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Brain Topography

, Volume 23, Issue 2, pp 221–226 | Cite as

Information Communication Networks in Severe Traumatic Brain Injury

  • Luca Pollonini
  • Swaroop Pophale
  • Ning Situ
  • Meng-Hung Wu
  • Richard E. Frye
  • Jose Leon-Carrion
  • George Zouridakis
Original Paper

Abstract

In this study we explored the use of coherence and Granger causality (GC) to separate patients in minimally conscious state (MCS) from patients with severe neurocognitive disorders (SND) that show signs of awareness. We studied 16 patients, 7 MCS and 9 SND with age between 18 and 49 years. Three minutes of ongoing electroencephalographic (EEG) activity was obtained at rest from 19 standard scalp locations, while subjects were alert but kept their eyes closed. GC was formulated in terms of linear autoregressive models that predict the evolution of several EEG time series, each representing the activity of one channel. The entire network of causally connected brain areas can be summarized as a graph of incompletely connected nodes. The 19 channels were grouped into five gross anatomical regions, frontal, left and right temporal, central, and parieto-occipital, while data analysis was performed separately in each of the five classical EEG frequency bands, namely delta, theta, alpha, beta, and gamma. Our results showed that the SND group consistently formed a larger number of connections compared to the MCS group in all frequency bands. Additionally, the number of connections in the delta band (0.1–4 Hz) between the left temporal and parieto-occipital areas was significantly different (P < 0.1%) in the two groups. Furthermore, in the beta band (12–18 Hz), the input to the frontal areas from all other cortical areas was also significantly different (P < 0.1%) in the two groups. Finally, classification of the subjects into distinct groups using as features the number of connections within and between regions in all frequency bands resulted in 100% classification accuracy of all subjects. The results of this study suggest that analysis of brain connectivity networks based on GC can be a highly accurate approach for classifying subjects affected by severe traumatic brain injury.

Keywords

Severe neurocognitive disorder Minimally conscious state Vegetative state Granger causality Functional connectivity analysis 

Notes

Acknowledgments

This work was supported in part by NSF grant no. 521527, by grants from UH-GEAR, the Institute for Space Systems Operations, and the Texas Learning and Computation Center at the University of Houston, and by a grant from the Center for Brain Injury Rehabilitation (CRECER), Seville, Spain.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Luca Pollonini
    • 1
  • Swaroop Pophale
    • 1
  • Ning Situ
    • 1
  • Meng-Hung Wu
    • 1
  • Richard E. Frye
    • 2
  • Jose Leon-Carrion
    • 3
    • 4
  • George Zouridakis
    • 1
  1. 1.Biomedical Imaging Lab, Departments of Engineering Technology, Computer Science, and Electrical and Computer EngineeringUniversity of HoustonHoustonUSA
  2. 2.Department of Pediatrics, Division of Child and Adolescent Neurology and the Children’s Learning InstituteUniversity of Texas Health Science CenterHoustonUSA
  3. 3.Human Neuropsychology LaboratoryUniversity of SevilleSevilleSpain
  4. 4.Center for Brain Injury Rehabilitation (CRECER)SevilleSpain

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