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Meeting Abstract

Gamma-Power in a Fronto-Parietal Network Predicts Motor-Imagery Performance

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Grosse-Wentrup,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Grosse-Wentrup, M. (2011). Gamma-Power in a Fronto-Parietal Network Predicts Motor-Imagery Performance. In Twentieth Annual Computational Neuroscience Meeting (CNS*2011): Workshop on Relevance of Coherent Neural Activity for Brain Functionality (pp. 9).


Cite as: https://hdl.handle.net/21.11116/0000-0002-4BC0-1
Abstract
While the neuro-physiological basis of motor imagery has be en studied in great detail, little is known on the neural determinants of good or bad motor-imagery performance. In this talk, I present evidence based on EEG recordings in normal subjects that the baseline power of fronto-parietal gamma-oscillations (i.e., oscillations of the electromagnetic field of the brain roughly above 50 Hz) predicts the performance of subsequent motor-imagery on a trial-to-trial basis. Furthermore,our results suggest that the power of these gamma-oscillations is not modulated by the instruction to initiate motor imagery, but oscillates autonomously at predominantly very low frequencies. I analyze these observations in the framework of Causal Bayesian Networks, and argue that they provide support for a causal influence of a fronto-parietal resting-state network on motor-imagery performance.