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|Title:||Compression in the sleep state from free scale analysis in free behaving mice.|
|Keywords:||Sleep compression;Behaviour time scale analysis;Detrended fluctuation analysis;Mice time scale polyfractal dynamics|
|Portuguese Abstract:||Introdução Based on the data recorded from an inertial sensor and electrophysiology from animal brain, we studied the freescale motion dynamics for the wake and nonREM states in mice. In this context, correlated, nocorrelated or anticorrelated signals are characterized by distinct fractal dimensions. We found that these states has almost identical Long Range Temporal Correlation (LRTC) profiles, but the nonREM states builds up with a compressed timing scale. As this study reflects the way that the muscle system behaves, the balance between being active and sleeping is not characterized only by minimizing the muscle activity during nonREM sleep, but also for changing the correlations time scale, to a faster side. Objetivos Whatever the brain does, its output is routed to the motor system trough the spinal cord, and it's function may result in some sort of movement. During sleep, the thalamus progressively blocks almost all motor commands, keeping activities in the autonomic system operational but shutting down the periferic system in what it is responsible for motion. In this state the body seems to be most of the time inactive, coordination and cooperation for the remaining vestigial movements may look to have little sense. Based on free scale statistics analysis, we investigate weather the motion with all the complexity it may have, keeps any relationship between awake and nonREM states. Métodos The dynamics for the animal movement was studied by the use of a complex statistics known as long range temporal correlation (LRTC). In these series of experiment there were used adult males C57BL6 mice (25months), and the experimental protocol was approved by the committee on the Ethics of Animal Experiments IINN International Institute of Neuroscience of Natal Edmond and Lily Safra (permit number: 08/2010). For local field potential recordings (LFP) and latter an hypnogram classification, there was implanted 16 chronic electrodes, in the hippocampus (5 electrodes), motor and somatosensory cortex (4 electrodes each). A three axis accelerometer was associated to the electronics in the headstage, and made possible activity recordings simultaneous with the LFP. The LFP and the accelerometer signal at a sample rate of 1000Hz was amplified and recorded in a 64 channel Plexon system for neural recording analysis. Spectral analysis of sleepwake cycle were used to identify and quantify occurrence of the states wake, paradoxal sleep REM, slow wavesleep (NonREM sleep). To estimate the multifractal properties of our experimental data we have calculated the multifractal spectra based on the Multifractal Fluctuation Analysis MFDFA method.The Detrended Fluctuation Analysis DFA is an improvement of the rescaled range method used to compute the Hurst Expoent. Resultados e Conclusões The signal fluctuation structure from the categorized activity data can be observed both by the power fourier and DFA analysis. Results are decipted in time in a log scale and the intervals revels a multifractal pattern related to memory persistence correlations given by the Hurst coefficient. Comparing the awake and NonREM states, we found a very similar profile as the curves shows in sequence very close values for the Hurst coefficient, differing mainly for the strength between these states. The awake and NonREM states has the same dynamics sequence in correlation memory persistence and also the multifractal pattern, but the NonREM state is compressed both in time and intensity by an amount around 20dB.|
|Appears in Collections:||ICe - Trabalhos apresentados em eventos|
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