Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac)

The need to integrate massively increasing amounts of data on the mammalian brain has driven several ambitious neuroscientific database projects that were started during the last decade. Databasing the brain's anatomical conn ectivity as delivered by tracing studies is of particular importance as the se data characterize fundamental structural constraints of the complex and poorly understood functional interactions between the components of real ne ural systems. Previous connectivity databases have been crucial for analysi ng anatomical brain circuitry in various species and have opened exciting n ew ways to interpret functional data, both from electrophysiological and fr om functional imaging studies. The eventual impact and success of connectiv ity databases, however, will require the resolution of several methodologic al problems that currently limit their use. These problems comprise four ma in points: (i) objective representation of coordinate-free, parcellation-ba sed data, (ii) assessment of the reliability and precision of individual da ta, especially in the presence of contradictory reports, (iii) data mining and integration of large sets of partially redundant and contradictory data , and (iv) automatic and reproducible transformation of data between incong ruent brain maps. Here, we present the specific implementation of the 'collation of connectiv ity data on the macaque brains (CoCoMac) database (http://www.cocomac.org). The design of this database addresses the methodological challenges listed above, and focuses on experimental and computational neuroscientists' need s to flexibly analyse and process the large amount of published experimenta l data from tracing studies. In this article, we explain step-by-step the c onceptual rationale and methodology of CoCoMac and demonstrate its practica l use by an analysis of connectivity in the prefrontal cortex.