WHITE MATTER ARCHITECTURE FROM DT-MRI

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得主要资金，因此可以在其他CRISP条目中表示。所列机构为中心机构，不一定为研究者机构。DT-MRI子项目的目的是为DT-MRI数据开发新的滤波、分割和纤维束成像方法。具体的临床目标是检测和定位白色物质的脑异常精神分裂症使用技术在DT-MRI和后处理。为了解决这些问题，我们正在开发新的技术，白色物质纤维束成像的基础上模拟的基本PDE描述各向异性扩散，并开发工具的可视化和交互式探索的三维DT-MRI数据。除了支持该核心的特定需求外，这些工具将直接适用于发育中婴儿大脑中白色物质损伤的早期检测，MS病变引起的白色物质破坏的探索和表征，以及神经外科手术期间重要白色物质束的可视化（例如，连接到初级和次级运动皮质的皮质脊髓束）。在过去的一年中，DTI核心集中在开发新的方法，使用随机方法的DTI纤维束成像。使用贝叶斯方法，我们已经开发出一种原则性的方法来描述固有的不确定性，在纤维束的位置，从一个单一的点，而不是一个单一的道是习惯的，通过生成一个分布的道。从这项工作中，我们得到了新的连接措施。我们进一步继续我们的工作，聚类纤维成解剖学上有意义的束，并有新的令人兴奋的结果，如何生成一个高维的地图集，用于描述基于聚类空间的神经解剖学。同样的技术也被证明对DTI数据的分组分析很有用，其中聚类被用来找到不同大脑区域之间的对应关系。另一个重要的工作是推广广泛使用的局部水分扩散剖面张量模型。在过去的一年里，我们还继续发展了双张量模型。双张量模型减少了复杂邻域产生的不确定性。一般来说，高阶模型易于改善扩散各向异性的跟踪和量化。

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. The purpose of the DT-MRI subproject is to develop novel filtering, segmentation, and tractography methods for DT-MRI data. The specific clinical objectives are to detect and localize white matter brain abnormalities in schizophrenia using techniques in DT-MRI and post-processing. To resolve these issues, we are developing new technology for white matter tractography based on simulation of the basic PDE describing anisotropic diffusion and also developing tools for visualization and interactive exploration of 3D DT-MRI data. In addition to supporting the specific needs in this core, these tools will be directly applicable to early detection of white matter damage in the developing infant brain, to exploration and characterization of white matter disruptions caused by MS lesions, and to the visualization of important white matter tracts during neurosurgical procedures (e.g., the cortical spinal tract connecting to the primary and secondary motor cortex). During the past year the DTI core has focused on developing new methods for DTI tractography using stochastic methods. Using a Bayesian approach, we have developed a principled way to describe the inherent uncertainty in fiber tract location by generating a distribution of tracts from a single point, rather than one single tract as is customary. From this work we have derived new measures of connectivity. We have further continued our work on clustering fibers into anatomically meaningful bundles, and have new exciting results on how to generate a high-dimensional atlas for describing the neuroanatomy based on the clustering space. The same technology has also proved useful for group analysis of DTI data, where clustering is used to find correspondences between regions across different brains. Another important line of work is to extend the widely used tensor model of local water diffusion profile. During the past year, we have also continued to develop the two-tensor model. The two-tensor model reduces the uncertainty generated by complex neighborhoods. In general, higher order models are apt to improve both tracking and quantification of diffusion anisotropy.