Coordinate-independent mapping of structural and functional data by objective relational transformation (ORT)

Neuroscience has produced an enormous amount of structural and functional d ata. Powerful database systems are required to make these data accessible f or computational approaches such as higher-order analyses and simulations. Available databases for key data such as anatomical and functional connecti vity between cortical areas! however, are still hampered by methodological problems. These problems arise predominantly from the parcellation problem, the use of incongruent parcellation schemes by different authors. We here present a coordinate-independent mathematical method to overcome this probl em: objective relational transformation (ORT). Based on new classifications for brain data and on methods from theoretical computer science, ORT repre sents a formally defined, transparent transformation method for reproducibl e, coordinate-independent mapping of brain data to freely chosen parcellati on schemes. We describe the methodology of ORT and discuss its strengths an d limitations. Using two practical examples, we show that ORT in conjunctio n with connectivity databases like CoCoMac (http://www.cocomac.org) is an i mportant tool for analyses of cortical organization and structure-function relationships.