From synchronization to network theory: A strategy for MEG data analysis

The functional connectivity of the brain has been studied here using knowledge from two different scientific fields. The methods of Synchronization Likelihood and network theory are applied to magnetoencephalography (MEG) data in an effort to analyze the brain as a complex network. These studies show an interesting small-world phenomenon in functional connectivity. Network and head-map images of the results are presented.

[1]  Jürgen Kurths,et al.  Analysing Synchronization Phenomena from Bivariate Data by Means of the Hilbert Transform , 1998 .

[2]  C. Stam,et al.  Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets , 2002 .

[3]  Luigi Fortuna,et al.  Complex spatio-temporal features in meg data. , 2006, Mathematical biosciences and engineering : MBE.

[4]  M P Young,et al.  Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[5]  Piotr J. Franaszczuk,et al.  An autoregressive method for the measurement of synchronization of interictal and ictal EEG signals , 1999, Biological Cybernetics.

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

[7]  E. Bullmore,et al.  Adaptive reconfiguration of fractal small-world human brain functional networks , 2006, Proceedings of the National Academy of Sciences.

[8]  F. Takens Detecting strange attractors in turbulence , 1981 .

[9]  L. Fortuna,et al.  BioS: a New Tool for Biopotential Experiments , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Cornelis J. Stam,et al.  Nonlinear Brain Dynamics , 2006 .

[11]  S. Strogatz Exploring complex networks , 2001, Nature.

[12]  Theiler,et al.  Spurious dimension from correlation algorithms applied to limited time-series data. , 1986, Physical review. A, General physics.

[13]  D. Tucker,et al.  EEG coherency II: experimental comparisons of multiple measures , 1999, Clinical Neurophysiology.

[14]  Adilson E. Motter,et al.  Dynamics on Complex Networks and Applications , 2006, cond-mat/0612068.

[15]  Olaf Sporns,et al.  Connectivity and complexity: the relationship between neuroanatomy and brain dynamics , 2000, Neural Networks.

[16]  Andrea Mechelli,et al.  A report of the functional connectivity workshop, Dusseldorf 2002 , 2003, NeuroImage.

[17]  Cornelis J Stam,et al.  Graph theoretical analysis of complex networks in the brain , 2007, Nonlinear biomedical physics.

[18]  L. Fortuna,et al.  Complementary Methods for Interpreting Brain Signals: Linear versus Nonlinear Techniques , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  L F Lago-Fernández,et al.  Fast response and temporal coherent oscillations in small-world networks. , 1999, Physical review letters.

[20]  K. Linkenkaer-Hansen,et al.  Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations , 2001, The Journal of Neuroscience.

[21]  Michel Le Van Quyen,et al.  Disentangling the dynamic core: a research program for a neurodynamics at the large-scale , 2003 .

[22]  D. Shannahoff-Khalsa,et al.  Randomized Controlled Trial of Yogic Meditation Techniques for Patients With Obsessive-Compulsive Disorder , 1999, CNS Spectrums.

[23]  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.

[24]  MichaelRosenblum,et al.  Analysing Synchronization Phenomena from Bivariate Data by Means of the Hilbert Transform , 2000 .

[25]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[26]  Holger Kantz,et al.  Practical implementation of nonlinear time series methods: The TISEAN package. , 1998, Chaos.

[27]  D. Shannahoff-Khalsa,et al.  Selective Unilateral Autonomic Activation: Implications for Psychiatry , 2007, CNS Spectrums.