A graph theoretic approach to dynamic functional connectivity tracking and network state identification

Abstract

With the advances in neuroimaging technology, it is now possible to measure human brain activity with increasing temporal and spatial resolution. This vast amount of spatio-temporal data requires the development of computational methods capable of building an integrated picture of the functional networks for a better understanding of the healthy and diseased brain [1]. Although the construction of these networks from neuroimaging data is well-established [2], current approaches are limited to the characterization of the global topology of static networks where the links between different brain regions represent average connectivity over a long time period [3], [2]. Recent studies suggest that human cognition arises from the rapid formation and dissociation of synchronized neural activity on short time scales in the order of milliseconds [4]. There is a strong need for new electroencephalogram (EEG)-based analytic frameworks for monitoring dynamic functional network activity. In this paper, we propose a graph theoretic approach for tracking the changing topology of functional connectivity networks across time. First, we introduce an event detection algorithm based on node level feature extraction and principal components analysis of time-dependent node correlation matrices. Then, we propose a k-means based clustering approach to characterize each time interval with the most common connectivity states. Finally, the proposed methodology is applied to the study of the dynamics of functional connectivity networks during error-related negativity (ERN).

Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes

“…Among the most recent characteristic examples of this trend are the two following works (Jamal et al 2014;Zoltowski et al 2014), which also build over the concept of time-varying functional connectivity patterns and, hence, bear some methodological similarities with the presented work. Additionally, the Dynome project should pay attention to the ''electrophysiological and anatomical signature'' of a cognitive function (e.g.…”

Time-Varying Brain Connectivity in fMRI Data: Whole-brain data-driven approaches for capturing and characterizing dynamic states

“…Data-driven approaches include sparsity-based parcellation 36 and latent variables analysis methods such as principal component analysis (PCA), group independent component analysis (ICA) 37 spatially constrained ICA 38 , independent vector analysis 25 , and tensor decompositions 39 . For example, in 40,41 first event related potentials in EEG data are detected and then summarized using PCA of time-dependent node correlation matrices. On the other hand, for fMRI data, decompositions that use ICA and IVA can be adapted to extract dynamic features in multiple ways as demonstrated in 30,34,42 among other references.…”

Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification

“…The only temporally dynamic component in the microstate model is the transition between states. Thus, each state lacks the dynamic spatiotemporal evolution of the electric field at the scalp (Dimitriadis et al, 2013; Zoltowski et al, 2014; Khanna et al, 2015; Mheich et al, 2015). …”