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Functional connectivity of the brain describes the networkof correlated activities of different brain areas. However,correlation does not imply causality and most synchroni-zation measures do not distinguish causal and non-causalinteractions among remote brain areas, i.e. determine theeffective connectivity [1]. Identification of causal interac-tions in brain networks is fundamental to understandingthe processing of information. Attempts at unveiling signsof functional or effective connectivity from non-invasiveMagneto-/Electroencephalographic (M/EEG) recordingsat the sensor level are hampered by volume conductionleading to correlated sensor signals without the presenceof effective connectivity. Here, we make use of the transferentropy (TE) concept to establish effective connectivity.The formalism of TE has been proposed as a rigorousquantification of the information flow among systems ininteraction and is a natural generalization of mutualinformation [2]. In contrast to Granger causality, TE is anon-linear measure and not influenced by volume con-duction.We evaluate TE on MEG data recorded during a Simontask. Subjects were presented with a "L" or "R" letter ineither the left or the right side of a screen and had to pressa left-side key in response to the "L" letter and the right-side key in response to the "R" letter, independently of thespatial location of the stimulus. The sequential organiza-tion of this task allows us to infer gross effective connec-tivity: Visual input has to be processed and the letterrecognized before motor action can be planned. Hence,effective connectivity from visual cortices to frontal andmotor areas was expected. Our tasks directed visual inputeither to the left or right hemisphere in early visual cortexand required left or right hemispheric motor responses,presumably changing connectivity patterns.TE of channel pairs was computed with a Kozachenko-Leonenko estimator while the statistical significance ofthe causal interactions was determined by a non-paramet-ric permutation test. The flow of information revealedthat different neuronal circuits (including long-rangecausal influences like inter hemispheric temporal lobeinteractions) were recruited for the four different experi-mental conditions, in accordance to the physiologicalexpectation. The effective networks of Simon versus non-Simon effect conditions were also compared to explorethe use of effective connectivity as a tool to investigatecognitive processes.

Using transfer entropy to measure the patterns of information flow though cortex: application to MEG recordings from a visual Simon task