Brain Topography

, Volume 23, Issue 4, pp 344–354 | Cite as

Multiple Pathways Analysis of Brain Functional Networks from EEG Signals: An Application to Real Data

  • Fabrizio De Vico Fallani
  • Francisco Aparecido Rodrigues
  • Luciano da Fontoura Costa
  • Laura Astolfi
  • Febo Cincotti
  • Donatella Mattia
  • Serenella Salinari
  • Fabio Babiloni
Original Paper


In the present study, we propose a theoretical graph procedure to investigate multiple pathways in brain functional networks. By taking into account all the possible paths consisting of h links between the nodes pairs of the network, we measured the global network redundancy R h as the number of parallel paths and the global network permeability P h as the probability to get connected. We used this procedure to investigate the structural and dynamical changes in the cortical networks estimated from a dataset of high-resolution EEG signals in a group of spinal cord injured (SCI) patients during the attempt of foot movement. In the light of a statistical contrast with a healthy population, the permeability index P h of the SCI networks increased significantly (P < 0.01) in the Theta frequency band (3–6 Hz) for distances h ranging from 2 to 4. On the contrary, no significant differences were found between the two populations for the redundancy index R h . The most significant changes in the brain functional network of SCI patients occurred mainly in the lower spectral contents. These changes were related to an improved propagation of communication between the closest cortical areas rather than to a different level of redundancy. This evidence strengthens the hypothesis of the need for a higher functional interaction among the closest ROIs as a mechanism to compensate the lack of feedback from the peripheral nerves to the sensomotor areas.


Cortical networks Graph theory Redundancy Permeability Spinal cord injury 



This study was performed with the support of the COST EU project NEUROMATH (BM 0601) and supported partially by the European ICT Programme Project FP7-224631. This paper only reflects the authors’ views and funding agencies are not liable for any use that may be made of the information contained herein. Luciano da F. Costa thanks CNPq (301303/06-1) and FAPESP (05/00587-5) for sponsorship. Francisco Aparecido Rodrigues is grateful to FAPESP (07/50633-9).


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Fabrizio De Vico Fallani
    • 1
    • 2
  • Francisco Aparecido Rodrigues
    • 3
  • Luciano da Fontoura Costa
    • 5
  • Laura Astolfi
    • 1
    • 4
  • Febo Cincotti
    • 1
  • Donatella Mattia
    • 1
  • Serenella Salinari
    • 4
  • Fabio Babiloni
    • 1
    • 2
  1. 1.Laboratory of Clinical NeurophysiopathologyIRCCS “Fondazione Santa Lucia”RomeItaly
  2. 2.Department of Human Physiology and PharmacologyUniversity “Sapienza”RomeItaly
  3. 3.Departamento de Matemática Aplicada e Estatística, Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São PauloSão PauloBrazil
  4. 4.Department of Informatica e SistemisticaUniversity “Sapienza”RomeItaly
  5. 5.Instituto de Física de São CarlosUniversidade de São PauloSão PauloBrazil

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