Analysis of the connection redundancy in functional networks from high-resolution EEG: A preliminary study

In the present study, we propose a theoretical graph procedure to investigate the communication redundancy in brain networks. By taking into account all the possible paths between pairs of cortical regions, this method captures the network redundancy i.e. a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. As an example for its potential, we apply this procedure to the cortical networks estimated from high-resolution EEG signals in a group of spinal cord injured patients during the attempt of the foot movement. Preliminary results suggest that in the high spectral contents the effects due to the spinal trauma affect the expected redundancy attitude by suppressing mainly the longer alternative pathways between the cortical regions.

[1]  Olaf Sporns,et al.  Graph Theory Methods for the Analysis of Neural Connectivity Patterns , 2003 .

[2]  J. Le,et al.  Method to reduce blur distortion from EEG's using a realistic head model , 1993, IEEE Transactions on Biomedical Engineering.

[3]  C. Stam,et al.  Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis , 2006, Neuroscience Letters.

[4]  Luciano da Fontoura Costa,et al.  Superedges: Connecting Structure and Dynamics in Complex Networks , 2008 .

[5]  F. Babiloni,et al.  Brain Network Analysis From High-Resolution EEG Recordings by the Application of Theoretical Graph Indexes , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[7]  P. Rossini,et al.  High-resolution electro-encephalogram: source estimates of Laplacian-transformed somatosensory-evoked potentials using a realistic subject head model constructed from magnetic resonance images , 2000, Medical and Biological Engineering and Computing.

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

[9]  A Gevins,et al.  High resolution EEG: 124-channel recording, spatial deblurring and MRI integration methods. , 1994, Electroencephalography and clinical neurophysiology.

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

[11]  G. Cecchi,et al.  Scale-free brain functional networks. , 2003, Physical review letters.

[12]  H. Duffau.  Brain plasticity: From pathophysiological mechanisms to therapeutic applications , 2006, Journal of Clinical Neuroscience.

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

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

[15]  Luciano da Fontoura Costa,et al.  Shape Analysis and Classification: Theory and Practice , 2000 .

[16]  C. Stam,et al.  Disturbed functional connectivity in brain tumour patients: Evaluation by graph analysis of synchronization matrices , 2006, Clinical Neurophysiology.

[17]  E. Bullmore,et al.  Neurophysiological architecture of functional magnetic resonance images of human brain. , 2005, Cerebral cortex.

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

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

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