Complex networks in brain electrical activity

Abstract

This paper is the result of work carried out at Starlab in collaboration with the Neurodynamics Laboratory of the U. of Barcelona (UBNL) focusing on complex networks analysis of EEG data (provided by UBNL). The approach is inspired by the work in [6] with fMRI data and represents a follow up on earlier efforts on analysis of ERP/MMN data using tomography and independent component analysis to characterize brain connectivity and spatial funcionalization [22]. Multichannel EEG measurements are first processed to obtain 3D voxel activations using the tomographic algorithm LORETA. Then, the correlation of the current intensity activation between voxel pairs is computed to produce a voxel cross-correlation coefficient matrix. Using several correlation thresholds, the cross-correlation matrix is then transformed into a network connectivity matrix and analyzed. The resulting analysis highlights significant differences between the spatial activations associated with Standard and Deviant tones, with interesting physiological implications. When compared to random data networks, physiological networks are more connected, with longer links and shorter path lengths. Furthermore, as compared to the Deviant stimulus case, Standard data networks are consistently more connected, with longer links and shorter path lengths-consistent with a "small worlds" character. The comparison between both networks shows that areas known to be activated in the MMN wave are connected. In particular, the analysis supports the idea that supra-temporal and inferior frontal data work together in the processing of the differences between sounds by highlighting an increased connectivity in the response to a novel sound. A major challenge for neuroscience is to map and analyze the spatiotemporal patterns of activity of the large neuronal populations which are believed to be responsible for information processing in the human brain. In this paper, we analyze the complex networks associated with brain electrical activity in a specific experimental context. Our approach uses multichannel EEG data analysis to characterize the spatial connectivity of the brain. Multichannel EEG measurements are first processed to obtain 3D voxel activations using the tomographic algorithm LORETA. Then, the correlation of the current intensity activation between voxel pairs is computed to produce a voxel cross-correlation coefficient matrix. Using several correlation thresholds, the cross-correlation matrix is then transformed into a network connectivity matrix and analyzed. To study a specific example, we selected data from an earlier experiment focusing on the Mismatch Negativity (MMN) brain wave-an Event-Related Potential (ERP)-because it gives an electrophysiological index of a "primitive intelligence" associated with auditory pattern and change detection in a regular auditory pattern. EEGs have an exceptional millisecond temporal resolution, but appear to result from mixed neuronal contributions whose spatial location and relationships are not fully understood. Although...

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