield volumetric differences related to age and sex and the impact of different intracranial volume and total hippocampal volume normalization methods.RESULTS: Estimated intracranial volume and total hippocampal volume of both protocols were strongly correlated (r ¼ 0.93 and 0.9, respectively; both P , .001). Hippocampal subfield volumes were correlated (ranging from r ¼ 0.42 for the subiculum to r ¼ 0.78 for the cornu ammonis [CA]1, all P , .001). However, absolute volumes were significantly different between protocols. volBrain produced larger CA1 and CA4-dentate gyrus and smaller CA2-CA3 and subiculum volumes compared with FreeSurfer v6.0. Regional age-and sex-related differences in subfield volumes were qualitatively and quantitatively different depending on segmentation protocol and intracranial volume/total hippocampal volume normalization method. CONCLUSIONS:The hippocampal subfield volume relationship to demographic factors and disease states should undergo nuanced interpretation, especially when considering different segmentation protocols. ABBREVIATIONS: CA ¼ cornu ammonis; DG ¼ dentate gyrus; HPSF ¼ hippocampal subfield; ICV ¼ intracranial volume; SR-SL-SM ¼ strata radiatum-lacunosum-moleculare; THV ¼ total hippocampal volume T he hippocampus is a major component of the limbic system, and it is affected in several neurocognitive and neuropsychiatric disorders from Alzheimer disease to major depressive disorder. 1,2 Located in the mesial temporal lobes, the hippocampus functions as a computational hub through its extensive afferent and efferent connections with cortical and subcortical structures. 3 The hippocampus and hippocampal-related structures sustain a range of cognitive functions in the context of episodic and semantic memory, spatial navigation, planning, and learning. 4 The hippocampus is additionally divided into distinct cytoarchitectonic regions called subfields, most prominently the dentate gyrus (DG), cornu ammonis (CA) subfields 1-4, and the subiculum. 5 Distinctive cognitive functions are supported by different subfields, 6 and subfields are differ...

Introduction Childhood obesity is increasingly prevalent and confers elevated risk of developing medical complications across the lifespan. Given the brain's prominent involvement in homeostatic and hedonic eating, it is crucial to understand brain health in obesity. Recent studies using quantitative T2-weighted MRI and diffusion-weighted MRI showed obesity-related putative neuroinflammation in human brain. To assess convergent validity across diffusion-based MRI techniques and extend these findings, this study characterized tissue microstructure of white matter (WM) tracts and energy regulation and reward processing brain regions in children across levels of obesity-related measures, using diffusion basis spectrum imaging (DBSI). Methods Using data from the Adolescent Brain Cognitive Development Study (ABCD Study®), DBSI metrics indicative of putative neuroinflammation were computed for WM tracts, hypothalamus, nucleus accumbens, caudate nucleus, and putamen. DBSI metrics were compared between children with normal-weight vs. obesity. DBSI metrics were also correlated to baseline and one- and two-year change in continuous obesity-related measures (waist circumference, BMI, and BMI z-scores). Striatal DBSI findings were compared to those of restriction spectrum imaging (RSI). Results A total of 263 nine- and ten-year old children from the ABCD Study® 2.0.1 data release (5 underweight; 126 with normal-weight; 64 with over-weight; 68 with obesity) who met inclusion and exclusion criteria were randomly selected. Relative to children with normal-weight, children with obesity had lower DBSI fiber fraction (FF; reflects apparent axonal/dendritic density) and higher DBSI restricted fraction (RF; reflects cellularity) in WM tracts throughout the brain (voxel-wise FWE-corrected p < 0.05), as well as higher DBSI-RF in the hypothalamus, nucleus accumbens, and caudate nucleus (Cohen's d's ≥ 0.28, p's ≤ 0.05). Across all children, greater baseline waist circumference was related to higher DBSI-RF in the hypothalamus (standardized β = 0.17, p = 0.0033), nucleus accumbens (standardized β = 0.21, p = 0.0001), and caudate nucleus (standardized β = 0.14, p = 0.013). Gain in waist circumference over one and two years related to higher baseline DBSI-RF in nucleus accumbens (standardized β = 0.12, p = 0.08) and in hypothalamus (standardized β = 0.15, p = 0.03), respectively. Overall, results were consistent for BMI and BMI z-scores. Similar results were observed using RSI metrics. Conclusion Novel findings include demonstration that, as in adults, childhood obesity is associated with DBSI-assessed putative neuroinflammation in WM tracts and hypothalamus. In addition, our results support the reproducibility of previous findings that MRI-assessed putative neuroinflammation in striatum and hypothalamus is related to obesity in children, contributing to a growing understanding of the role of adiposity in brain health across the lifespan.

ImportanceLower neighborhood and household socioeconomic status (SES) are associated with negative health outcomes and altered brain structure in children. It is unclear whether such findings extend to white matter and via what mechanisms.ObjectiveTo assess whether and how neighborhood and household SES are independently associated with children’s white matter microstructure and examine whether obesity and cognitive performance (reflecting environmental cognitive and sensory stimulation) are plausible mediators.Design, Setting, and ParticipantsThis cross-sectional study used baseline data from participants in the Adolescent Brain Cognitive Development (ABCD) study. Data were collected at 21 US sites, and school-based recruitment was used to represent the US population. Children aged 9 to 11 years and their parents or caregivers completed assessments between October 1, 2016, and October 31, 2018. After exclusions, 8842 of 11 875 children in the ABCD study were included in the analyses. Data analysis was conducted from July 11 to December 19, 2022.ExposuresNeighborhood disadvantage was derived from area deprivation indices at participants’ primary residence. Household SES factors were total income and highest parental educational attainment.Main Outcomes and MeasuresA restriction spectrum imaging (RSI) model was used to quantify restricted normalized directional (RND; reflecting oriented myelin organization) and restricted normalized isotropic (RNI; reflecting glial and neuronal cell bodies) diffusion in 31 major white matter tracts. The RSI measurements were scanner harmonized. Obesity was assessed through body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), age- and sex-adjusted BMI z scores, and waist circumference, and cognition was assessed through the National Institutes of Health Toolbox Cognition Battery. Analyses were adjusted for age, sex, pubertal development stage, intracranial volume, mean head motion, and twin or siblingship.ResultsAmong 8842 children, 4543 (51.4%) were boys, and the mean (SD) age was 9.9 (0.7) years. Linear mixed-effects models revealed that greater neighborhood disadvantage was associated with lower RSI-RND in the left superior longitudinal fasciculus (β = −0.055; 95% CI, −0.081 to −0.028) and forceps major (β = −0.040; 95% CI, −0.067 to −0.013). Lower parental educational attainment was associated with lower RSI-RND in the bilateral superior longitudinal fasciculus (eg, right hemisphere: β = 0.053; 95% CI, 0.025-0.080) and bilateral corticospinal or pyramidal tract (eg, right hemisphere: β = 0.042; 95% CI, 0.015-0.069). Structural equation models revealed that lower cognitive performance (eg, lower total cognition score and higher neighborhood disadvantage: β = −0.012; 95% CI, −0.016 to −0.009) and greater obesity (eg, higher BMI and higher neighborhood disadvantage: β = −0.004; 95% CI, −0.006 to −0.001) partially accounted for the associations between SES and RSI-RND. Lower household income was associated with higher RSI-RNI in most tracts (eg, right inferior longitudinal fasciculus: β = −0.042 [95% CI, −0.073 to −0.012]; right anterior thalamic radiations: β = −0.045 [95% CI, −0.075 to −0.014]), and greater neighborhood disadvantage had similar associations in primarily frontolimbic tracts (eg, right fornix: β = 0.046 [95% CI, 0.019-0.074]; right anterior thalamic radiations: β = 0.045 [95% CI, 0.018-0.072]). Lower parental educational attainment was associated with higher RSI-RNI in the forceps major (β = −0.048; 95% CI, −0.077 to −0.020). Greater obesity partially accounted for these SES associations with RSI-RNI (eg, higher BMI and higher neighborhood disadvantage: β = 0.015; 95% CI, 0.011-0.020). Findings were robust in sensitivity analyses and were corroborated using diffusion tensor imaging.Conclusions and RelevanceIn this cross-sectional study, both neighborhood and household contexts were associated with white matter development in children, and findings suggested that obesity and cognitive performance were possible mediators in these associations. Future research on children’s brain health may benefit from considering these factors from multiple socioeconomic perspectives.

Importance Both neighborhood and household socioeconomic disadvantage relate to negative health outcomes and altered brain structure in children. It is unclear whether such findings extend to white matter development, and via what mechanisms socioeconomic status (SES) influences the brain. Objective To test independent associations between neighborhood and household SES indicators and white matter microstructure in children, and examine whether body mass index and cognitive function (a proxy of environmental cognitive/sensory stimulation) may plausibly mediate these associations. Design This cross-sectional study used baseline data from the Adolescent Brain Cognitive Development (ABCD) Study, an ongoing 10-year cohort study tracking child health. Setting School-based recruitment at 21 U.S. sites. Participants Children aged 9 to 11 years and their parents/caregivers completed baseline assessments between October 1st, 2016 and October 31st, 2018. Data analysis was conducted from July to December 2022. Exposures Neighborhood disadvantage was derived from area deprivation indices at primary residence. Household SES indicators were total income and the highest parental education attainment. Main Outcomes and Measures Thirty-one major white matter tracts were segmented from diffusion-weighted images. The Restriction Spectrum Imaging (RSI) model was implemented to measure restricted normalized directional (RND; reflecting oriented myelin organization) and isotropic (RNI; reflecting glial/neuronal cell bodies) diffusion in each tract. Obesity-related measures were body mass index (BMI), BMI z-scores, and waist circumference, and cognitive performance was assessed using the NIH Toolbox Cognition Battery. Linear mixed-effects models tested the associations between SES indicators and scanner-harmonized RSI metrics. Structural equation models examined indirect effects of obesity and cognitive performance in the significant associations between SES and white mater microstructure summary principal components. Analyses were adjusted for age, sex, pubertal development stage, intracranial volume, and head motion. Results The analytical sample included 8842 children (4299 [48.6%] girls; mean age [SD], 9.9 [0.7] years). Greater neighborhood disadvantage and lower parental education were independently associated with lower RSI-RND in forceps major and corticospinal/pyramidal tracts, and had overlapping associations in the superior longitudinal fasciculus. Lower cognition scores and greater obesity-related measures partially accounted for these SES associations with RSI-RND. Lower household income was related to higher RSI-RNI in almost every tract, and greater neighborhood disadvantage had similar effects in primarily frontolimbic tracts. Lower parental education was uniquely linked to higher RSI-RNI in forceps major. Greater obesity-related measures partially accounted for these SES associations with RSI-RNI. Findings were robust in sensitivity analyses and mostly corroborated using traditional diffusion tensor imaging (DTI). Conclusions and Relevance These cross-sectional results demonstrate that both neighborhood and household contexts are relevant to white matter development in children, and suggest cognitive performance and obesity as possible pathways of influence. Interventions targeting obesity reduction and improving cognition from multiple socioeconomic angles may ameliorate brain health in low-SES children.