Neurodevelopmental Effects of Prenatal Exposure to Maternal Drinking, Smoking and Adverse Psychosocial Factors: Deep Phenotyping of Infant CNS and ANS Function

产前暴露于母亲饮酒、吸烟和不良心理社会因素对神经发育的影响：婴儿 CNS 和 ANS 功能的深层表型分析

• 批准号: 10367527
• 负责人: William P. Fifer
• 金额: $ 43.12万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367527
• 关键词: 3 year old; Address; Age; Age-Months; Alcohol consumption; Alcohols; Anxiety; Artificial Intelligence; Autonomic nervous system; Birth; Child; Cognitive; Data; Data Set; Databases; Development; Documentation; Early Intervention; Early identification; Electrocardiogram; Electroencephalogram; Electrophysiology (science); Emotional; Enrollment; Ethnic group; Exposure to; Fetal Alcohol Exposure; Fetal Alcohol Spectrum Disorder; Follow-Up Studies; Funding; Geography; Goals; Grant; Infant; Infant Development; Lead; Learning; Life; Machine Learning; Measures; Mediating; Mediator of activation protein; Medical History; Mental Depression; Mental Health; Methodology; Mission; Mothers; Motor Skills; National Institute on Alcohol Abuse and Alcoholism; Neonatal; Newborn Infant; Outcome; Parents; Participant; Pattern; Perinatal; Phenotype; Physiological; Physiology; Pregnancy; Psychopathology; Psychosocial Factor; Public Health; Reporting; Research; Research Design; Research Personnel; Risk; Risk Factors; Shapes; Signal Transduction; Smoking; Socioeconomic Factors; Socioeconomic Status; South Africa; Substance abuse problem; Sudden infant death syndrome; Techniques; Testing; Toddler; Underrepresented Populations; United States; United States National Institutes of Health; alcohol exposure; alcohol risk; antepartum depression; autism spectrum disorder; base; cognitive ability; cohort; demographics; drinking; experience; fetal; follow-up; health disparity; health disparity populations; health economics; improved; infant outcome; innovation; insight; maternal alcohol use; maternal anxiety; maternal depression; maternal risk; neurobehavioral; neurodevelopment; neurodevelopmental effect; neurophysiology; northern plains; offspring; prenatal; prenatal exposure; psychiatric symptom; racial diversity; racial population; recruit; resilience; screening; secondary analysis; signal processing; smoking during pregnancy; smoking exposure; social; socioeconomics; stillbirth; substance use

PROJECT SUMMARY Alcohol use and smoking during pregnancy often occur in the context of maternal anxiety and depression. However, little is known regarding how different combinations of maternal alcohol use, smoking, and psychiatric symptoms interact to alter early central and autonomic nervous system (CNS, ANS) development and subsequent neurobehavioral outcomes in offspring. To better understand these relationships, this grant proposes to comprehensively characterize maternal alcohol, smoking, depression, and anxiety patterns during pregnancy via machine learning and artificial intelligence methodologies; to perform deep phenotyping of electrocortical and physiological data from infants; to test the overall hypothesis that physiology-derived measures mediate the relationship between maternal prenatal exposures and subsequent child neurodevelopmental outcomes. To accomplish these goals, this research proposes to use existing data from the completed Safe Passage Study conducted by the NIH funded Prenatal Alcohol in SIDS and Stillbirth (PASS) Network and from recently completed follow-up studies which enrolled subsets of the PASS participants. PASS cohort participants are from geographically and socio-economically diverse regions in South Africa and the Northern Plains of the United States. The extant PASS database comprises a detailed documentation of daily alcohol and smoking exposure and prenatal depression/anxiety assessments collected for 10,325 maternal/infant dyads and more than 1,000 variables which provide extensive information on maternal-fetal-infant medical history, demographics, and socio-economic status. These exposure data will be analyzed in relation to neonatal CNS/ANS deep phenotyping profiles derived from the analysis of 3,355 infant electroencephalograms (EEG) and electrocardiogram (ECG) data recorded either at birth or at one month of age. The extant neurobehavioral outcomes data were collected in four distinct cohorts of participants originally enrolled in PASS and followed up at 12 months (n=849) and 24-37 months of age (n=667). The follow-up study datasets contain item-level data, sub-test raw scores, and scaled-scores for the Mullen scales of early learning, the Parent Report of Children’s Abilities-Revised (PARCA-R), the Bayley scales of infant development III screening test and the Modified Checklist for Autism in Toddlers Revised with Follow up (M-CHAT-R/F). The proposed approach is expected to elucidate mechanisms of suboptimal neurodevelopment and resilience, and ultimately inform screenings for at-risk groups. Additionally, the proposed comprehensive deep phenotyping is intended to identify markers that are potential mediators of the relationship between prenatal exposures and outcomes. We anticipate that our findings will capitalize on the geocultural diversity of our cohorts and provide meaningful information associated with health disparities in underrepresented populations.

项目总结