Uniresolution representations of white-matter data from CoCoMac
This addresses a specific issue in neuroinformatics for researchers analyzing brain connectivity data, but it is incremental as it builds on existing database and network analysis methods.
The paper tackled the problem of multi-resolution white-matter tract data from the CoCoMac database, which can lead to incorrect assumptions in network analysis, by proposing three methods to unify the data into single-resolution networks and comparing their network metrics and degree distributions.
Tracing data as collated by CoCoMac, a seminal neuroinformatics database, is at multiple resolutions -- white matter tracts were studied for areas and their subdivisions by different reports. Network theoretic analysis of this multi-resolution data often assumes that the data at various resolutions is equivalent, which may not be correct. In this paper we propose three methods to resolve the multi-resolution issue such that the resultant networks have connectivity data at only one resolution. The different resultant networks are compared in terms of their network analysis metrics and degree distributions.