NORMAL MR NEUROMORPHOMETRY BY GLOBAL PATTERN MATCHING

这个子项目是利用由NIH/NCRR资助的中心拨款提供的资源的许多研究子项目之一。子项目和调查员(PI)可能从另一个NIH来源获得了主要资金，因此可能会出现在其他CRISE条目中。列出的机构是针对中心的，而不一定是针对调查员的机构。MRI可以观察到由于生长发育、衰老、性二态、偏侧性和惯用手造成的正常大脑的微小神经解剖学差异，但需要精确和准确的方法来检测它们。现代磁共振脑成像方法提供了关于个体大脑解剖结构的详细活体信息。然而，由于无法快速量化个体间的形态差异，这些解剖学数据的解释一直受到阻碍。困难在于两个方面。首先，不同个体之间的图像必须在共同的参照系中，但它们不是以这种方式收集的。其次，即使登记在案，正常的变异也会使比较变得困难(如果不是不可能的话)。这一建议的主要焦点是发展大脑和特定亚区，特别是海马体和颞叶的神经解剖变异的数学表示法。这涉及将单个m个形态地图集映射到多个单独的目标MR图像体积。图谱是一个多值空间数组，包含信号值(对应于CT、MR和彩色冰冻切片图像)及其对研究具有医学和生物学意义的所有子体积的符号标记(组织类型、解剖命名)。这些表示为在TRD4的AIMS 2、3和5中开发的从图谱(模板)及其子体积到靶解剖家族的平滑映射的算法生成提供了独特的工具。通过这种方式，从正常个体组中选择的形态测量特征将被比较。通过评估这些方法在描述正常人群方面的表现，我们将能够明确地测试关于异常人群中大脑亚结构变化的假设，这些假设不能用现有方法解决。进展：与Pat Barta博士合作，我们现在已经实现了自动分割上颞区的算法，允许研究Vannier拨款中规定的解剖的正常变异。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Small neuroanatomical differences in the normal brain due to growth and development, aging, sexual dimorphism, laterality, and handedness are observable by MRI but require precise and accurate methods for their detection. Modern MR brain imaging methods provide detailed in vivo information regarding the anatomical structure of individual brains. However, interpretation of these anatomic data has been hindered by the inability to expeditiously quantify morphological differences across individuals. The difficulty lies in two areas. First, images between different individuals must be in a common reference frame, but they are not collected in this fashion. Second, even when registered, normal variation makes comparisons difficult if not impossible. The major focus of this proposal is the development of mathematical representations of neuroanatomical variation of the brain and specific subregions, especially in the hippocampus and temporal lobe. This involves mapping of a single m orphometric atlas to multiple individual target MR image volumes. An atlas is a multivalued spatial array that contains signal values (corresponding to CT, MR and color cryosection images) with their symbolic labels (tissue type, anatomic nomenclature) for all subvolumes of medical and biological significance to the investigation. These representations provide a unique tool for the algorithmic generation of smooth maps from an atlas (template) and its subvolumes onto families of target anatomies being developed in aims 2,3 and 5 of TRD4. In this way, selected morphometric features from groups of normal individuals will be compared. By assessing the performance of these methods in the characterization of normal populations, we will be able to definitively test hypotheses regarding brain substructure changes in abnormal populations that cannot be resolved with present methods. Progress: Working with Dr. Pat Barta we have now implemented the algorithms for automated segmentation of superior temporal areas allowing for the study of normal variation of anatomy as specified in the Vannier grant.