Identification of Dynamic functional brain network states Through Tensor Decomposition

TL;DR

This paper introduces a tensor decomposition method to identify dynamic, quasi-stationary functional brain network states from EEG data, advancing understanding of brain connectivity changes during cognitive processes.

Contribution

The study presents a novel tensor decomposition approach for detecting and characterizing dynamic brain network states, addressing the limitations of static network analysis.

Findings

Successfully identified temporally invariant network states

Applied method to EEG data during error-related negativity

Revealed dynamic reorganization of brain networks

Abstract

With the advances in high resolution neuroimaging, there has been a growing interest in the detection of functional brain connectivity. Complex network theory has been proposed as an attractive mathematical representation of functional brain networks. However, most of the current studies of functional brain networks have focused on the computation of graph theoretic indices for static networks, i.e. long-time averages of connectivity networks. It is well-known that functional connectivity is a dynamic process and the construction and reorganization of the networks is key to understanding human cognition. Therefore, there is a growing need to track dynamic functional brain networks and identify time intervals over which the network is quasi-stationary. In this paper, we present a tensor decomposition based method to identify temporally invariant 'network states' and find a common topographic representation for each state. The proposed methods are applied to electroencephalogram (EEG) data during the study of error-related negativity (ERN).