Identification of Dynamic functional brain network states Through Tensor Decomposition
This work addresses the need to track dynamic functional brain networks for understanding human cognition, representing an incremental advancement in neuroimaging analysis.
The paper tackles the problem of identifying dynamic functional brain network states, which are quasi-stationary time intervals, by proposing a tensor decomposition method, and applies it to EEG data during error-related negativity studies.
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).