Predicting long-term outcomes in preterm infants using multimodal neuroimaging techniques and environmental factors
Predicting long-term outcomes in preterm infants using multimodal neuroimaging techniques and environmental factors
批准号:
10681464
负责人:
Elveda Gozdas
金额:
$13.21万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-08-10 至 2025-01-31
关键词:
2 year oldAdolescenceAdultAffectAgeArchitectureAttentionBehavioralBiologicalBiophysicsBirthBrainBrain regionCognitiveDataDevelopmentDiffusion Magnetic Resonance ImagingEarly InterventionEducationEnvironmental Risk FactorEpidemicGoalsGrainHealthcareHouseholdHumanImaging TechniquesIncomeInfantInterventionKnowledgeLanguageLifeLife Cycle StagesLinkMagnetic Resonance ImagingMapsMeasuresMental disordersMethodsModelingMolecularMorphologyMotorNeonatalNeuritesNeurocognitiveNeurodevelopmental ImpairmentNeurodevelopmental ProblemOccupationsOutcomeParental AgesPatternPlayPredictive FactorPremature BirthPremature InfantProcessPropertyResearchRoleShapesStructural defectStructureSurvivorsSystemTechniquesTestingTimeTissuesbrain tissuecognitive functionconnectome based predictive modelingdesigndiagnostic toolearly detection biomarkerseffective therapyexperiencefamily structuregray matterhigh riskimaging biomarkerimprovedinsightmaternal depressionmultimodal neuroimagingneonatal brainneonatal periodnervous system disordernetwork architectureneuroimagingnovelnovel strategiespredictive markerpredictive modelingprematurepreventive interventionquantitative imagingwhite matter
中文摘要
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英文摘要
Project Summary/Abstract
The outlined proposal expands the previous research in preterm infant brains by integrating multiple advanced
neuroimaging methods, including novel quantitative magnetic resonance imaging (MRI) techniques and
environmental factors to predict long-term neurodevelopmental issues. Preterm infants have a high rate of
long-term neurodevelopmental impairments that often require additional health care and intervention. Recent
advances in neuroimaging have provided great insight into the patterns of specific alterations in preterm
infants. However, the studies are limited to demonstrate how brain function and structure and their interaction
relate to or explain later neurodevelopmental outcomes in preterm infants. Further, while recent studies
showed that microstructural tissue properties using recently developed quantitative MRI techniques (multi-shell
diffusion tensor imaging, quantitative T1, qT1) are more sensitive and reliable to define underlying neurological
diseases and, thus, it is crucial to investigate the microstructural tissue properties during the neonatal period to
fully understand how these measures are related to preterm birth and predict later cognitive problems in
preterm infants. The overarching aims of this project are designed to use neonatal multimodal neuroimaging
techniques, including qT1, for the first time, combining with environmental factors using advanced
computational approaches to define early biomarkers of later preterm neurodevelopmental outcomes. We
hypothesize that the links between structural-functional brain networks are significantly altered, and when
combined, will provide an exclusive prediction on later neurodevelopmental outcomes. Similarly, we will test
the working hypothesis preterm infants will have abnormal white and grey matter microstructures, and these
patterns will be correlated with the neurodevelopmental problems. Furthermore, we will explore the
environmental factors contributing to later cognitive issues that will play an essential role in predicting later
neurodevelopmental problems in preterm infants. The proposed research results will provide an early
diagnostic tool that could inform the treatments and implementation of preventative interventions before any
cognitive problem emerges. It also has an important impact on identifying behavioral targets to improve the life
course outcomes in preterm infants.
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Predicting long-term outcomes in preterm infants using multimodal neuroimaging techniques and environmental factors
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批准号:10507663
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项目类别:
-
资助金额:$13.21万
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财政年份:2022
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负责人:Elveda Gozdas
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依托单位:
海外基金