The identification of community structure in graphs continues to attract great interest in several fields. Network neuroscience is particularly concerned with this problem considering the key roles communities play in brain processes and functionality. Most methods used for community detection in brain graphs are based on the maximization of a highly parameter-dependent modularity function. In practice, the parametrization of this function often obscures the physical meaning and hierarchical organization of the partitions of network nodes. In this work, we present a new method able to detect communities at different scales in a natural, unrestricted way. First, to obtain an estimation of the information flow in the network we release random walkers to freely move over it. The activity of the walkers is separated into oscillatory modes by using empirical mode decomposition. After grouping nodes by their co-occurrence at each time scale, the so-called k-modes clustering returns the desired partitions. Our algorithm was first tested on benchmark graphs with favorable performance. We used the method on brain networks, including the anatomical connectivity of the macaque and human brains and a model for the interactions between nodes. We found a repertoire of community structures in the anatomical and functional networks, with a clear link existing between these two. The observed partitions range from the evident division in two hemispheres –in which all processes are managed globally– to specialized communities seemingly given by physical proximity and shared function. Our results stimulate the research of hierarchical community organization in terms of temporal scales of information flow. Highlights Oscillatory modes of networks’ signals carry information on architectural rules. Meaningful partitions of the brain network are found over different temporal scales. The multiscale organization of the brain responds to the function of its components.
A method for multiscale community detection in brain networks
Lazaro M. Sanchez-Rodriguez,Yasser Iturria-Medina,Pauline Mouches,R. Sotero
Published 2019 in bioRxiv
ABSTRACT
PUBLICATION RECORD
- Publication year
2019
- Venue
bioRxiv
- Publication date
2019-08-22
- Fields of study
Biology, Computer Science
- Identifiers
- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
- No claims are published for this paper.
CONCEPTS
- No concepts are published for this paper.