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Cerebral Cortex Advance Access originally published online on March 17, 2006
Cerebral Cortex 2007 17(2):400-414; doi:10.1093/cercor/bhj157
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Detection of Active and Silent States in Neocortical Neurons from the Field Potential Signal during Slow-Wave Sleep

Mikhail Mukovski1, Sylvain Chauvette2, Igor Timofeev2 and Maxim Volgushev1,3

1 Department of Neurophysiology, Ruhr-University Bochum, Bochum, Germany, 2 Department of Anatomy and Physiology, Laval University, Quebec, Canada, 3 Institute of Higher Nervous Activity and Neurophysiology Russian Academy of Sciences, Moscow, Russia

Address correspondence to Maxim Volgushev, Department of Neurophysiology, Ruhr-University Bochum, MA 4/149 D-44801 Bochum, Germany. Email: maxim{at}neurop.rub.de.

Oscillations of the local field potentials (LFPs) or electroencephalogram (EEG) at frequencies below 1 Hz are a hallmark of the slow-wave sleep. However, the timing of the underlying cellular events, which is an alternation of active and silent states of thalamocortical network, can be assessed only approximately from the phase of slow waves. Is it possible to detect, using the LFP or EEG, the timing of each episode of cellular activity or silence? With simultaneous recordings of the LFP and intracellular activity of 2–3 neocortical cells, we show that high–gamma-range (20–100 Hz) components in the LFP have significantly higher power when cortical cells are in active states as compared with silent-state periods. Exploiting this difference we have developed a new method, which uses the LFP signal to detect episodes of activity and silence of neocortical neurons. The method allows robust, reliable, and precise detection of timing of each episode of activity and silence of the neocortical network. It works with both surface and depth EEG, and its performance is affected little by the EEG prefiltering during recording. These results open new perspectives for studying differential operation of neural networks during periods of activity and silence, which rapidly alternate on the subsecond scale.

Key Words: active and silent states • EEG and intracellular • oscillations • sleep • state detection


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