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Power-law dynamics in cortical excitability as probed by early somatosensory evoked responses

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Stephani,  Tilman
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
International Max Planck Research School NeuroCom, Leipzig, Germany;

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Villringer,  Arno
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
MindBrainBody Institute, Berlin School of Mind and Brain, Humboldt University Berlin, Berlin, Germany;

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Nikulin,  Vadim V.
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Center for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russian Federation;

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Citation

Stephani, T., Waterstraat, G., Haufe, S., Curio, G., Villringer, A., & Nikulin, V. V. (2019). Power-law dynamics in cortical excitability as probed by early somatosensory evoked responses. bioRxiv. doi:10.1101/809285.


Cite as: http://hdl.handle.net/21.11116/0000-0004-F0C4-F
Abstract
While it is well-established that instantaneous changes in neuronal networks’ states lead to variability in brain responses and behavior, the mechanisms causing this variability are poorly understood. Insights into the organization of underlying system dynamics may be gained by examining the temporal structure of network state fluctuations, such as reflected in instantaneous cortical excitability. Using the early part of single-trial somatosensory evoked potentials in the human EEG, we non-invasively tracked the magnitude of excitatory post-synaptic potentials in the primary somatosensory cortex (BA 3b) in response to median nerve stimulation. Fluctuations in cortical excitability demonstrated long-range temporal dependencies decaying according to a power-law across trials. As these dynamics covaried with pre-stimulus alpha oscillations, we establish a functional link between ongoing and evoked activity and argue that the co-emergence of similar temporal power-laws may originate from neuronal networks poised close to a critical state, representing a parsimonious organizing principle of neural variability.