Surrogate-assisted analysis of weighted functional brain networks

TL;DR

This paper introduces a surrogate-assisted method for analyzing weighted functional brain networks, helping to distinguish meaningful network features from confounding factors, with applications to epilepsy EEG/MEG data.

Contribution

The study presents a novel surrogate normalization approach for weighted brain network analysis, improving interpretability and reliability of network characteristics.

Findings

Surrogate normalization segregates trivial from meaningful network features.

The approach reveals additional insights into epilepsy brain networks.

It prevents misinterpretation of network analysis results.

Abstract

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.