2012/05/24 by Gerrit Ansmann, Klaus Lehnertz
Biochemistry, Genetics and Molecular Biology · Mathematics · Neuroscience · Physics and Astronomy · Psychology · #Artificial intelligence #Complex network #Computer science #Confounding #Connectome #Functional Brain Connectivity Studies #Functional connectivity #Graph #Machine learning #Mathematics #Mental Health Research Topics #Network analysis #Neural dynamics and brain function #Neuroscience #Normalization (sociology) #Pattern recognition (psychology) #Power graph analysis #Psychology #Statistics #Theoretical computer science #physics.med-ph #q-bio.NC
paper · pdf · doi:10.1016/j.jneumeth.2012.05.008
published as Journal of Neuroscience Methods 208, 165-172 (2012)
openalex publication_date 2012/05/24 · arxiv created 2014/08/26 · arxiv updated 2014/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Graph-theoretical analyses of complex brain networks is a rapidly evolving field with a strong impact for neuroscientific and related clinical research. Due to a number of confounding variables, however, a reliable and meaningful characterization of particularly functional brain networks is a major challenge. Addressing this problem, we present an analysis approach for weighted networks that makes use of surrogate networks with preserved edge weights or vertex strengths. We first investigate whether characteristics of weighted networks are influenced by trivial properties of the edge weights or vertex strengths (e.g., their standard deviations). If so, these influences are then effectively segregated with an appropriate surrogate normalization of the respective network characteristic. We demonstrate this approach by re-examining, in a time-resolved manner, weighted functional brain networks of epilepsy patients and control subjects derived from simultaneous EEG/MEG recordings during different behavioral states. We show that this surrogate-assisted analysis approach reveals complementary information about these networks, can aid with their interpretation, and thus can prevent deriving inappropriate conclusions.