Uniresolution representations of white-matter data from CoCoMac

TL;DR

This paper addresses the challenge of multi-resolution white matter data from CoCoMac by proposing three methods to unify the data into single-resolution networks, enabling more accurate network analysis.

Contribution

It introduces three novel methods to resolve multi-resolution issues in neuroinformatics data, ensuring consistent network representations at a single resolution.

Findings

The proposed methods produce networks with comparable analysis metrics.

Networks at different resolutions show significant differences in degree distributions.

Unified networks improve the reliability of network theoretic analyses.

Abstract

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.