Cumulative Effects of Resting-State Connectivity Across All Brain Networks Significantly Correlate with Attention-Deficit Hyperactivity Disorder Symptoms.

Identification of replicable neuroimaging correlates of attention-deficit hyperactivity disorder (ADHD) has been hindered by small sample sizes, small effects, and heterogeneity of methods. Given evidence that ADHD is associated with alterations in widely distributed brain networks and the small effects of individual brain features, a whole-brain perspective focusing on cumulative effects is warranted. The use of large, multisite samples is crucial for improving reproducibility and clinical utility of brain-wide MRI association studies. To address this, a polyneuro risk score (PNRS) representing cumulative, brain-wide, ADHD-associated resting-state functional connectivity was constructed and validated using data from the Adolescent Brain Cognitive Development (ABCD, N = 5,543, 51.5% female) study, and was further tested in the independent Oregon-ADHD-1000 case-control cohort (N = 553, 37.4% female). The ADHD PNRS was significantly associated with ADHD symptoms in both cohorts after accounting for relevant covariates (p < 0.001). The most predictive PNRS involved all brain networks, though the strongest effects were concentrated among the default mode and cingulo-opercular networks. In the longitudinal Oregon-ADHD-1000, non-ADHD youth had significantly lower PNRS (Cohen's d = -0.318, robust p = 5.5 × 10-4) than those with persistent ADHD (age 7-19). The PNRS, however, did not mediate polygenic risk for ADHD. Brain-wide connectivity was robustly associated with ADHD symptoms in two independent cohorts, providing further evidence of widespread dysconnectivity in ADHD. Evaluation in enriched samples demonstrates the promise of the PNRS approach for improving reproducibility in neuroimaging studies and unraveling the complex relationships between brain connectivity and behavioral disorders.

The association between duration of breastfeeding and childhood asthma outcomes.

BACKGROUND: Postnatal exposures, including breastfeeding, may influence asthma development. OBJECTIVE: To investigate the association between breastfeeding duration and child asthma. METHODS: We studied 2021 mother-child dyads in the ECHO PATHWAYS consortium of prospective pregnancy cohorts (GAPPS, CANDLE, TIDES). Women reported the duration of any and exclusive breastfeeding and child asthma outcomes during follow-up at child age 4 to 6 years. Outcomes included current wheeze (previous 12 months), ever asthma, current asthma (having ≥2 of current wheeze, ever asthma, medication use in past 12-24 months), and strict current asthma (ever asthma with either or both current wheeze and medication use in past 12-24 months). We used multivariable logistic regression to assess associations (odds ratios and 95% confidence intervals) between breastfeeding and asthma outcomes adjusting for potential confounders. We assessed effect modification by mode of delivery, infant sex, and maternal asthma. RESULTS: Among women, 33%, 13%, 9%, and 45% reported 0 to less than 2, 2 to 4, 5 to 6, and more than 6 months of any breastfeeding, respectively. The duration of any breastfeeding had a protective linear trend with ever asthma but no other outcomes. There was a duration-dependent protective association of exclusive breastfeeding and child asthma outcomes (eg, current asthma adjusted odds ratio [95% confidence interval], 0.64 [0.41-1.02], 0.61 [0.38-0.98], and 0.52 (0.31-0.87) for 2to 4 months, 5 to 6 months, and more than 6 months, respectively, compared with <2 months). For exclusive breastfeeding, protective associations were stronger in dyads with children born by vaginal vs cesarean delivery although interactions did not reach statistical significance (Pinteractions 0.12-0.40). CONCLUSION: Longer duration of exclusive breastfeeding had a protective association with child asthma.

Leveraging Distributed Brain Signal at Rest to Predict Internalizing Symptoms in Youth: Deriving a Polyneuro Risk Score From the ABCD Study Cohort.

BACKGROUND: The prevalence of internalizing psychopathology rises precipitously from early to mid-adolescence, yet the underlying neural phenotypes that give rise to depression and anxiety during this developmental period remain unclear. METHODS: Youths from the Adolescent Brain Cognitive Development (ABCD) Study (ages 9-10 years at baseline) with a resting-state functional magnetic resonance imaging scan and mental health data were eligible for inclusion. Internalizing subscale scores from the Brief Problem Monitor-Youth Form were combined across 2 years of follow-up to generate a cumulative measure of internalizing symptoms. The total sample (N = 6521) was split into a large discovery dataset and a smaller validation dataset. Brain-behavior associations of resting-state functional connectivity with internalizing symptoms were estimated in the discovery dataset. The weighted contributions of each functional connection were aggregated using multivariate statistics to generate a polyneuro risk score (PNRS). The predictive power of the PNRS was evaluated in the validation dataset. RESULTS: The PNRS explained 10.73% of the observed variance in internalizing symptom scores in the validation dataset. Model performance peaked when the top 2% functional connections identified in the discovery dataset (ranked by absolute ß weight) were retained. The resting-state functional connectivity networks that were implicated most prominently were the default mode, dorsal attention, and cingulo-parietal networks. These findings were significant (p < 1 × 10-6) as accounted for by permutation testing (n = 7000). CONCLUSIONS: These results suggest that the neural phenotype associated with internalizing symptoms during adolescence is functionally distributed. The PNRS approach is a novel method for capturing relationships between resting-state functional connectivity and behavior.

Neural Circuit Markers of Familial Risk for Depression Among Healthy Youths in the Adolescent Brain Cognitive Development Study.

BACKGROUND: Family history of depression is a robust predictor of early-onset depression, which may confer risk through alterations in neural circuits that have been implicated in reward and emotional processing. These alterations may be evident in youths who are at familial risk for depression but who do not currently have depression. However, the identification of robust and replicable findings has been hindered by few studies and small sample sizes. In the current study, we sought to identify functional connectivity (FC) patterns associated with familial risk for depression. METHODS: Participants included healthy (i.e., no lifetime psychiatric diagnoses) youths at high familial risk for depression (HR) (n = 754; at least one parent with a history of depression) and healthy youths at low familial risk for psychiatric problems (LR) (n = 1745; no parental history of psychopathology) who were 9 to 10 years of age and from the Adolescent Brain Cognitive Development (ABCD) Study sample. We conducted whole-brain seed-to-voxel analyses to examine group differences in resting-state FC with the amygdala, caudate, nucleus accumbens, and putamen. We hypothesized that HR youths would exhibit global amygdala hyperconnectivity and striatal hypoconnectivity patterns primarily driven by maternal risk. RESULTS: HR youths exhibited weaker caudate-angular gyrus FC than LR youths (α = 0.04, Cohen's d = 0.17). HR youths with a history of maternal depression specifically exhibited weaker caudate-angular gyrus FC (α = 0.03, Cohen's d = 0.19) as well as weaker caudate-dorsolateral prefrontal cortex FC (α = 0.04, Cohen's d = 0.21) than LR youths. CONCLUSIONS: Weaker striatal connectivity may be related to heightened familial risk for depression, primarily driven by maternal history. Identifying brain-based markers of depression risk in youths can inform approaches to improving early detection, diagnosis, and treatment.

Familial risk for depression moderates neural circuitry in healthy preadolescents to predict adolescent depression symptoms in the Adolescent Brain Cognitive Development (ABCD) Study.

BACKGROUND: There is an imminent need to identify neural markers during preadolescence that are linked to developing depression during adolescence, especially among youth at elevated familial risk. However, longitudinal studies remain scarce and exhibit mixed findings. Here we aimed to elucidate functional connectivity (FC) patterns among preadolescents that interact with familial depression risk to predict depression two years later. METHODS: 9-10 year-olds in the Adolescent Brain Cognitive Development (ABCD) Study were classified as healthy (i.e., no lifetime psychiatric diagnoses) at high familial risk for depression (HR; n=559) or at low familial risk for psychopathology (LR; n=1203). Whole-brain seed-to-voxel resting-state FC patterns with the amygdala, putamen, nucleus accumbens, and caudate were calculated. Multi-level, mixed-effects regression analyses were conducted to test whether FC at ages 9-10 interacted with familial risk to predict depression symptoms at ages 11-12. RESULTS: HR youth demonstrated stronger associations between preadolescent FC and adolescent depression symptoms (ps<0.001) as compared to LR youth (ps>0.001), primarily among amygdala/striatal FC with visual and sensory/somatomotor networks. CONCLUSIONS: Preadolescent amygdala and striatal FC may be useful biomarkers of adolescent-onset depression, particularly for youth with family histories of depression. This research may point to neurobiologically-informed approaches to prevention and intervention for depression in adolescents.

Multi-level fMRI analysis applied to hemispheric specialization in the language network, functional areas, and their behavioral correlations in the ABCD sample.

Prior research suggests that the organization of the language network in the brain is left-dominant and becomes more lateralized with age and increasing language skill. The age at which specific components of the language network become adult-like varies depending on the abilities they subserve. So far, a large, developmental study has not included a language task paradigm, so we introduce a method to study resting-state laterality in the Adolescent Brain Cognitive Development (ABCD) study. Our approach mixes source timeseries between left and right homotopes of the (1) inferior frontal and (2) middle temporal gyri and (3) a region we term "Wernicke's area" near the supramarginal gyrus. Our large subset sample size of ABCD (n = 6153) allows improved reliability and validity compared to previous, smaller studies of brain-behavior associations. We show that behavioral metrics from the NIH Youth Toolbox and other resources are differentially related to tasks with a larger linguistic component over ones with less (e.g., executive function-dominant tasks). These baseline characteristics of hemispheric specialization in youth are critical for future work determining the correspondence of lateralization with language onset in earlier stages of development.

A precision functional atlas of personalized network topography and probabilities.

Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

Polyneuro risk scores capture widely distributed connectivity patterns of cognition.

Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework's ability to reliably capture brain-behavior relationships across 3 cognitive scores - general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.

Personalized functional brain network topography is associated with individual differences in youth cognition.

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain's functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex's sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

Prenatal exposure to particulate matter and placental gene expression.

BACKGROUND: While strong evidence supports adverse maternal and offspring consequences of air pollution, mechanisms that involve the placenta, a key part of the intrauterine environment, are largely unknown. Previous studies of air pollution and placental gene expression were small candidate gene studies that rarely considered prenatal windows of exposure or the potential role of offspring sex. We examined overall and sex-specific associations of prenatal exposure to fine particulate matter (PM2.5) with genome-wide placental gene expression. METHODS: Participants with placenta samples, collected at birth, and childhood health outcomes from CANDLE (Memphis, TN) (n = 776) and GAPPS (Seattle, WA) (n = 205) cohorts of the ECHO-PATHWAYS Consortium were included in this study. PM2.5 exposures during trimesters 1, 2, 3, and the first and last months of pregnancy, were estimated using a spatiotemporal model. Cohort-specific linear adjusted models were fit for each exposure window and expression of >11,000 protein coding genes from paired end RNA sequencing data. Models with interaction terms were used to examine PM2.5-offspring sex interactions. False discovery rate (FDR < 0.10) was used to correct for multiple testing. RESULTS: Mean PM2.5 estimate was 10.5-10.7 µg/m3 for CANDLE and 6.0-6.3 µg/m3 for GAPPS participants. In CANDLE, expression of 13 (11 upregulated and 2 downregulated), 20 (11 upregulated and 9 downregulated) and 3 (2 upregulated and 1 downregulated) genes was associated with PM2.5 in the first trimester, second trimester, and first month, respectively. While we did not find any statistically significant association, overall, between PM2.5 and gene expression in GAPPS, we found offspring sex and first month PM2.5 interaction for DDHD1 expression (positive association among males and inverse association among females). We did not observe PM2.5 and offspring sex interactions in CANDLE. CONCLUSION: In CANDLE, but not GAPPS, we found that prenatal PM2.5 exposure during the first half of pregnancy is associated with placental gene expression.

Cohort profile: the ECHO prenatal and early childhood pathways to health consortium (ECHO-PATHWAYS).

PURPOSE: Exposures early in life, beginning in utero, have long-term impacts on mental and physical health. The ECHO prenatal and early childhood pathways to health consortium (ECHO-PATHWAYS) was established to examine the independent and combined impact of pregnancy and childhood chemical exposures and psychosocial stressors on child neurodevelopment and airway health, as well as the placental mechanisms underlying these associations. PARTICIPANTS: The ECHO-PATHWAYS consortium harmonises extant data from 2684 mother-child dyads in three pregnancy cohort studies (CANDLE [Conditions Affecting Neurocognitive Development and Learning in Early Childhood], TIDES [The Infant Development and Environment Study] and GAPPS [Global Alliance to Prevent Prematurity and Stillbirth]) and collects prospective data under a unified protocol. Study participants are socioeconomically diverse and include a large proportion of Black families (38% Black and 51% White), often under-represented in research. Children are currently 5-15 years old. New data collection includes multimodal assessments of primary outcomes (airway health and neurodevelopment) and exposures (air pollution, phthalates and psychosocial stress) as well as rich covariate characterisation. ECHO-PATHWAYS is compiling extant and new biospecimens in a central biorepository and generating the largest placental transcriptomics data set to date (N=1083). FINDINGS TO DATE: Early analyses demonstrate adverse associations of prenatal exposure to air pollution, phthalates and maternal stress with early childhood airway outcomes and neurodevelopment. Placental transcriptomics work suggests that phthalate exposure alters placental gene expression, pointing to mechanistic pathways for the developmental toxicity of phthalates. We also observe associations between prenatal maternal stress and placental corticotropin releasing hormone, a marker of hormonal activation during pregnancy relevant for child health. Other publications describe novel methods for examining exposure mixtures and the development of a national spatiotemporal model of ambient outdoor air pollution. FUTURE PLANS: The first wave of data from the unified protocol (child age 8-9) is nearly complete. Future work will leverage these data to examine the combined impact of early life social and chemical exposures on middle childhood health outcomes and underlying placental mechanisms.