Assessment of neural dynamic coupling and causal interactions between independent EEG components from cognitive tasks using linear and nonlinear methods

Abstract

Over the past few years there has been an increased interest in studying the underlying neural mechanism of cognitive brain activity. In this direction, we study the brain activity based on its independent components instead of the EEG signal itself. Both linear and nonlinear synchronization measures are applied to EEG components, which are free of volume conduction effects and background noise. More specifically, a robust nonlinear state-space generalized synchronization assessment method and the recently introduced partial directed coherence are investigated in a working memory paradigm, during mental rehearsal of pictures. The latter is a linear method able to assess not only the independence of the brain regions, but also the direction of the statistically significant relationships. The results are in accordance with previous psychophysiology studies suggesting increased synchrony between prefrontal and parietal components during the rehearsal process, most prominently in gamma (ca. 40 Hz) band. This study indicates that functional connectivity during cognitive processes may be successfully assessed using independent components, which reflect distinct spatial patterns of activity.

EEG complexity as a biomarker for autism spectrum disorder risk

“…Abnormal brain connectivity, whether locally, regionally or both, may be a cause of a number of behavioral disorders, including ASD [9], and changes in local complexity are believed to be related to brain connectivity [55]. Local neural network connectivity undergoes rapid change during early development, and this may be reflected in the multiscale entropy of EEG signals, which is one measure of signal complexity that has been associated with health and disease [23].…”

Graph Analysis and Visualization for Brain Function Characterization Using EEG Data

“…Thus, the dynamics of interaction among different EEG/ MEG channels may be used for indexing neural synchrony of such local or distant brain sources [13]. Such sources act synchronously behaving similar to coupled oscillators [14] and their interactions can be measured using pair-wise linear (cross-coherence or phasecoherence) [16] or nonlinear dynamics and models [15,16,17]. Furthermore, the causality of the functional coupling of such oscillatory activities can be assessed with partially directed coherence, which reveals the direction of statistically significant relationships [18].…”

“…Synchronization can be evaluated not only on the actual recordings on the scalp electrodes but also on independent components. The later are derived from linear un-mixing transforms and are free from volume conduction effects [15,16].Networks are modeled by graphs which consist of a set of vertices and a set of pair of vertices called edges (Figure 1). In this respect, graph theory offers a unique perspective and a common framework for studying interactions between local and remote cortical areas, where areas correspond to vertices and interactions to edges.…”

Neurotechnology and Psychiatric Biomarkers

“…The complexity of synchronization patterns appears to change during network development and reflects different neural wiring schemes and levels of cluster organization (Fuchs et al, 2007). The synchronization patterns of complex networks have been shown to be closely related to the topology of the network (Arenas et al, 2006) and are related to brain connectivity (Sakkalis et al, 2008). Synchronization between sensors is an indicator of connectivity between brain regions on a scale commensurate with the sensor spacing.…”

“…The analysis of signal complexity and interaction between signals leading to transient synchronization may reveal information about local neural complexity and long-range communication between brain regions (Buzsáki, 2006;Stam, 2005;Varela et al, 2001). The synchronization patterns of complex networks have been shown to be closely related to the topology of the network (Arenas et al, 2006) and are related to brain connectivity (Sakkalis et al, 2008). EEG signals are believed to derive from pyramidal cells aligned in parallel in the cerebral cortex and hippocampus (Sörnmo and Laguna, 2005), which act as many interacting nonlinear oscillators (Nunez and Srinivasan, 2006).…”