Mapping Patterns of Brain Tissue Growth in Premature Neonates

描述（由申请人提供）：高比例的早产儿在以后的生活中患有神经发育和学习障碍。到目前为止，很少有可靠的致病或预测因素已被确定，可以在出生后早期识别，并与这些人的不良结果。白色损伤通常在早产儿的磁共振成像（MRI）研究中被识别，但这些病变对脑形态和神经发育结果的影响尚未得到可靠的确定。MRI上可见的异常白色物质和相关灰质完整性可能导致正常脑发育的几何学可量化干扰，这在新生儿MRI中可检测到。我们推测，这种损害，与特定的功能能力和发生在早期的发展，可能是高度集中和解剖学上微妙的性质。该项目将开发最先进的图像分析技术，专门用于从高分辨率MRI数据中研究发育中的早产儿大脑。这最初旨在检测早期大脑发育与30个月临床结果之间的关系，以确定预测结果的一致解剖特征。具体来说，它将开发技术来研究发育神经解剖学的两个主要的可量化方面：局部和全球组织体积的模式和由此产生的皮质折叠模式。它将结合联合收割机基于变形的形态测量技术，专门的组织分割算法，以及大脑表面曲率分析方法。将开发新的统计分析方法，允许对组织生长和表面曲率模式进行空间假设自由分析，以检测预测不良结局的发育早期局灶性障碍。最后，我们将通过寻找与视觉和运动结果异常相关的特定解剖模式来评估这些技术。这种技术在许多新的早产儿脑解剖学的体内研究中有广泛的应用。它们有望揭示潜在可避免的损伤模式，并可能允许在大脑可塑性高的早期进行干预。最终，我们理解的这些改进可能有助于改善发展结果。公共卫生相关性：早产是儿童神经精神障碍的主要原因之一。该项目将开发和应用从早产儿的MRI扫描中检测大脑异常的新方法。这将特别允许早产后正常和异常脑组织生长模式的空间映射。这些方法将有助于我们了解早产儿的大脑发育，并揭示可能的新的神经解剖学标志物，使早期临床干预的不良神经功能的结果。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: Premature birth is one of the major causes of childhood neuropsychiatric disorder. This project will develop and apply new methods of detecting brain abnormalities from MRI scans of the premature neonate. The will specifically allow the spatial mapping of patterns of normal and abnormal brain tissue growth after premature birth. These methods will contribute to our understanding of brain development in premature babies and reveal possible new neuronanatomical markers for poor neurological outcome that allow early clinical intervention.