Functional modularity of background activities in normal and epileptic brain networks.

We analyze the connectivity structure of weighted brain networks extracted from spontaneous magnetoencephalographic signals of healthy subjects and epileptic patients (suffering from absence seizures) recorded at rest. We find that, for the activities in the 5-14 Hz range, healthy brains exhibit a sparse connectivity, whereas the brain networks of patients display a rich connectivity with a clear modular structure. Our results suggest that modularity plays a key role in the functional organization of brain areas during normal and pathological neural activities at rest.

[1]  David Gfeller,et al.  Spectral coarse graining of complex networks. , 2007, Physical review letters.

[2]  C. Stam,et al.  Indications for network regularization during absence seizures: Weighted and unweighted graph theoretical analyses , 2009, Experimental Neurology.

[3]  C. J. Stam,et al.  Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network? , 2004, Neuroscience Letters.

[4]  D. Brillinger.  Time series - data analysis and theory , 1981, Classics in applied mathematics.

[5]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[6]  William Gaetz,et al.  Enhanced Synchrony in Epileptiform Activity? Local versus Distant Phase Synchronization in Generalized Seizures , 2005, The Journal of Neuroscience.

[7]  Aarnout Brombacher,et al.  Probability... , 2009, Qual. Reliab. Eng. Int..

[8]  Srinivasan Parthasarathy,et al.  Proceedings of the Seventh SIAM International Conference on Data Mining, April 26-28, 2007, Minneapolis, Minnesota, USA , 2007, SDM.

[9]  R. Guimerà,et al.  Functional cartography of complex metabolic networks , 2005, Nature.

[10]  O. Sporns,et al.  Organization, development and function of complex brain networks , 2004, Trends in Cognitive Sciences.

[11]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[12]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[13]  Ericka Stricklin-Parker,et al.  Ann , 2005 .

[14]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Tzyy-Ping Jung,et al.  Imaging brain dynamics using independent component analysis , 2001, Proc. IEEE.

[16]  P. Steerenberg,et al.  Targeting pathophysiological rhythms: prednisone chronotherapy shows sustained efficacy in rheumatoid arthritis. , 2010, Annals of the rheumatic diseases.

[17]  Sujit K Sikdar,et al.  Small‐world network topology of hippocampal neuronal network is lost, in an in vitro glutamate injury model of epilepsy , 2007, The European journal of neuroscience.

[18]  John Hunter,et al.  Functional holography analysis: simplifying the complexity of dynamical networks. , 2006, Chaos.

[19]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[20]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[21]  Bruno O. Shubert,et al.  Random variables and stochastic processes , 1979 .

[22]  R. Solé,et al.  Spontaneous emergence of modularity in cellular networks , 2008, Journal of The Royal Society Interface.

[23]  Richard M. Leahy,et al.  A comparison of random field theory and permutation methods for the statistical analysis of MEG data , 2005, NeuroImage.

[24]  K. Sneppen,et al.  Specificity and Stability in Topology of Protein Networks , 2002, Science.

[25]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[26]  Andrew G. Glen,et al.  APPL , 2001 .

[27]  C. Stam,et al.  Nonlinear synchronization in EEG and whole‐head MEG recordings of healthy subjects , 2003, Human brain mapping.

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