Harmonizing and Archiving of Large-scale Infant Neuroimaging Data
大规模婴儿神经影像数据的协调和归档
基本信息
- 批准号:10189251
- 负责人:
- 金额:$ 62.7万
- 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:2021-06-01 至 2025-05-31
- 项目状态:未结题
- 来源:
- 关键词:2 year old5 year oldAddressAdoptedAdultAge-MonthsArchivesAreaAtlasesBRAIN initiativeBig DataBiologicalBiological PreservationBirthBrainCerebral cortexChildCodeDataData SetDepositionDevelopmentDiffuseEnsureExhibitsFeedbackFundingGoalsGrowthHumanImageImaging technologyInfantInstitutesIntuitionJointsLearningMRI ScansMachine LearningMagnetic Resonance ImagingMapsMethodsMyelinNational Institute of Mental HealthNetwork-basedNeurodevelopmental DisorderPatternPositioning AttributeProcessPropertyProtocols documentationQuality ControlReproducibilityResolutionRiskScanningSeriesSiteSource CodeStrategic PlanningStructureSurfaceTechniquesThickTissuesTravelTwin Multiple BirthUnited States National Institutes of HealthVariantautism spectrum disorderbasebrain abnormalitiescomputational atlascomputerized toolsconnectomecortex mappingcritical perioddata archivedata harmonizationdeep learningdesignimaging studyimprovedinfancyneural networkneuroimagingnovelpostnatalresponsesecondary analysistool
项目摘要
Project Abstract
The first postnatal years are an exceptionally dynamic and critical period of structural and functional development
of the human brain. Many neurodevelopmental disorders are the consequence of abnormal brain development
during this stage. Several NIH-funded studies have recently acquired and released large-scale infant brain MRI
datasets in the National Institute of Mental Health Data Archive (NDA), leading to over 3,000 publically-available
infant MRI scans from multiple imaging sites. Joint analysis of these big data of infant brains will undoubtedly
improve our limited understanding of normative early brain development and neurodevelopmental disorders with
boosted statistical power and reproducibility. However, the processed and harmonized data of these multi-site
infant MR images still remain publically absent, due to the challenges in processing and analyzing infant MR
images, which typically exhibit extremely low tissue contrast, large within-tissue intensity variations, and
regionally-heterogeneous dynamic changes. To address this critical issue, the goal of this project is to
comprehensively process, harmonize, discover and archive large-scale, multi-site public infant MRI datasets to
significantly advance early brain development studies, by taking advantage of our infant-tailored
computational tools and further developing advanced machine learning techniques. In Aim 1, we will
extensively process large-scale infant MRI datasets by adopting our established and recently-improved infant-
dedicated cortical surface-based computational tools and further develop a deep spherical neural network for
quality control of produced cortical property maps. This will lead to quality-ensured vertex-wise maps of multiple
biologically-distinct cortical properties, e.g., cortical thickness, surface area, myelin content, sulcal depth, local
gyrification, curvature and diffusivity. In Aim 2, to remove site effects associated with different scanners and
imaging protocols and meanwhile preserve biological associations, we will harmonize the computed cortical
property maps from multi-site data in Aim 1 by leveraging our surface-to-surface cycle-consistent generative
adversarial networks (S2SGAN) based on the spherical U-Net, without requiring traveling subjects (paired data)
across sites. To further increase the efficiency and learn more robust feature representation in the whole multi-
site data, we propose to extend S2SGAN to jointly harmonize all multi-site cortical property maps using a single
generator. In Aim 3, leveraging the informative growth patterns and gradient information of the harmonized maps
of multiple cortical properties in Aim 2, we will discover distinct cortical regions, by capitalizing on multi-view
nonnegative matrix factorization in a data-driven manner, without making any assumption on the parametric
forms of growth patterns. All our processed data, results, computational tools, and source codes will be deposited
into NDA, NITRC, and GitHub to significantly accelerate the pace of early brain development studies.
项目摘要
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Gang Li其他文献
Gang Li的其他文献
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Infant Functional Connectome Fingerprinting based on Deep Learning
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10288361 - 财政年份:2021
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Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI
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- 批准号:
10162317 - 财政年份:2018
- 资助金额:
$ 62.7万 - 项目类别:
Continued Development of Infant Brain Analysis Tools
婴儿大脑分析工具的持续开发
- 批准号:
9755508 - 财政年份:2018
- 资助金额:
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Using High Throughput Approach to Identify/Characterize Functional Variants on MS
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$ 62.7万 - 项目类别:
Continued Development of Infant Brain Analysis Tools
婴儿大脑分析工具的持续开发
- 批准号:
9919645 - 财政年份:2018
- 资助金额:
$ 62.7万 - 项目类别:
Continued Development of Infant Brain Analysis Tools
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10396127 - 财政年份:2018
- 资助金额:
$ 62.7万 - 项目类别:
Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI
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- 批准号:
10407000 - 财政年份:2018
- 资助金额:
$ 62.7万 - 项目类别:
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