Combining collation and annotation efforts toward completion of the rat and mouse connectomes in BAMS - Bota, Mihail; Dong, Hong-Wei; Swanson, Larry W. (Frontiers in Neuroinformatics, 2012)
Many different independently published neuroanatomical parcellation schemes (brain maps, nomenclatures, or atlases) can exist for a particular species, although one scheme (a standard scheme) is typically chosen for mapping neuroanatomical data in a particular study. This is problematic for building connection matrices (connectomes) because the terms used to name structures (especially gray matter regions and white matter tracts) in different parcellation schemes differ widely and interrelationships are seldom defined. Therefore, data sets cannot be compared across studies that have been mapped on different neuroanatomical atlases without a reliable translation method. Because resliceable 3D brain models for relating systematically and topographically different parcellation schemes are still in the first phases of development, it is necessary to rely on qualitative comparisons between regions and tracts that are either inserted directly by neuroanatomists or trained annotators, or are extracted or inferred by collators from the available literature. The structure and functionality of BAMS (http://brancusi.usc.edu/bkms) Cell Module is then presented as a basis for constructing interrelated connectomes based on neuron types (mesoconnectomes) and individual neurons (microconnectomes). Next the latest version of BAMS rat macroconnectome is presented because it is significantly more populated with the number of inserted connectivity reports reaching a landmark value (50,000), and it is based on a different classification scheme. Finally, a general methodology and strategy is discussed for producing global connection matrices, starting with rigorous mapping of data, then inserting and annotating it in BAMS, and ending with online generation of large scale connections matrices.