Resiliency of EEG-Based Brain Functional Networks

Applying tools available in network science and graph theory to study brain networks has opened a new era in understanding brain mechanisms. Brain functional networks extracted from EEG time series have been frequently studied in health and diseases. In this manuscript, we studied failure resiliency of EEG-based brain functional networks. The network structures were extracted by analysing EEG time series obtained from 30 healthy subjects in resting state eyes-closed conditions. As the network structure was extracted, we measured a number of metrics related to their resiliency. In general, the brain networks showed worse resilient behaviour as compared to corresponding random networks with the same degree sequences. Brain networks had higher vulnerability than the random ones (P < 0.05), indicating that their global efficiency (i.e., communicability between the regions) is more affected by removing the important nodes. Furthermore, the breakdown happened as a result of cascaded failures in brain networks was severer (i.e., less nodes survived) as compared to randomized versions (P < 0.05). These results suggest that real EEG-based networks have not been evolved to possess optimal resiliency against failures.

[1]  S. Shen-Orr,et al.  Networks Network Motifs : Simple Building Blocks of Complex , 2002 .

[2]  David J. Hill,et al.  Attack Vulnerability of Complex Communication Networks , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[3]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[4]  G. Sandini,et al.  Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. , 2009, Brain : a journal of neurology.

[5]  A. F. Westdrop,et al.  Volume conduction effects on correlation analysis of EEG data , 1993, [1993] Proceedings of the Twelfth Southern Biomedical Engineering Conference.

[6]  Mahdi Jalili,et al.  EEG-based functional networks in schizophrenia , 2011, Comput. Biol. Medicine.

[7]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[8]  M. Perc.  Evolution of cooperation on scale-free networks subject to error and attack , 2009, 0902.4661.

[9]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[10]  Edward T. Bullmore,et al.  Connectomics: A new paradigm for understanding brain disease , 2015, European Neuropsychopharmacology.

[11]  Albert-Lszl Barabsi,et al.  Network Science , 2016, Encyclopedia of Big Data.

[12]  E. Bullmore,et al.  A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.

[13]  M. Jalili,et al.  Synchronizability of EEG-Based Functional Networks in Early Alzheimer's Disease , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Boleslaw K. Szymanski,et al.  Cascading Failures in Spatially-Embedded Random Networks , 2013, PloS one.

[15]  Zengqiang Chen,et al.  Error and attack tolerance of evolving networks with local preferential attachment , 2007 .

[16]  Massimo Marchiori,et al.  Economic small-world behavior in weighted networks , 2003 .

[17]  Ulrik Brandes,et al.  What is network science? , 2013, Network Science.

[18]  Ananthram Swami,et al.  Guest Editorial: Network science , 2013, IEEE J. Sel. Areas Commun..

[19]  Danielle S Bassett,et al.  Brain graphs: graphical models of the human brain connectome. , 2011, Annual review of clinical psychology.

[20]  G. Frisoni,et al.  Functional network disruption in the degenerative dementias , 2011, The Lancet Neurology.

[21]  Mahdi Jalili,et al.  Synchronizability of dynamical scale-free networks subject to random errors , 2011 .

[22]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[23]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[24]  Adilson E Motter,et al.  Cascade-based attacks on complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Reto Meuli,et al.  Demyelination of superficial white matter in early Alzheimer's disease: a magnetization transfer imaging study , 2012, Neurobiology of Aging.

[26]  Baharan Mirzasoleiman,et al.  Cascaded failures in weighted networks. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Mahdi Jalili,et al.  Error and attack tolerance of small-worldness in complex networks , 2011, J. Informetrics.

[28]  E. Tolosa,et al.  Functional brain networks and cognitive deficits in Parkinson's disease , 2014, Human brain mapping.

[29]  Mahdi Jalili,et al.  Psychogenic seizures and frontal disconnection: EEG synchronisation study , 2011, Journal of Neurology, Neurosurgery & Psychiatry.

[30]  Adilson E Motter.  Cascade control and defense in complex networks. , 2004, Physical review letters.

[31]  Reto Meuli,et al.  Dysconnection Topography in Schizophrenia Revealed with State-Space Analysis of EEG , 2007, PloS one.

[32]  Edward T. Bullmore,et al.  On the use of correlation as a measure of network connectivity , 2012, NeuroImage.

[33]  Junjing Wang,et al.  Graph theoretical analysis reveals disrupted topological properties of whole brain functional networks in temporal lobe epilepsy , 2014, Clinical Neurophysiology.

[34]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

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

[36]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[37]  D. Tucker,et al.  EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. , 1997, Electroencephalography and clinical neurophysiology.

[38]  Changsong Zhou,et al.  Hierarchical organization unveiled by functional connectivity in complex brain networks. , 2006, Physical review letters.

[39]  Edward T. Bullmore,et al.  Efficiency and Cost of Economical Brain Functional Networks , 2007, PLoS Comput. Biol..

[40]  Nick Chater,et al.  Networks in Cognitive Science , 2013, Trends in Cognitive Sciences.

[41]  Richard S. Frackowiak,et al.  Properties of functional brain networks correlate with frequency of psychogenic non-epileptic seizures , 2012, Front. Hum. Neurosci..

[42]  C. Stam,et al.  Small-world networks and disturbed functional connectivity in schizophrenia , 2006, Schizophrenia Research.

[43]  Neo D. Martinez,et al.  Simple rules yield complex food webs , 2000, Nature.

[44]  Carlos J. Melián,et al.  Complex networks: two ways to be robust? , 2002 .

[45]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[46]  V. Latora,et al.  Efficiency of scale-free networks: error and attack tolerance , 2002, cond-mat/0205601.

[47]  P. Nunez,et al.  EEG and MEG coherence: Measures of functional connectivity at distinct spatial scales of neocortical dynamics , 2007, Journal of Neuroscience Methods.

[48]  C. Stam,et al.  Small-world networks and functional connectivity in Alzheimer's disease. , 2006, Cerebral cortex.

[49]  David J. Hill,et al.  Cascading failure in Watts–Strogatz small-world networks , 2010 .

[50]  Jürgen Kayser,et al.  Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks , 2006, Clinical Neurophysiology.