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

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)221-226
Number of pages6
JournalBrain Topography
Volume23
Issue number2
DOIs
StatePublished - Jun 2010

Keywords

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

ASJC Scopus subject areas

  • Clinical Neurology
  • Anatomy
  • Neurology
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

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