The relationship between different brain areas is characterized by functional networks through correlations of time series obtained from neuroimaging experiments. Due to its high spatial resolution, functional MRI data is commonly used for generating functional networks of the entire brain. These networks are comprised of the measurement points/channels as nodes and links are established if there is a correlation in the measured time series of these nodes. However, since the evaluation of correlation becomes more accurate with the length of the underlying time series, we construct in this paper functional networks from MEG data, which has a much higher time resolution than fMRI. We study in particular how the network topologies change in an experiment on ambiguity of words, where the subject first receives a priming word before being presented with an ambiguous or unambiguous target word.

Network structures can be found in almost any kind of natural or artificial systems as transport medium for communication between the respective nodes. In this paper we study certain key topological features of brain functional networks obtained from functional magnetic resonance imaging (fMRI) measurements. We compare complex network measures of the extracted topologies with those from Internet service providers (ISPs). Our goal is to identify important features which will be helpful in designing more robust and adaptive future information network architectures.