Mapping Patterns of Brain Tissue Growth in Premature Neonates

摘要 高比例的早产儿在以后会有神经发育和学习障碍。 在生活中。到目前为止，几乎没有确定可靠的原因或预测因素，可以在 出生后早期，与这些个体的不良结局有关。脑白质损伤是常见的 在磁共振成像(MRI)研究中发现的早产儿，但其后果 这些损伤对脑形态和神经发育结局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几何上可量化的 正常大脑发育的障碍，在新生儿核磁共振中可以检测到。我们假设这样的情况 损伤与特定的功能能力有关，发生在发育早期，可能是高度集中的和 在本质上解剖学上微妙的。该项目将发展最先进的图像分析技术，特别是 从高分辨率MRI数据研究发育中的早产儿脑。这最初的目的是 检测早期脑发育与30个月后临床结果之间的关系，以确定 预测结果的一致的解剖学特征。具体地说，它将开发研究这两者的技术 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图案。它将结合基于变形的形态测量技术，专门 组织分割算法，结合脑表面曲率分析方法。新统计 将开发分析方法，允许在空间假设的情况下分析组织生长和 表面曲率，以便在发育早期发现预测不良结果的局灶性扰动。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。这种技术在许多新的体内研究中有广泛的应用。 现在正在进行过早的脑部解剖。他们承诺揭示潜在的伤害模式 这是可以避免的，并可能允许在大脑可塑性高的早期进行干预。最终，这些 提高我们的理解可能有助于改善发育结果。

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.