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

• 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
• 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

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： 在围产期期间发生的大脑皮层有快速发展。在 为了表征这种复杂的过程时空地图集。现在， 但是，婴儿脑的皮质表面图谱的可用性有限。这 相比 给成人引入的人，因为难以从不合规的新生儿获得数据 和幼儿以及在开发过程中显示的对比和几何形状的快速变化 婴儿。随着大型纵向大脑成像研究的出现，例如UNC/UMN婴儿 Connectome项目（BCP）和健康的大脑和儿童发展（HBCD）研究，新型 需要算法解决方案来有效地处理这些数据集。在提议期间 项目，我们打算开发兼容兼容的管道，该管道将建立一致和 随着时间的推移，围产期皮质的无偏表示，以及一组工具 标准的临床MRI获取将实现准确稳健的球形计算 发育中的大脑的表示以及详细的宏观结构注释。这样的 然后，表面将用于表征健康的大脑发育。 生活。对于我们的临床应用，我们将专注于早期逆境对大脑的影响 开发以及解散有关脑异常的矛盾发现 早产儿，例如其前后颞叶中的旋转复杂性。这 工作将在MGH/Harvard/MIT Martinos生物医学成像中心进行 在本地合作者收集的数据集上，NIH资助了公开可用的大规模数据集 以及华盛顿大学同事组装的临床队列。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，试图提供计算工具的强大而准确 对于将来的临床研究

项目成果

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: 10.1016/j.biopsych.2022.07.005
• 发表时间: 2023
• 期刊: Biological psychiatry
• 影响因子: 10.6
• 作者: Fetal,Infant,andToddlerNeuroimagingGroup;Korom,Marta;CatalinaCamacho,M;Ford,Aiden;Taha,Hana;Scheinost,Dustin;Spann,Marisa;Vaughn,KellyA
• 通讯作者: Vaughn,KellyA