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Abstract Background The programming of sleep architecture begins in pregnancy and depends upon optimal in utero formation and maturation of the neural connectivity of the brain. Particulate air pollution exposure can disrupt fetal brain development but associations between fine particulate matter (PM 2.5) exposure during pregnancy and child sleep outcomes have not been previously explored. Methods Analyses included 397 mother-child pairs enrolled in a pregnancy cohort in Mexico City. Daily ambient prenatal PM 2.5 exposure was estimated using a validated satellite-based spatio-temporally resolved prediction model. Child sleep periods were estimated objectively using wrist-worn, continuous actigraphy over a 1-week period at age 4–5 years. Data-driven advanced statistical methods (distributed lag models (DLMs)) were employed to identify sensitive windows whereby PM 2.5 exposure during gestation was significantly associated with changes in sleep duration or efficiency. Models were adjusted for maternal education, season, child's age, sex, and BMI z-score. Results Mother's average age was 27.7 years, with 59% having at least a high school education. Children slept an average of 7.7 h at night, with mean 80.1% efficiency. The adjusted DLM identified windows of PM 2.5 exposure between 31 and 35 weeks gestation that were significantly associated with decreased sleep duration in children. In addition, increased PM 2.5 during weeks 1–8 was associated with decreased sleep efficiency. In other exposure windows (weeks 39–40), PM 2.5 was associated with increased sleep duration. Conclusion Prenatal PM 2.5 exposure is associated with altered sleep in preschool-aged children in Mexico City. Pollutant exposure during sensitive windows of pregnancy may have critical influence upon sleep programming. Highlights • Prenatal PM 2.5 exposure was associated with sleep disruption in preschoolers. • Higher PM 2.5 was linked to decreased sleep efficiency and altered sleep duration. • Significant associations were identified during sensitive windows of gestation. [ABSTRACT FROM AUTHOR]

In 2009, the National Institute of Mental Health launched the Research Domain Criteria, an attempt to move beyond diagnostic categories and ground psychiatry within neurobiological constructs that combine different levels of measures (e.g., brain imaging and behavior). Statistical methods that can integrate such multimodal data, however, are often vulnerable to overfitting, poor generalization, and difficulties in interpreting the results. We propose an innovative machine learning framework combining multiple holdouts and a stability criterion with regularized multivariate techniques, such as sparse partial least squares and kernel canonical correlation analysis, for identifying hidden dimensions of cross-modality relationships. To illustrate the approach, we investigated structural brain–behavior associations in an extensively phenotyped developmental sample of 345 participants (312 healthy and 33 with clinical depression). The brain data consisted of whole-brain voxel-based gray matter volumes, and the behavioral data included item-level self-report questionnaires and IQ and demographic measures. Both sparse partial least squares and kernel canonical correlation analysis captured two hidden dimensions of brain–behavior relationships: one related to age and drinking and the other one related to depression. The applied machine learning framework indicates that these results are stable and generalize well to new data. Indeed, the identified brain–behavior associations are in agreement with previous findings in the literature concerning age, alcohol use, and depression-related changes in brain volume. Multivariate techniques (such as sparse partial least squares and kernel canonical correlation analysis) embedded in our novel framework are promising tools to link behavior and/or symptoms to neurobiology and thus have great potential to contribute to a biologically grounded definition of psychiatric disorders. [ABSTRACT FROM AUTHOR]