Spatial and Temporal Patterns of Functional Activation

描述（由申请人提供）：我们的P20奖（MH 57180）的中心主题，从明尼苏达大学提交，是建模和可视化的空间和时间模式的功能激活在活的人类大脑。该应用程序包含两个神经信息学组件，项目2（功能神经成像中的共识模式）和项目3（计算解剖学和可视化），两者都涉及图像配准。虽然已经提出了许多基于特征的3D配准算法（翘曲）的PET，MRI和fMRI脑体积的对象间和对象内配准，这些算法的性能尚未优化的特征层次结构和选择。此外，“优度偏差”标准可能会有所不同，这取决于所解决的研究问题和MRI/fMRI数据的类型和质量。由于在项目2和3中进行的MRI/fMRI研究需要高质量的图像配准，我们建议系统地评估和比较几种自动化的基于特征的配准算法的性能。为此，我们将（i）建立一个由20个专业分割的高分辨率多模态数据库，（T1、T2和PD）全脑MR体积，用于评价受试者间脑图像配准，（ii）系统评价受试者间脑图像配准自动算法的准确性，（三）创建一个软件框架，允许自动配准算法的开发人员和交互式配准算法的用户系统地探索使用不同的特征图用于算法的图像/大脑相似性的内部计算，以及（iv）将我们标记的大脑体积、软件模块和测试结果作为可下载的分发集连同文档和日志文件一起发布在INC网站上。这项工作将与麦吉尔大学和宾夕法尼亚大学的研究人员合作完成。

DESCRIPTION (provided by applicant): The central theme of our P20 award (MH57180), submitted from the University of Minnesota, is modeling and visualization of spatial and temporal patterns of functional activation in the living human brain. The application incorporates two neuroinformatics components, Project 2 (Consensus Patterns in Functional Neuroimaging) and Project 3 (Computational Anatomy and Visualization), both of which involve image registration. Although numerous feature-based 3D registration algorithms (warps) for inter- and intrasubject registration of PET, MRI and fMRI brain volumes have been proposed, the performance of these algorithms has not been optimized with regard to feature hierarchy and selection. Moreover, "goodness-of-warp" criteria may vary depending upon the research question being addressed and the type and quality of MRI/fMRI data. Since the MRI/fMRI research being conducted in Projects 2 and 3 requires high-quality image registration, we propose to systematically evaluate and compare the performance of several automated feature-based registration algorithms. To this end, we will (i) develop a data base of 20 expertly-segmented high-resolution multimodal (T1, T2 and PD) whole-brain MR volumes for the evaluation of intersubject brain image registration, (ii) systematically evaluate the accuracy of automated algorithms for intersubject brain image registration, (iii) create a software framework that allows developers of automatic registration algorithms and users of interactive registration algorithms to systematically explore the utility of using different feature maps for an algorithm's internal computation of image/brain similarity, and (iv) post our labeled brain volumes, software modules and test results on the INC Web site as downloadable distribution sets, together with documentation and log files. This work will be accomplished in collaboration with investigators at McGill University and the University of Pennsylvania.