Mapping Patterns of Brain Tissue Growth in Premature Neonates

描述（由申请人提供）：早产儿童在以后的生活中患有神经发育和学习障碍的比例很高。迄今为止，很少有可靠的病因或预测因素被确定，可以在产后早期识别，并与这些个体的不良结局有关。早产儿的核磁共振成像（MRI）研究通常会发现白质损伤，但这些损伤对脑形态和神经发育结果的影响尚未得到可靠的确定。MRI上可见的异常白质和相关灰质完整性可能导致正常大脑发育的几何量化紊乱，这在新生儿MRI中可以检测到。我们假设这种损伤与特定的功能能力有关，发生在发育早期，可能是高度局部的，在解剖学上是微妙的。该项目将发展最先进的图像分析技术，专门研究高分辨率MRI数据中发育中的早产儿大脑。这项研究最初旨在检测30个月时早期大脑发育与临床结果之间的关系，以确定预测结果的一致解剖学特征。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层折叠模式。它将结合基于形变的形态测量技术、专门的组织分割算法以及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的模式进行空间假设自由分析，以便发现预测不良结果的发育早期焦点干扰。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。这些技术在目前正在进行的许多新的早产儿脑解剖学体内研究中有广泛的应用。他们承诺揭示潜在可避免的损伤模式，并可能在早期大脑可塑性高的时候进行干预。最终，我们在理解上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。这个项目将开发和应用新的方法来检测大脑异常的MRI扫描早产儿。这将特别允许在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对早产儿大脑发育的理解，并揭示可能的新的神经解剖学标记，用于早期临床干预。

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.