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Childhood exposure to social disadvantage is a major risk factor for psychiatric disorders and poor developmental, educational, and occupational outcomes, presumably because adverse exposures alter the neurodevelopmental processes that contribute to risk trajectories. Yet, given the limited social mobility in the United States and other countries, childhood social disadvantage is frequently preceded by maternal social disadvantage during pregnancy, potentially altering fetal brain development during a period of high neuroplasticity through hormonal, microbiome, epigenetic, and immune factors that cross the placenta and fetal blood-brain barrier. The current study examines prenatal social disadvantage to determine whether these exposures in utero are associated with alterations in functional brain networks as early as birth. As part of the Early Life Adversity and Biological Embedding study, mothers were recruited during pregnancy, prenatal social disadvantage was assessed across trimesters, and their healthy, full-term offspring were imaged using resting-state functional magnetic resonance imaging during the first weeks of life. Multivariate machine learning methods revealed that neonatal functional connectivity (FC) varied as a function of prenatal exposure to social disadvantage (n = 261, R = 0.43, R 2 = 0.18), with validation in an independent sample. Alterations in FC associated with prenatal social disadvantage occurred brain-wide and were most pronounced in association networks (fronto-parietal, ventral attention, dorsal attention) and the somatomotor network. Amygdala FC was altered at birth, with a pattern shared across subcortical structures. These findings provide critical insights into how early in development functional networks begin to diverge in the context of social disadvantage and elucidate the functional networks that are most impacted., Competing Interests: Competing interests statement:M.E.R. and D.S. (one of the reviewers) are on a commentary in 2023 as part of two large consortium.

Objective: To investigate whether parenting or neonatal brain volumes mediate associations between prenatal social disadvantage (PSD) and cognitive/language abilities and whether these mechanisms vary by level of disadvantage., Study Design: Pregnant women were recruited prospectively from obstetric clinics in St Louis, Missouri. PSD encompassed access to social (eg, education) and material (eg, income to needs, health insurance, area deprivation, and nutrition) resources during pregnancy. Neonates underwent brain magnetic resonance imaging. Mother-child dyads (n = 202) returned at age 1 year for parenting observations and at age 2 years for cognition/language assessments (Bayley Scales of Infant and Toddler Development, Third Edition). Generalized additive and mediation models tested hypotheses., Results: Greater PSD associated nonlinearly with poorer cognitive/language scores. Associations between parenting and cognition/language were moderated by disadvantage, such that supportive and nonsupportive parenting behaviors related only to cognition/language in children with lesser PSD. Parenting mediation effects differed by level of disadvantage: both supportive and nonsupportive parenting mediated PSD-cognition/language associations in children with lesser disadvantage, but not in children with greater disadvantage. PSD-associated reductions in neonatal subcortical grey matter (β = 0.19; q = 0.03), white matter (β = 0.23; q = 0.02), and total brain volume (β = 0.18; q = 0.03) were associated with lower cognition, but did not mediate the associations between PSD and cognition., Conclusions: Parenting moderates and mediates associations between PSD and early cognition and language, but only in families with less social disadvantage. These findings, although correlational, suggest that there may be a critical threshold of disadvantage, below which mediating or moderating factors become less effective, highlighting the importance of reducing disadvantage as primary prevention., Competing Interests: Declaration of Competing Interest This study was funded by R01MH113883, K01MH122735, T32NS121881, and T32 MH100019 from the NIH, the March of Dimes Foundation, grant MI-II-2018-725 from the Children's Discovery Institute, grant P50 HD103525 from the Washington University Intellectual and Developmental Disability Research Center, and National Alliance for Research on Schizophrenia & Depression Young Investigator Grant 28521 from the Brain and Behavior Research Foundation, and grant KL2 TR00234. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Adult cortex is organized into distributed functional communities. Yet, little is known about community architecture of children's brains. Here, we uncovered the community structure of cortex in childhood using fMRI data from 670 children aged 9-11 years (48% female, replication sample n=544, 56% female) from the Adolescent Brain and Cognitive Development study. We first applied a data-driven community detection approach to cluster cortical regions into communities, then employed a generative model-based approach called the weighted stochastic block model to further probe community interactions. Children showed similar community structure to adults, as defined by Yeo and colleagues in 2011, in early-developing sensory and motor communities, but differences emerged in transmodal areas. Children have more cortical territory in the limbic community, which is involved in emotion processing, than adults. Regions in association cortex interact more flexibly across communities, creating uncertainty for the model-based assignment algorithm, and perhaps reflecting cortical boundaries that are not yet solidified. Uncertainty was highest for cingulo-opercular areas involved in flexible deployment of cognitive control. Activation and deactivation patterns during a working memory task showed that both the data-driven approach and a set of adult communities statistically capture functional organization in middle childhood. Collectively, our findings suggest that community boundaries are not solidified by middle childhood., Competing Interests: Declaration of Competing Interest The authors declare no competing financial interests., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2021. Published by Elsevier Inc.)