Combining network topology and information theory to construct representative brain networks.

ABSTRACT: Network neuroscience employs graph theory to investigate the human brain as a complex network, and derive generalizable insights about the brain's network properties. However, graph-theoretical results obtained from network construction pipelines that produce idiosyncratic networks may not generalize when alternative pipelines are employed. This issue is especially pressing because a wide variety of network construction pipelines have been employed in the human network neuroscience literature, making comparisons between studies problematic. Here, we investigate how to produce networks that are maximally representative of the broader set of brain networks obtained from the same neuroimaging data. We do so by minimizing an information-theoretic measure of divergence between network topologie

SUBMITTER: Luppi AI 

PROVIDER: S-EPMC7935031 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature