Exploring Age-Related Changes in Dynamical Non-Stationarity in Electroencephalographic Signals during Early Adolescence

Dynamics of brain signals such as electroencephalogram (EEG) can be characterized as a sequence of quasi-stable patterns. Such patterns in the brain signals can be associated with coordinated neural oscillations, which can be modeled by non-linear systems. Further, these patterns can be quantified through dynamical non-stationarity based on detection of qualitative changes in the state of the systems underlying the observed brain signals. This study explored age-related changes in dynamical non-stationarity of the brain signals recorded at rest, longitudinally with 128-channel EEG during early adolescence (10 to 13 years of age, 56 participants). Dynamical non-stationarity was analyzed based on segmentation of the time series with subsequent grouping of the segments into clusters with similar dynamics. Age-related changes in dynamical non-stationarity were described in terms of the number of stationary states and the duration of the stationary segments. We found that the EEG signal became more non-stationary with age. Specifically, the number of states increased whereas the mean duration of the stationary segment decreased with age. These two effects had global and parieto-occipital distribution, respectively, with the later effect being most dominant in the alpha (around 10 Hz) frequency band.

[1]  Natasa Kovacevic,et al.  Differential Maturation of Brain Signal Complexity in the Human Auditory and Visual System , 2009, Frontiers in human neuroscience.

[2]  E. Fetz,et al.  Coherent 25- to 35-Hz oscillations in the sensorimotor cortex of awake behaving monkeys. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Anthony Randal McIntosh,et al.  Partial least squares analysis of neuroimaging data: applications and advances , 2004, NeuroImage.

[4]  Natasa Kovacevic,et al.  Maturation of EEG power spectra in early adolescence: a longitudinal study. , 2011, Developmental science.

[5]  James A. Roberts,et al.  Biophysical Mechanisms of Multistability in Resting-State Cortical Rhythms , 2011, The Journal of Neuroscience.

[6]  P. Grassberger,et al.  Estimation of the Kolmogorov entropy from a chaotic signal , 1983 .

[7]  Christopher G. Wilson,et al.  The effect of time delay on Approximate & Sample Entropy calculations , 2008 .

[8]  J. Martinerie,et al.  Anticipation of epileptic seizures from standard EEG recordings , 2001, The Lancet.

[9]  Vasily A. Vakorin,et al.  Variability of Brain Signals Processed Locally Transforms into Higher Connectivity with Brain Development , 2011, Journal of Neuroscience.

[10]  Gerhard Werner,et al.  Metastability, criticality and phase transitions in brain and its models , 2007, Biosyst..

[11]  J. Fermaglich.  Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[12]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[13]  D Lehmann,et al.  EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. , 1987, Electroencephalography and clinical neurophysiology.

[14]  Sunil Wimalaratna,et al.  Dynamical Nonstationarity Analysis of Resting EEGs in Alzheimer's Disease , 2007, ICONIP.

[15]  D. Gribkov,et al.  Learning dynamics from nonstationary time series: analysis of electroencephalograms. , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[16]  Hermann Haken,et al.  Exploring the Brain , 2013 .

[17]  A. Fingelkurts,et al.  MAKING COMPLEXITY SIMPLER: MULTIVARIABILITY AND METASTABILITY IN THE BRAIN , 2004, The International journal of neuroscience.

[18]  Robert Savit,et al.  Stationarity and nonstationarity in time series analysis , 1996 .

[19]  Thomas Schreiber,et al.  Classification of Time Series Data with Nonlinear Similarity Measures , 1997 .

[20]  Claude Alain,et al.  Event-related neural activity associated with habit and recollection , 2002, Neuropsychologia.

[21]  A. McIntosh,et al.  Spatiotemporal analysis of experimental differences in event-related potential data with partial least squares. , 2001, Psychophysiology.

[22]  S. Huffel,et al.  Anticipation of epileptic seizures from standard EEG recordings , 2003, The Lancet.

[23]  M. Breakspear,et al.  Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity , 2009, The Journal of Neuroscience.

[24]  Mircea Ariel Schoenfeld,et al.  A multivariate, spatiotemporal analysis of electromagnetic time-frequency data of recognition memory , 2003, NeuroImage.

[25]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[26]  Alexander A. Fingelkurts,et al.  Nonstationary nature of the brain activity as revealed by EEG/MEG: Methodological, practical and conceptual challenges , 2005, Signal Process..

[27]  Dietrich Lehmann,et al.  Millisecond by Millisecond, Year by Year: Normative EEG Microstates and Developmental Stages , 2002, NeuroImage.

[28]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[29]  J. Taylor,et al.  Principles of Brain Functioning , 1999 .

[30]  Klaus-Robert Müller,et al.  Identification of nonstationary dynamics in physiological recordings , 2000, Biological Cybernetics.

[31]  O. Sporns,et al.  Key role of coupling, delay, and noise in resting brain fluctuations , 2009, Proceedings of the National Academy of Sciences.

[32]  J. Velazquez,et al.  EEG nonstationarity during intracranially recorded seizures: statistical and dynamical analysis , 2005, Clinical Neurophysiology.

[33]  Joseph P. LaSalle,et al.  A Course in Mathematical Analysis , 1906, Nature.

[34]  S. Huffel,et al.  Anticipation of epileptic seizures from standard EEG recordings , 2003, The Lancet.

[35]  O. Dunkel,et al.  A Course in Mathematical Analysis , 1904 .

[36]  Natasa Kovacevic,et al.  Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development , 2008, PLoS Comput. Biol..

[37]  R. Kötter,et al.  Cortical network dynamics with time delays reveals functional connectivity in the resting brain , 2008, Cognitive Neurodynamics.