Neural representation of language: activation versus long-range connectivity

Cognitive functions are thought to build on connectivity within large-scale neuronal networks, rather than on strictly localized processes. Yet, present understanding of neural mechanisms of language function, as derived from neuroimaging, is based on mapping brain areas that are more active during specific linguistic tasks than in control conditions. Connectivity can then be evaluated among those areas. However, network nodes should ideally be determined based on their correlated time series of activity. Recent developments in analysis methods now facilitate localization and characterization of functionally connected neural networks directly from real-time magnetoencephalography data. Analysis of long-range connectivity might clarify and expand the view provided by traditional neurophysiological and hemodynamic activation studies. Here, we use silent reading as the example process.