Multichannel Detrended Fluctuation Analysis Reveals Synchronized Patterns of Spontaneous Spinal Activity in Anesthetized Cats

The analysis of the interaction and synchronization of relatively large ensembles of neurons is fundamental for the understanding of complex functions of the nervous system. It is known that the temporal synchronization of neural ensembles is involved in the generation of specific motor, sensory or cognitive processes. Also, the intersegmental coherence of spinal spontaneous activity may indicate the existence of synaptic neural pathways between different pairs of lumbar segments. In this study we present a multichannel version of the detrended fluctuation analysis method (mDFA) to analyze the correlation dynamics of spontaneous spinal activity (SSA) from time series analysis. This method together with the classical detrended fluctuation analysis (DFA) were used to find out whether the SSA recorded in one or several segments in the spinal cord of the anesthetized cat occurs either in a random or in an organized manner. Our results are consistent with a non-random organization of the sets of neurons involved in the generation of spontaneous cord dorsum potentials (CDPs) recorded either from one lumbar segment (DFA- mean = 1.040.09) or simultaneously from several lumbar segments (mDFA- mean = 1.010.06), where  = 0.5 indicates randomness while 0.5 indicates long-term correlations. To test the sensitivity of the mDFA method we also examined the effects of small spinal lesions aimed to partially interrupt connectivity between neighboring lumbosacral segments. We found that the synchronization and correlation between the CDPs recorded from the L5 and L6 segments in both sides of the spinal cord were reduced when a lesion comprising the left dorsal quadrant was performed between the segments L5 and L6 (mDFA- = 0.992 as compared to initial conditions mDFA- = 1.186). The synchronization and correlation were reduced even further after a similar additional right spinal lesion (mDFA- = 0.924). In contrast to the classical methods, such as correlation and coherence quantification that define a relation between two sets of data, the mDFA method properly reveals the synchronization of multiple groups of neurons in several segments of the spinal cord. This method is envisaged as a useful tool to characterize the structure of higher order ensembles of cord dorsum spontaneous potentials after spinal cord or peripheral nerve lesions.

[1]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[2]  Wentian Li,et al.  Universal 1/f noise, crossovers of scaling exponents, and chromosome-specific patterns of guanine-cytosine content in DNA sequences of the human genome. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  E. Gasteiger,et al.  Observations on the role of afferent and descending impulses on the spontaneous potentials of the spinal cord. , 1953, Electroencephalography and clinical neurophysiology.

[4]  Spatz Wb Unipolar brush cells in marmoset cerebellum and cochlear nuclei express calbindin. , 2000 .

[5]  W. Singer,et al.  Visuomotor integration is associated with zero time-lag synchronization among cortical areas , 1997, Nature.

[6]  G. A. Kerkut,et al.  The isolated mammalian spinal cord , 1995, Progress in Neurobiology.

[7]  Michael J. O'Donovan The origin of spontaneous activity in developing networks of the vertebrate nervous system , 1999, Current Opinion in Neurobiology.

[8]  W. Singer,et al.  Dynamic predictions: Oscillations and synchrony in top–down processing , 2001, Nature Reviews Neuroscience.

[9]  Bioelectric Activity of Spinal Cord in Patients with Vertebrospinal Pathologies , 2001, Bulletin of Experimental Biology and Medicine.

[10]  P. König,et al.  Bi‐directional interactions between visual areas in the awake behaving cat , 2000, Neuroreport.

[11]  N Birbaumer,et al.  Spectral responses in the gamma-band: physiological signs of higher cognitive processes? , 1995, Neuroreport.

[12]  P. König,et al.  Top-down processing mediated by interareal synchronization. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[13]  P. Rudomín,et al.  Changes in correlation between spontaneous activity of dorsal horn neurones lead to differential recruitment of inhibitory pathways in the cat spinal cord , 2012, The Journal of physiology.

[14]  Effects of spinal and peripheral nerve lesions on the intersegmental synchronization of the spontaneous activity of dorsal horn neurons in the cat lumbosacral spinal cord , 2004, Neuroscience Letters.

[15]  José F Fontanari,et al.  Multifractal analysis of DNA walks and trails. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  M. Markus,et al.  On-off intermittency and intermingledlike basins in a granular medium. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Hamid R. Rabiee,et al.  Nonlinear Analysis of Anesthesia Dynamics by Fractal Scaling Exponent , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  J. Cate Spontaneous electrical activity of the spinal cord. , 1950 .

[19]  P. Rudomín,et al.  Modulation of synaptic transmission from segmental afferents by spontaneous activity of dorsal horn spinal neurones in the cat , 2000, The Journal of physiology.

[20]  Y. Sarica,et al.  Studies on the human spontaneous electromyelogram (EMycloG). I. Normal subjects. , 1983, Electroencephalography and clinical neurophysiology.

[21]  Michael J. O'Donovan,et al.  Mechanisms of spontaneous activity in developing spinal networks. , 1998, Journal of neurobiology.

[22]  E. Gasteiger,et al.  Spinal electrogram of freely moving cat: supraspinal and segmental influences. , 1970, Brain research.

[23]  Christoph Braun,et al.  Coherence of gamma-band EEG activity as a basis for associative learning , 1999, Nature.

[24]  L. Telesca,et al.  Long-range correlations in two-dimensional spatio-temporal seismic fluctuations , 2007 .

[25]  Tonic and phasic differential GABAergic inhibition of synaptic actions of joint afferents in the cat , 2006, Experimental Brain Research.

[26]  F. Varela,et al.  Perception's shadow: long-distance synchronization of human brain activity , 1999, Nature.

[27]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[28]  H. Stanley,et al.  Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series , 2002, physics/0202070.

[29]  P. Rudomín,et al.  Intersegmental synchronization of spontaneous activity of dorsal horn neurons in the cat spinal cord , 2003, Experimental Brain Research.

[30]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[31]  Matthias M. Müller,et al.  Processing of affective pictures modulates right-hemispheric gamma band EEG activity , 1999, Clinical Neurophysiology.

[32]  P. D. Wall,et al.  Synchronous inherent oscillations of potentials within the rat lumbar spinal cord , 1996, Neuroscience Letters.