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Predicting task performance based on electrophysiological resting state networks

Human performance differs between individuals, but also within individuals between repetitions of the same task. The proposed project investigates whether it is possible to identify intrinsic brain features responsible for performance differences in tasks and that correspondingly allow to predict performance.  This requires analyzing the dynamics of brain activity and the interaction between brain regions before the actual task. The brain networks that emerge while we are not performing a task are called resting state networks (RSNs). To study the dynamics of these RSNs, participants will be measured in the magnetoencephalogram (MEG). Using Hidden-Markov models will allow investigating the fast changing network activity. The pattern of networks and the networks active immediately preceding a stimulus presentation are expected to be predictive for the behavioral performance of the subjects. Identification and characterization of such a task-rest nexus will allow for a better understanding of the functional relevance of resting state brain activity. Because changes in resting state activity have been identified in many neurological and psychiatric diseases, such findings will be instrumental to better diagnose certain diseases through the recording of spontaneous brain activity.

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