Grant: CIHR Fellowship
Direct Payment
$150,000.00
Unravelling early life nutritional influences on child brain development: An MRI and machine learning approach
EF 2023-24
Résumé
Brain development begins during early pregnancy and peaks during the first 1000 days of life. This particularly sensitive period is influenced by nature (genetics), as well as nurture (environment, nutrition, lifestyle, and other factors). Among nurture, maternal nutrient intake during pre- and post-natal periods is known to be important for infant brain health. Proper brain development during this stage sets the foundation for children's cognitive, behavioral, and learning skills, which ultimately underlie later academic success. This proposal will use a multidisciplinary approach to answer the important question: how are brain growth and subsequent neurocognitive outcomes influenced by early nutrition? Using a unique dataset from 145 mother-infant pairs, we aim to identify associations between maternal nutrition during lactation via >70 nutrients consumed by mothers and brain MRI performed during infancy. Using advanced neuroimaging analysis, we will identify brain MRI patterns associated with maternal nutrient status to determine which combinations of nutrients most impact brain growth. Next, we will use a large-scale cohort dataset (>41 000 participants) with information on maternal diet and nutrient status during pregnancy alongside child neurodevelopmental (cognitive, social, and behavioral) measures. Using machine learning and multivariate analysis techniques, we will identify combinations of nutrients that are most predictive of neurodevelopmental outcomes. As past research mainly focuses on preterm-born children or single nutrient effects, this study fills a distinct gap in the literature to study term-born children and combinations of nutrients that best promote brain development. These results will provide the foundation for developing an evidence-based dietary approach for prospective mothers to promote improved child neurodevelopmental outcomes, broadly contributing to the societal advancement of Canada's next generation of youth.
Programme
CIHR Fellowship
Bénéficiaire
Emplacement
Massachusetts, United States of America
Type
Grant
Source
CIHR Funding Decisions Database
Tel que publié
Id
492359
Name
Perrier Stefanie E
Region
Massachusetts
Country
United States of America
Orgtype
Research Institution
Instname2
Human Development, Child and Youth Health
Theme2
Biomedical
Prcname2
Fellowships - Post-PhD
Projecttitle
Unravelling early life nutritional influences on child brain development: An MRI and machine learning approach
Programtype2
Fellowship Programs
Orgnameinp2
Direct Payment
Cihramount2
$150,000
Cihrequipment2
$0
Programname2
CIHR Fellowship
Keyworddelim
Child Brain Development; Machine Learning; Magnetic Resonance Imaging; Neurodevelopment; Neuroimaging; Nutrition
Abstract
Brain development begins during early pregnancy and peaks during the first 1000 days of life. This particularly sensitive period is influenced by nature (genetics), as well as nurture (environment, nutrition, lifestyle, and other factors). Among nurture, maternal nutrient intake during pre- and post-natal periods is known to be important for infant brain health. Proper brain development during this stage sets the foundation for children's cognitive, behavioral, and learning skills, which ultimately underlie later academic success. This proposal will use a multidisciplinary approach to answer the important question: how are brain growth and subsequent neurocognitive outcomes influenced by early nutrition? Using a unique dataset from 145 mother-infant pairs, we aim to identify associations between maternal nutrition during lactation via >70 nutrients consumed by mothers and brain MRI performed during infancy. Using advanced neuroimaging analysis, we will identify brain MRI patterns associated with maternal nutrient status to determine which combinations of nutrients most impact brain growth. Next, we will use a large-scale cohort dataset (>41 000 participants) with information on maternal diet and nutrient status during pregnancy alongside child neurodevelopmental (cognitive, social, and behavioral) measures. Using machine learning and multivariate analysis techniques, we will identify combinations of nutrients that are most predictive of neurodevelopmental outcomes. As past research mainly focuses on preterm-born children or single nutrient effects, this study fills a distinct gap in the literature to study term-born children and combinations of nutrients that best promote brain development. These results will provide the foundation for developing an evidence-based dietary approach for prospective mothers to promote improved child neurodevelopmental outcomes, broadly contributing to the societal advancement of Canada's next generation of youth.
Competitiondate
202310
Approvedterm2
3 yrs 0 mth
Pinamesdelim
Perrier, Stefanie E
Approvedterm
3 ans 0 mois
Cihrcontribution2
~ $150,000~ $0~