Resting-state directed brain connectivity patterns in adolescents from source-reconstructed EEG signals based on information flow rate

Quantifying the brain’s effective connectivity offers a unique window onto the causal architecture coupling the different regions of the brain. Here, we advocate a new, data-driven measure of directed (or effective) brain connectivity based on the recently developed information flow rate coefficient. The concept of the information flow rate is founded in the theory of stochastic dynamical systems and its derivation is based on first principles; unlike various commonly used linear and nonlinear correlations and empirical directional coefficients, the information flow rate can measure causal relations between time series with minimal assumptions. We apply the information flow rate to electroencephalography (EEG) signals in adolescent males to map out the directed, causal, spatial interactions between brain regions during resting-state conditions. To our knowledge, this is the first study of effective connectivity in the adolescent brain. Our analysis reveals that adolescents show a pattern of information flow that is strongly left lateralized, and consists of short and medium ranged bidirectional interactions across the frontal-central-temporal regions. These results suggest an intermediate state of brain maturation in adolescence.

[1]  X. Liang,et al.  Unraveling the cause-effect relation between time series. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[3]  X. Liang,et al.  Information flow and causality as rigorous notions ab initio. , 2015, Physical review. E.

[4]  Karl J. Friston Functional and effective connectivity in neuroimaging: A synthesis , 1994 .

[5]  Hiroki Sayama,et al.  Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood , 2015, NeuroImage.

[6]  Jan-Mathijs Schoffelen,et al.  A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls , 2016, Front. Syst. Neurosci..

[7]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[8]  Wei Liao,et al.  Nonlinear connectivity by Granger causality , 2011, NeuroImage.

[9]  D. Long Networks of the Brain , 2011 .

[10]  Cornelis J. Stam,et al.  The Brain Matures with Stronger Functional Connectivity and Decreased Randomness of Its Network , 2012, PloS one.

[11]  Vangelis Sakkalis,et al.  Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG , 2011, Comput. Biol. Medicine.

[12]  Naznin Virji-Babul,et al.  Alterations in resting-state brain networks in concussed adolescent athletes. , 2015, Journal of neurotrauma.

[13]  S. Bressler,et al.  Granger Causality: Basic Theory and Application to Neuroscience , 2006, q-bio/0608035.

[14]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[15]  N. Virji-Babul,et al.  Changes in brain-behavior relationships following a 3-month pilot cognitive intervention program for adults with traumatic brain injury , 2017, Heliyon.

[16]  W. Hesse,et al.  The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies , 2003, Journal of Neuroscience Methods.

[17]  M. Greicius,et al.  Resting-state functional connectivity reflects structural connectivity in the default mode network. , 2009, Cerebral cortex.

[18]  Vince D. Calhoun,et al.  Lateralization of resting state networks and relationship to age and gender , 2015, NeuroImage.

[19]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[20]  Richard N. Aslin,et al.  Top-down modulation in the infant brain: Learning-induced expectations rapidly affect the sensory cortex at 6 months , 2015, Proceedings of the National Academy of Sciences.

[21]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[22]  X. San Liang,et al.  The Liang-Kleeman Information Flow: Theory and Applications , 2013, Entropy.

[23]  M. Mintun,et al.  Brain work and brain imaging. , 2006, Annual review of neuroscience.

[24]  X San Liang,et al.  Information flow within stochastic dynamical systems. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  M. Lindquist The Statistical Analysis of fMRI Data. , 2008, 0906.3662.

[26]  K. G. Mideksa,et al.  EEG-MEG Integration Enhances the Characterization of Functional and Effective Connectivity in the Resting State Network , 2015, PloS one.

[27]  X. Liang,et al.  Causation and information flow with respect to relative entropy. , 2018, Chaos.

[28]  G. Rangarajan,et al.  Multiple Nonlinear Time Series with Extended Granger Causality , 2004 .

[29]  Edward T. Bullmore,et al.  Conditional Mutual Information Maps as Descriptors of Net Connectivity Levels in the Brain , 2010, Front. Neuroinform..

[30]  C. Stam,et al.  The organization of physiological brain networks , 2012, Clinical Neurophysiology.

[31]  Gordon Pipa,et al.  Transfer entropy—a model-free measure of effective connectivity for the neurosciences , 2010, Journal of Computational Neuroscience.

[32]  Ying Liu,et al.  Quantification of Effective Connectivity in the Brain Using a Measure of Directed Information , 2012, Comput. Math. Methods Medicine.

[33]  X. Liang,et al.  Normalizing the causality between time series. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  W. Klonowski Everything you wanted to ask about EEG but were afraid to get the right answer , 2009, Nonlinear biomedical physics.

[35]  R. Quiroga,et al.  Stationarity of the EEG series , 1995 .

[36]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[37]  Barry Horwitz,et al.  The elusive concept of brain connectivity , 2003, NeuroImage.

[38]  C. Stam,et al.  Direction of information flow in large-scale resting-state networks is frequency-dependent , 2016, Proceedings of the National Academy of Sciences.

[39]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[40]  Walter Schneider,et al.  Empirical validation of directed functional connectivity , 2017, NeuroImage.

[41]  X. Liang Local predictability and information flow in complex dynamical systems , 2013 .

[42]  Pan Lin,et al.  Dynamic Default Mode Network across Different Brain States , 2017, Scientific Reports.

[43]  Michael X Cohen,et al.  Analyzing Neural Time Series Data: Theory and Practice , 2014 .

[44]  Rainer Goebel,et al.  Mapping directed influence over the brain using Granger causality and fMRI , 2005, NeuroImage.

[45]  Md. Hedayetul Islam Shovon,et al.  Transfer Entropy and Information Flow Patterns in Functional Brain Networks during Cognitive Activity , 2014, ICONIP.

[46]  Karl J. Friston Functional and Effective Connectivity: A Review , 2011, Brain Connect..

[47]  Virji-BabulNaznin,et al.  Changes in functional brain networks following sports-related concussion in adolescents. , 2014 .

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

[49]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[50]  Richard Kleeman,et al.  Information transfer between dynamical system components. , 2005, Physical review letters.

[51]  Z. Jane Wang,et al.  Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm , 2009, J. Mach. Learn. Res..

[52]  Abraham Z. Snyder,et al.  A brief history of the resting state: The Washington University perspective , 2012, NeuroImage.

[53]  Paul Marriott,et al.  Using Phase Shift Granger Causality to Measure Directed Connectivity in EEG Recordings , 2014, Brain Connect..

[54]  N. Wenderoth,et al.  Detecting large‐scale networks in the human brain using high‐density electroencephalography , 2017, Human brain mapping.

[55]  Patrick Berg,et al.  Advanced Tools for Digital EEG Review:: Virtual Source Montages, Whole-head Mapping, Correlation, and Phase Analysis , 2002, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[56]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[57]  Steven L. Bressler,et al.  Wiener–Granger Causality: A well established methodology , 2011, NeuroImage.

[58]  Lei Ding,et al.  Reconstructing Large-Scale Brain Resting-State Networks from High-Resolution EEG: Spatial and Temporal Comparisons with fMRI , 2016, Brain Connect..

[59]  K. Amunts,et al.  Towards multimodal atlases of the human brain , 2006, Nature Reviews Neuroscience.

[60]  M J Taylor1,et al.  Top-down modulation of early selective attention processes in children. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[61]  T. Paus Mapping brain maturation and cognitive development during adolescence , 2005, Trends in Cognitive Sciences.

[62]  C. Lebel,et al.  Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood , 2011, The Journal of Neuroscience.

[63]  A. Seth,et al.  Granger causality and transfer entropy are equivalent for Gaussian variables. , 2009, Physical review letters.

[64]  A. Seth,et al.  Granger Causality Analysis in Neuroscience and Neuroimaging , 2015, The Journal of Neuroscience.

[65]  F. Gonzalez-Lima,et al.  Structural equation modeling and its application to network analysis in functional brain imaging , 1994 .

[66]  H. Kennedy,et al.  Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels , 2014, Neuron.

[67]  Thomas F. Nugent,et al.  Dynamic mapping of human cortical development during childhood through early adulthood. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[68]  Courtney G. E. Hilderman,et al.  Network analysis of perception-action coupling in infants , 2014, Front. Hum. Neurosci..

[69]  F. Perrin,et al.  Spherical splines for scalp potential and current density mapping. , 1989, Electroencephalography and clinical neurophysiology.

[70]  Daniel Brandeis,et al.  Developmental changes of functional and directed resting-state connectivities associated with neuronal oscillations in EEG , 2013, NeuroImage.

[71]  Motoaki Kawanabe,et al.  Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis , 2016, PloS one.

[72]  T. Oberlander,et al.  Infants and adults have similar regional functional brain organization for the perception of emotions , 2017, Neuroscience Letters.

[73]  Mingzhou Ding,et al.  Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance , 2001, Biological Cybernetics.

[74]  Joseph T. Lizier,et al.  An Introduction to Transfer Entropy: Information Flow in Complex Systems , 2016 .