Deducing logical relationships between spatially registered cortical parcellations under conditions of uncertainty.

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.