Leveraging artificial intelligence to develop novel tools for studying infant brain development
利用人工智能开发研究婴儿大脑发育的新工具
基本信息
- 批准号:10302034
- 负责人:
- 金额:$ 1.33万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-09-01 至 2021-10-15
- 项目状态:已结题
- 来源:
- 关键词:3 year old3-DimensionalAddressAdoptedAgeAgreementAmygdaloid structureAnxietyArtificial IntelligenceAttention deficit hyperactivity disorderAutomobile DrivingAwardBackBase of the BrainBehaviorBirthBrainBrain imagingBrain scanChild HealthClinicalCodeCognitionCommunitiesComputer softwareDataData SetDevelopmentDevelopmental Delay DisordersDiseaseEarly identificationEthnic OriginFunctional Magnetic Resonance ImagingFundingFunding MechanismsFutureGestational AgeGrowthHippocampus (Brain)HumanIndividualInfantKnowledgeLabelLaboratoriesLanguageLearningLifeMRI ScansMagnetic Resonance ImagingManualsMeasuresMental DepressionMental disordersMethodologyMethodsModelingNeuropsychologyNeurosciencesOutcomeParticipantPerformancePhasePilot ProjectsPrincipal Component AnalysisProblem behaviorProcessPsychological TransferPsychologyRaceReproducibilityResearchResearch PersonnelRestSample SizeSamplingScanningShapesSourceStandardizationStructureSymptomsTechniquesTestingTimeTissuesToddlerTrainingUnited States National Institutes of HealthUniversitiesartificial neural networkautomated segmentationbasecareercareer developmentcognitive abilitycognitive developmentcohortconnectomeconvolutional neural networkdata repositoryearly detection biomarkersemotional functioningexecutive functionfunctional MRI scangray matterimaging modalityimprovedinfancylarge scale datalong short term memory networkmultimodalityneuroimagingnovelrapid growthsexskillssocialtooluser-friendlyvirtualweb based interfaceweb interfacewhite matter
项目摘要
PROJECT SUMMARY. The first 24-months of human life are dynamic, characterized by rapid growth, and
increasingly recognized as crucial for establishing cognitive abilities and behaviors that last a lifetime. However,
little is known about trajectories of structural and functional brain development during this sensitive period in
typically developing infants, and even less is known about how deviations in these trajectories relate to emerging
cognition and behavior or predict later developmental outcomes. This is partially due to current technical
limitations on quantification of brain structure and function in infants via magnetic resonance imaging (MRI) – an
important, non-invasive approach to the study of developmental neuroscience. Currently there are insufficient
methods to analyze infant MRI scans across the first 24 months of life, especially for brain segmentation – the
first and critical step for virtually all quantitative analyses across MRI modalities. Without accurate and automated
segmentation, infant MRI analysis is prone to systematic errors and is labor-intensive, limiting the rigor and
reproducibility of infant MRI research. This limitation curtails and delays the utility of large-scale infant MRI
datasets in the foreseeable future. Addressing these research gaps would significantly advance efforts toward
early identification of developmental delays and/or disorders. I propose developing AI-based infant neuroimaging
analysis tools for studying the early human brain development via two large-scale datasets: the NIH funded Baby
Connectome Project and a centralized MRI data repository from Environmental Influence on Child Health
Outcomes. In my pilot studies, I have shown the show good-to-excellent agreement with ground-truth labels from
two different sources, and superior performance compared to other commonly used segmentation methods. My
first aim is to develop an automated and generalizable brain segmentation pipeline with 3D convolutional neural
networks – an AI approach. This segmentation tool can accommodate and process infant brain scans spanning
each month over the first 2 years of life. The final AI-based pipeline will be rigorously validated internally, and
tested externally. We will release the pipeline as a user-friendly, web-based interface for researchers to use in
scientific community. In Aim 2, I will delineate the growth trajectories of regional brain morphometrics, major
functional networks, and measure their relationships to neuropsychological functions during the first 24months
of life via data from BCP. In Aim 3, I will leverage two different approaches (AI and LPCA) to predict the
developmental outcomes assessed up to 3 years old. with the first-year longitudinal multimodal MRI scans from
BCP. The interdisciplinary training phase of the award, conducted in the laboratory of Dr. Jonathan Posner at
Columbia University, includes a comprehensive plan for the acquisition of technical and professional skills that
will enable my transition to research independence. The successful completion of this project will yield reliable
tools and novel data-driven methods for studying early brain developmental, fill critical knowledge gaps of early
development, and advance efforts toward early identification of developmental delays and disorders.
项目总结。人类生命的前24个月是动态的,以快速生长为特征
项目成果
期刊论文数量(0)
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YUN WANG其他文献
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{{ truncateString('YUN WANG', 18)}}的其他基金
Leveraging artificial intelligence to develop novel tools for studying infant brain development
利用人工智能开发研究婴儿大脑发育的新工具
- 批准号:
10554951 - 财政年份:2022
- 资助金额:
$ 1.33万 - 项目类别:
Search for the Structural Basis of Biomacromolecular Fun
寻找生物大分子乐趣的结构基础
- 批准号:
7052687 - 财政年份:
- 资助金额:
$ 1.33万 - 项目类别:
Search for the Structural Basis of Biomacromolecular Fun
寻找生物大分子乐趣的结构基础
- 批准号:
7338503 - 财政年份:
- 资助金额:
$ 1.33万 - 项目类别:
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