Low-dimensional attractor for neural activity from local field potentials in optogenetic mice

We used optogenetic mice to investigate possible nonlinear responses of the medial prefrontal cortex (mPFC) local network to light stimuli delivered by a 473 nm laser through a fiber optics. Every 2 s, a brief 10 ms light pulse was applied and the local field potentials (LFPs) were recorded with a 10 kHz sampling rate. The experiment was repeated 100 times and we only retained and analyzed data from six animals that showed stable and repeatable response to optical stimulations. The presence of nonlinearity in our data was checked using the null hypothesis that the data were linearly correlated in the temporal domain, but were random otherwise. For each trail, 100 surrogate data sets were generated and both time reversal asymmetry and false nearest neighbor (FNN) were used as discriminating statistics for the null hypothesis. We found that nonlinearity is present in all LFP data. The first 0.5 s of each 2 s LFP recording were dominated by the transient response of the networks. For each trial, we used the last 1.5 s of steady activity to measure the phase resetting induced by the brief 10 ms light stimulus. After correcting the LFPs for the effect of phase resetting, additional preprocessing was carried out using dendrograms to identify “similar” groups among LFP trials. We found that the steady dynamics of mPFC in response to light stimuli could be reconstructed in a three-dimensional phase space with topologically similar “8”-shaped attractors across different animals. Our results also open the possibility of designing a low-dimensional model for optical stimulation of the mPFC local network.

[1]  Peter A. Tass,et al.  A model of desynchronizing deep brain stimulation with a demand-controlled coordinated reset of neural subpopulations , 2003, Biological Cybernetics.

[2]  Guo Yuan,et al.  Estimating the predictability of an oceanic time series using linear and nonlinear methods , 2004 .

[3]  J. Yorke,et al.  Chaotic behavior of multidimensional difference equations , 1979 .

[4]  P. Uhlhaas,et al.  Working memory and neural oscillations: alpha–gamma versus theta–gamma codes for distinct WM information? , 2014, Trends in Cognitive Sciences.

[5]  Mehmet Emre Çek,et al.  Analysis of observed chaotic data , 2004 .

[6]  G. Nicolis,et al.  Evidence for climatic attractors , 1987, Nature.

[7]  C. Morris,et al.  Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.

[8]  Roger D. Traub,et al.  Simulation of Gamma Rhythms in Networks of Interneurons and Pyramidal Cells , 1997, Journal of Computational Neuroscience.

[9]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[10]  G. Friehs,et al.  Deep brain stimulation of the ventral internal capsule/ventral striatum for obsessive-compulsive disorder: worldwide experience , 2010, Molecular Psychiatry.

[11]  A. Schnitzler,et al.  Normal and pathological oscillatory communication in the brain , 2005, Nature Reviews Neuroscience.

[12]  L Pecora,et al.  Early Seizure Detection , 2001, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[13]  M. Small Applied Nonlinear Time Series Analysis: Applications in Physics, Physiology and Finance , 2005 .

[14]  J. Martinerie,et al.  The brainweb: Phase synchronization and large-scale integration , 2001, Nature Reviews Neuroscience.

[15]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[16]  Leon D. Iasemidis,et al.  Epileptic seizure prediction and control , 2003, IEEE Transactions on Biomedical Engineering.

[17]  Holger Kantz,et al.  Practical implementation of nonlinear time series methods: The TISEAN package. , 1998, Chaos.

[18]  J. Lisman,et al.  The Theta-Gamma Neural Code , 2013, Neuron.

[19]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[20]  Sorinel A Oprisan,et al.  How noise contributes to time-scale invariance of interval timing. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Sorinel Adrian Oprisan,et al.  Local Linear Approximation of the Jacobian Matrix Better Captures Phase Resetting of Neural Limit Cycle Oscillators , 2014, Neural Computation.

[22]  Los AlamOs Nallon Testing for nonlinearity in time series: the method of surrogate data — Source link , 2005 .

[23]  R. Miles,et al.  How Many Subtypes of Inhibitory Cells in the Hippocampus? , 1998, Neuron.

[24]  W. Art Chaovalitwongse,et al.  Adaptive epileptic seizure prediction system , 2003, IEEE Transactions on Biomedical Engineering.

[25]  S. Oprisan REDUCING THE COMPLEXITY OF COMPUTATIONAL MODELS OF NEURONS USING BIFURCATION DIAGRAMS , 2009 .

[26]  E. Poole,et al.  Current practice of clinical electroencephalography D. W. Klass &D. D. Daly, Raven Press, 1979, 544 pp. $61.20 , 1980, Neuroscience.

[27]  Thomas Naselaris,et al.  Optogenetically evoked gamma oscillations are disturbed by cocaine administration , 2013, Front. Cell. Neurosci..

[28]  A. Babloyantz,et al.  Low-dimensional chaos in an instance of epilepsy. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[29]  A. Provenzale,et al.  Finite correlation dimension for stochastic systems with power-law spectra , 1989 .

[30]  Joachim Holzfuss,et al.  Approach to error-estimation in the application of dimension algorithms , 1986 .

[31]  C. Buhusi,et al.  What is all the noise about interval timing? , 2013, BMC Neuroscience.

[32]  Ki-Young Jung,et al.  Nonlinear dynamic characteristics of electroencephalography in a high-dose pilocarpine-induced status epilepticus model , 2003, Epilepsy Research.

[33]  V. Jayaraman,et al.  Encoding and Decoding of Overlapping Odor Sequences , 2006, Neuron.

[34]  V. Jayaraman,et al.  Intensity versus Identity Coding in an Olfactory System , 2003, Neuron.

[35]  Cees Diks,et al.  Reversibility as a criterion for discriminating time series , 1995 .

[36]  Grzegorz Litak,et al.  Cutting process dynamics by nonlinear time series and wavelet analysis. , 2007, Chaos.

[37]  Nenad Koncar,et al.  A note on the Gamma test , 1997, Neural Computing & Applications.

[38]  Sorinel Adrian Oprisan,et al.  The Influence of Limit Cycle Topology on the Phase Resetting Curve , 2002, Neural Computation.

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

[40]  Dimitris Kugiumtzis,et al.  Non-uniform state space reconstruction and coupling detection , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  Steven J. Schiff,et al.  Differentiation of linearly correlated noise from chaos in a biologic system using surrogate data , 1992, Biological Cybernetics.

[42]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[43]  A. Burgess,et al.  Functional connectivity of gamma EEG activity is modulated at low frequency during conscious recollection. , 2002, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[44]  C J Stam,et al.  Nonlinear Dynamical Analysis of Periodic Lateralized Epileptiform Discharges , 1998, Clinical EEG.

[45]  Howell Tong,et al.  Non-linear time series analysis , 2005 .

[46]  Dimitris Kugiumtzis,et al.  Surrogate Data Test on Time Series , 2002 .

[47]  Richard A. Heath,et al.  Nonlinear Dynamics: Techniques and Applications in Psychology , 2000 .

[48]  K. Konstantinou,et al.  Deterministic non-linear source processes of volcanic tremor signals accompanying the 1996 Vatnajökull eruption, central Iceland , 2002 .

[49]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[50]  J. D. Farmer,et al.  State space reconstruction in the presence of noise" Physica D , 1991 .

[51]  Asla Pitkänen,et al.  Epileptic seizure detection: A nonlinear viewpoint , 2005, Comput. Methods Programs Biomed..

[52]  H. Peitgen,et al.  Functional Differential Equations and Approximation of Fixed Points , 1979 .

[53]  James Theiler,et al.  Estimating fractal dimension , 1990 .

[54]  Sorinel Adrian Oprisan,et al.  Technique for eliminating nonessential components in the refinement of a model of dopamine neurons , 2006, Neurocomputing.

[55]  David S. Broomhead,et al.  Phase portraits from a time series: A singular system approach , 1987 .

[56]  Björn Kralemann,et al.  Phase dynamics of coupled oscillators reconstructed from data. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[57]  W. van Emde Boas,et al.  Non-linear analysis of intracranial human EEG in temporal lobe epilepsy , 1999, Clinical Neurophysiology.

[58]  Leonard A. Smith,et al.  Distinguishing between low-dimensional dynamics and randomness in measured time series , 1992 .

[59]  Sorinel Adrian Oprisan,et al.  An application of the least-squares method to system parameters extraction from experimental data. , 2002, Chaos.

[60]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[61]  Kenji Kirihara,et al.  Hierarchical Organization of Gamma and Theta Oscillatory Dynamics in Schizophrenia , 2012, Biological Psychiatry.

[62]  C. Canavier,et al.  Dynamics from a time series: can we extract the phase resetting curve from a time series? , 2003, Biophysical journal.

[63]  R. Eykholt,et al.  Estimating the Lyapunov-exponent spectrum from short time series of low precision. , 1991, Physical review letters.

[64]  F. Takens Detecting strange attractors in turbulence , 1981 .

[65]  B. Efron The jackknife, the bootstrap, and other resampling plans , 1987 .

[66]  Joseph R. Madsen,et al.  Human theta oscillations exhibit task dependence during virtual maze navigation , 1999, Nature.

[67]  T. Schreiber,et al.  Surrogate time series , 1999, chao-dyn/9909037.