Mapping Patterns of Brain Tissue Growth in Premature Neonates

绘制早产儿脑组织生长模式

基本信息

批准号：
8514085
负责人：
Colin Studholme
金额：
$ 32.24万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-08-01 至 2016-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8514085
关键词：
Accounting Activities of Daily Living Address Age Algorithms Anatomy Atlases Birth Brain Brain region Calcarine Sulcus Caring Cerebrum Characteristics Child Childhood Clinical Cognitive Contralateral Cross-Sectional Studies Data Data Analyses Development Disease Evaluation Gestational Age Goals Growth Human Image Analysis Individual Infant Injury Intervention Knowledge Learning Disabilities Lesion Life Linear Models Magnetic Resonance Imaging Manuals Maps Measures Methodology Methods Modeling Motor Motor Cortex Nature Neonatal Neurites Neuroanatomy Neurodevelopmental Disability Neurological outcome Outcome Outcome Measure Pathway interactions Pattern Population Population Heterogeneity Precentral gyrus Predictive Factor Premature Birth Premature Infant Public Health Publishing Resolution Scanning Statistical Methods Statistical Models Structure Surface Techniques Term Birth Testing Time Tissues Validation Visual Visual Cortex Work abstracting base brain morphology brain tissue brain volume functional disability gray matter improved in vivo interest morphometry neonate neuropsychiatry novel postnatal premature visual motor white matter white matter injury

项目摘要

Abstract A high percentage of prematurely born children suffer from neurodevelopmental and learning disabilities later in life. To date, few reliable causative or predictive factors have been identified that can be recognized during the early postnatal period and relate to poor outcome in these individuals. White matter injury is commonly identified on magnetic resonance imaging (MRI) studies of prematurely born infants, but the consequences of these lesions on brain morphology and neurodevelopmental outcome has not been reliably determined. Abnormal white matter and related gray matter integrity visible on MRI may result in geometrically quantifiable disturbances from normal brain development, which are detectable in neonatal MRI. We hypothesize that such damage, relating to specific functional abilities and occurring early in development, may be highly focal and anatomically subtle in nature. This project will develop state of the art image analysis techniques specifically to study the developing premature neonatal brain from high resolution MRI data. This is initially aimed at detecting relationships between early brain development and clinical outcome at 30 months, in order to identify consistent anatomical features that predict outcome. Specifically, it will develop techniques to study the two main quantifiable aspects of developmental neuroanatomy: patterns of local and global tissue volume and the resulting patterns of cortical folding. It will combine deformation based morphometric techniques, specialized tissue segmentation algorithms, together with methods of brain surface curvature analysis. New statistical analysis methods will be developed that allow spatial hypothesis free analysis of patterns of tissue growth and surface curvature, in order to detect early focal disturbances in development that predict poor outcome. Finally, we will evaluate these techniques by looking for specific anatomical patterns related to visual and motor outcome abnormalities. Such techniques have a range of applications in the many new in-vivo studies of premature brain anatomy now being performed. They promise to reveal patterns of injury that are potentially avoidable and may allow interventions at early ages when brain plasticity is high. Ultimately, these improvements in our understanding may help to improve developmental outcome.

抽象的很大比例的早产儿随后会出现神经发育和学习障碍在生活中。迄今为止，很少有可靠的致病因素或预测因素能够在产后早期并与这些人的不良结局有关。白质损伤常见对早产儿的磁共振成像 (MRI) 研究发现了这一点，但其后果这些病变对大脑形态和神经发育结果的影响尚未得到可靠确定。 MRI 上可见的异常白质和相关灰质完整性可能会导致几何量化正常大脑发育的干扰，可在新生儿 MRI 中检测到。我们假设这样的与特定功能能力相关并发生在发育早期的损害可能是高度集中的和本质上在解剖学上很微妙。该项目将专门开发最先进的图像分析技术利用高分辨率 MRI 数据研究发育中的早产新生儿大脑。此举最初的目的是检测早期大脑发育与 30 个月时临床结果之间的关系，以确定预测结果的一致的解剖特征。具体来说，它将开发研究这两种技术的技术发育神经解剖学的主要可量化方面：局部和整体组织体积的模式以及由此产生的皮质折叠模式。它将结合基于变形的形态测量技术、专门组织分割算法，以及大脑表面曲率分析方法。新统计将开发分析方法，允许对组织生长模式进行空间假设自由分析表面曲率，以便检测发育中预测不良结果的早期局灶性干扰。最后，我们将通过寻找与视觉和运动相关的特定解剖模式来评估这些技术结果异常。此类技术在许多新的体内研究中具有广泛的应用现在正在进行早产性大脑解剖。他们承诺揭示潜在的伤害模式这是可以避免的，并且可以在大脑可塑性较高的早期进行干预。最终，这些我们理解的提高可能有助于改善发展成果。