Deducing logical relationships between spatially registered cortical parcellations under conditions of uncertainty
Deducing logical relationships between spatially registered cortical parcellations under conditions of uncertainty
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DOI：
10.1016/j.neunet.2008.05.010
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发表时间：
2008-10-01
期刊：
影响因子：
7.8
通讯作者：
Koetter, Rolf
中科院分区：
文献类型：
--
作者：
Bezgin, Gleb;Wanke, Egon;Koetter, Rolf
我们提出了一种称为空间目标关系变换（SORT）的新技术，作为一种自动方法，用于推导在同一欧几里得空间中注册的不同脑图的皮层区域之间的逻辑关系。最近，出现了大量基于体素的三维结构和功能成像数据，这些数据为我们提供了有关大脑中不同定义区域的位置的基于坐标的信息，而与坐标无关、基于分区的映射仍然常用于大多数动物追踪和映射研究中。由于基于体素的成像方法的影响以及将其特征归因于与坐标无关的大脑实体的需要，这种映射变得越来越重要。我们在这里的动机不是做出模糊的陈述，更精确的空间陈述会更好，而是寻找不同方法、不同个体、或使用不同变形算法映射到三维空间的区域之间的同一性（或其他逻辑关系）标准。当人们叠加并比较多模态数据库（例如 CARET，http://brainmap.wustl.edu/caret）中的不同数据集时，这一问题的相关性立即变得显而易见，其中基于体素的数据被注册到此处介绍的过程所利用的表面节点。我们描述了 SORT 算法及其在 Java 2 编程语言（http://Java.sun.com/，可供下载）中的实现。我们给出了我们的方法的实际使用示例，并针对使用替代技术推导出的与坐标无关的语句和推论的数据库验证了 SORT 方法。(C) 2008 Elsevier Ltd。保留所有权利。
We Propose a new technique, called Spatial Objective Relational Transformation (SORT), as an automated approach for derivation of logical relationships between cortical areas in different brain maps registered in the same Euclidean space. Recently, there have been large amounts of voxel-based three-dimensional structural and functional imaging data that provide us with coordinate-based information about the location of differently defined areas in the brain, whereas coordinate-independent, parcellation-based mapping is still commonly used in the majority of animal tracing and mapping studies. Because of the impact of voxel-based imaging methods and the need to attribute their features to coordinate-independent brain entities, this mapping becomes increasingly important. Our motivation here is not to make vague statements where more precise spatial statements would be better, but to find criteria for the identity (or other logical relationships) between areas that were delineated by different methods, in different individuals, or mapped to three-dimensional space using different deformation algorithms. The relevance of this problem becomes immediately obvious as one superimposes and compares different datasets in multimodal databases (e.g. CARET, http://brainmap.wustl.edu/caret), where voxel-based data are registered to surface nodes exploited by the procedure presented here. We describe the SORT algorithm and its implementation in the Java 2 programming language (http://Java.sun.com/, which we make available for download. We give an example of practical use of our approach, and validate the SORT approach against a database of the coordinate-independent statements and inferences that have been deduced using alternative techniques. (C) 2008 Elsevier Ltd. All rights reserved.