Wavelet p-Leader Non-Gaussian Multiscale Expansions for EEG series: an Exploratory Study on Cold-Pressor Test

Brain dynamics recorded through electroencephalography (EEG) have been proven to be the output of a nonstationary and nonlinear system. Thus, multifractality of EEG series has been exploited as a useful tool for a neurophysiological characterization in health and disease. However, the role of EEG multifractality under peripheral stress is unknown. In this study, we propose to make use of a novel tool, the recently defined non-Gaussian multiscale analysis, to investigate brain dynamics in the range of 4-8Hz following a cold-pressor test versus a resting state. The method builds on the wavelet p-leader multifractal spectrum to quantify different types of departure from Gaussian and linear properties, and is compared here to standard linear descriptive indices. Results suggest that the proposed non-Gaussian multiscale indices were able to detect expected changes over the somatosensory and premotor cortices, over regions different from those detected by linear analyses. They further indicate that preferred responses for the contralateral somatosensory cortex occur at scales 2.5s and 5s. These findings contribute to the characterization of the so-called central autonomic network, linking dynamical changes at a peripheral and a central nervous system levels.

[1]  P. Abry,et al.  Bootstrap for Empirical Multifractal Analysis , 2007, IEEE Signal Processing Magazine.

[2]  S Seri,et al.  Quantitative EEG modifications during the Cold Water Pressor Test: hemispheric and hand differences. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[3]  Hideki Takayasu,et al.  Fractals in the Physical Sciences , 1990 .

[4]  Anatole Lécuyer,et al.  Exploring two novel features for EEG-based brain-computer interfaces: Multifractal cumulants and predictive complexity , 2010, Neurocomputing.

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

[6]  B. Weiss,et al.  Spatio-temporal analysis of monofractal and multifractal properties of the human sleep EEG , 2009, Journal of Neuroscience Methods.

[7]  Weidong Zhou,et al.  Multifractal Analysis and Relevance Vector Machine-Based Automatic Seizure Detection in Intracranial EEG , 2015, Int. J. Neural Syst..

[8]  D. Popivanov,et al.  Multifractality of decomposed EEG during imaginary and real visual-motor tracking , 2006, Biological Cybernetics.

[9]  T. Vicsek Fractal Growth Phenomena , 1989 .

[10]  C. Stam,et al.  Scale‐free dynamics of global functional connectivity in the human brain , 2004, Human brain mapping.

[11]  Patrice Abry,et al.  Self-similarity and multifractality in human brain activity: A wavelet-based analysis of scale-free brain dynamics , 2018, Journal of Neuroscience Methods.

[12]  Enzo Pasquale Scilingo,et al.  Time-Resolved Directional Brain–Heart Interplay Measurement Through Synthetic Data Generation Models , 2019, Annals of Biomedical Engineering.

[13]  Jian Cui,et al.  Baroreflex modulation of muscle sympathetic nerve activity during cold pressor test in humans. , 2002, American journal of physiology. Heart and circulatory physiology.

[14]  W. Richter,et al.  Distraction Modulates Anterior Cingulate Gyrus Activations during the Cold Pressor Test , 2001, NeuroImage.

[15]  J. Maisog,et al.  Pain intensity processing within the human brain: a bilateral, distributed mechanism. , 1999, Journal of neurophysiology.

[16]  K L Casey,et al.  Forebrain mechanisms of nociception and pain: analysis through imaging. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[17]  D. Wolpert,et al.  Maintaining internal representations: the role of the human superior parietal lobe , 1998, Nature Neuroscience.

[18]  I.Y. Kim,et al.  Multifractal Analysis of Sleep EEG Dynamics in Humans , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[19]  S. Mallat A wavelet tour of signal processing , 1998 .

[20]  Enzo Pasquale Scilingo,et al.  A new Modelling Framework to Study Time-Varying Directional Brain-Heart Interactions: Preliminary Evaluations and Perspectives , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[21]  Q. Wang,et al.  Real-Time Mental Arithmetic Task Recognition From EEG Signals , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[23]  Patrice Abry,et al.  A Wavelet-Based Joint Estimator of the Parameters of Long-Range Dependence , 1999, IEEE Trans. Inf. Theory.

[24]  Lars Arendt-Nielsen,et al.  Dynamic changes and spatial correlation of EEG activities during cold pressor test in man , 2002, Brain Research Bulletin.

[25]  E. Bacry,et al.  Multifractal formalism for fractal signals: The structure-function approach versus the wavelet-transform modulus-maxima method. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[26]  Herwig Wendt,et al.  p-exponent and p-leaders, Part I: Negative pointwise regularity , 2015, 1507.05113.

[27]  Nicola Vanello,et al.  Proneness to social anxiety modulates neural complexity in the absence of exposure: A resting state fMRI study using Hurst exponent , 2015, Psychiatry Research: Neuroimaging.

[28]  Eiichi Watanabe,et al.  Wavelet $p$-Leader Non Gaussian Multiscale Expansions for Heart Rate Variability Analysis in Congestive Heart Failure Patients , 2019, IEEE Transactions on Biomedical Engineering.

[29]  Juliane Britz,et al.  EEG microstate sequences in healthy humans at rest reveal scale-free dynamics , 2010, Proceedings of the National Academy of Sciences.

[30]  Patrice Abry,et al.  Multifractal analysis of fetal heart rate variability in fetuses with and without severe acidosis during labor. , 2011, American journal of perinatology.

[31]  April R. Levin,et al.  The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data , 2018, Front. Neurosci..

[32]  V. Napadow,et al.  The Autonomic Brain: An Activation Likelihood Estimation Meta-Analysis for Central Processing of Autonomic Function , 2013, The Journal of Neuroscience.