Building the Ferretome

• ¹ Universitätsklinikum Hamburg-Eppendorf, Department of Computational Neuroscience, Germany

Databases of structural connections of the mammalian brain, such as CoCoMac (cocomac.g-node.org) or BAMS (brancusi.usc.edu), are a valuable resource for the analysis of brain connectivity and the modeling of brain dynamics in species such as the rhesus macaque or the rat, and have also contributed to the computational modeling of the human brain.
Another important model species which is widely used in electrophysiologic or developmental studies is the ferret. However, at the moment no systematic compilation of connectivity is available for this species. Thus, we have begun developing a database of anatomic connections and architectonic features of the ferret brain (the ‘Ferretome’, www.ferretome.org).
The main goals of this database project are: to assemble structural information on the ferret brain that is currently widely distributed in the literature or in in-house laboratory databases into single resource which is open to the scientific community; to try and build an extendable community resource that is beneficial not only to researchers in neuroinformatics and computational neuroscience, but also to neuroanatomists, by adding value to their data through algorithms for efficient data representation, analysis and visualization; to create techniques for the representation of quantitative and raw data; to expand existing database ontologies in order to accommodate further neuroarchitecture information for identifying essential relations between brain structure and connections.
The Ferretome database is being developed in MySQL and has adapted essential features of the CoCoMac methodology and legacy. In particular, its data model is derived from CoCoMac. It also uses a semantic parcelation of ferret brain regions as well as a logical brain maps transformation algorithm (objective relational transformation, ORT). The database has been populated with literature reports on tract tracing observations in the ferret brain using a custom-designed web interface that allows efficient and validated simultaneous input and proofreading by multiple curators.
The database is also equipped with a web interface for generating output data that was designed with keeping in mind non-computer science specialist users. This interface will be extended to produce connectivity matrices in several formats including a graphical representation superimposed on established ferret brain maps. An important feature of the Ferretome database is the possibility to trace back entries in connectivity matrices to the original studies archived in the system.
We hope that the Ferretome database will become a useful resource for neuroinformatics and neural modeling, will support studies of the ferret brain in the long-term and facilitate advances in comparative studies of mesoscopic brain connectivity.