Continuous longitudinal atlas construction for the study of brain development

用于大脑发育研究的连续纵向图谱构建

基本信息

批准号：
10683307
负责人：
Lilla Zollei
金额：
$ 51.38万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-12 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10683307
关键词：
Adult Algorithms Anterior Area Atlases Behavior Birth Rate Brain Brain imaging Cause of Death Cerebral cortex Child Child Development Childhood Clinical Clinical Research Communities Complex Computational algorithm Data Data Collection Data Set Development Disease Evolution Exposure to Funding Future Geometry Goals Growth Health Hippocampus Image Image Analysis Impaired cognition Infant Institution Interruption Life Longitudinal Studies Machine Learning Magnetic Resonance Imaging Maps Mental disorders Modeling Multimodal Imaging Neonatal Neurodevelopmental Disability Neurologist Neurology Neurosciences Neurosciences Research Outcome Pattern Perinatal Play Population Positioning Attribute Poverty Predisposition Premature Birth Premature Infant Process Research Resolution Resources Rest Risk Role Socialization Stress Structure Substance abuse problem Surface Temporal Lobe Temporal Sulcus Testing Time Toddler United States United States National Institutes of Health Universities Washington Work algorithm development biomedical imaging brain abnormalities brain magnetic resonance imaging clinical application cognitive control cognitive function cohort computerized data processing computerized tools connectome data acquisition data complexity deep learning early life adversity extreme prematurity frontal lobe imaging study improved infancy infant death large scale data learning strategy multidimensional data multidisciplinary myelination neonate neurodevelopment neuroimaging non-compliance novel perinatal period potential biomarker prenatal procedural pain recruit spatiotemporal statistics tool translational neuroscience unsupervised learning white matter

项目摘要

Abstract: There is rapid development of the cerebral cortex that takes place during the perinatal period. In order to characterize this complex process spatio-temporal atlases are needed. At present, however, there is limited availability of cortical surface atlases for the infant brain. The development of robust tools applicable to this population has been significantly lagging compared to those introduced for adults given the difficulty of obtaining data from non-compliant neonates and toddlers and the rapid change in contrast and geometry displayed during development in infants. With the advent of large longitudinal brain imaging studies, such as the UNC/UMN Baby Connectome Project (BCP) and HEALthy Brain and Child Development (HBCD) Study, novel algorithmic solutions are needed to efficiently process these data sets. During the proposed project, we intend to develop a FreeSurfer-compatible pipeline that will establish a consistent and unbiased representation of the perinatal cortex over time and a set of tools that, when used with standard clinical MRI acquisitions, will enable the computation of accurate and robust spherical representations of the developing brain along with detailed macrostructural annotations. Such surfaces then will be used to characterize healthy brain development over the first ten years of life. For our clinical application, we will focus on the effects of early life adversity on brain development as well as on disentangling contradictory findings about brain abnormalities in very preterm infants, such as gyrification complexity in their anterior and posterior temporal lobes. This work will be performed at the MGH/Harvard/MIT Martinos Center for Biomedical Imaging relying on datasets collected by local collaborators, NIH-funded publicly available large scale data sets as well as a clinical cohort assembled by colleagues at the Washington University. Such an effort will allow us to take advantage of cutting-edge neonatal imaging and computational algorithm development expertise in an attempt to deliver computational tools robust and accurate enough for future clinical studies

抽象的：围产期大脑皮层迅速发育。在为了描述这一复杂过程，需要时空地图集。现在，然而，婴儿大脑的皮质表面图谱的可用性有限。这与此相比，适用于该人群的强大工具的开发明显滞后鉴于很难从不合规的新生儿那里获取数据，因此针对成人引入了这些和幼儿以及在发育过程中显示的对比度和几何形状的快速变化婴儿。随着大型纵向脑成像研究的出现，例如 UNC/UMN Baby 连接组项目 (BCP) 和健康大脑和儿童发展 (HBCD) 研究，新颖需要算法解决方案来有效地处理这些数据集。拟议期间项目中，我们打算开发一个与 FreeSurfer 兼容的管道，该管道将建立一致且随着时间的推移围产期皮质的公正表示以及一组工具，当与标准临床 MRI 采集，将能够计算准确且稳健的球面发育中的大脑的表征以及详细的宏观结构注释。这样的然后表面将被用来表征前十年的健康大脑发育生活。对于我们的临床应用，我们将重点关注早年逆境对大脑的影响发展以及解决有关大脑异常的矛盾发现早产儿，例如其前颞叶和后颞叶的回旋复杂性。这工作将在麻省总医院/哈佛大学/麻省理工学院马蒂诺斯生物医学成像中心进行基于当地合作者收集的数据集、NIH 资助的公开大规模数据集以及由华盛顿大学同事组建的临床队列。这样的努力将使我们能够利用尖端的新生儿成像和计算算法开发专业知识，试图提供足够强大和准确的计算工具用于未来的临床研究

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

An Opportunity to Increase Collaborative Science in Fetal, Infant, and Toddler Neuroimaging.

增加胎儿、婴儿和幼儿神经影像学协作科学的机会。

DOI：
发表时间：
2023-05-15
期刊：
影响因子：
10.6
作者：
Fetal, Infant, and Toddler Neuroimaging Group;Korom, Marta;Catalina Camacho, M;Ford, Aiden;Taha, Hana;Scheinost, Dustin;Spann, Marisa;Vaughn, Kelly A
通讯作者：
Vaughn, Kelly A