Estimating the Asymmetry of Brain Network Organization in Stroke Patients from High-Density EEG Signals

Nadia Mammone, Simona De Salvo, Silvia Marino, Lilla Bonanno, Cosimo Ieracitano, Serena Dattola, Fabio La Foresta, Francesco Carlo Morabito

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Following a stroke, the functional brain connections are impaired, and there is some evidence that the brain tries to reorganize them to compensate the disruption, establishing novel neural connections. Electroencephalography (EEG) can be used to study the effects of stroke on the brain network organization, indirectly, through the study of brain-electrical connectivity. The main objective of this work is to study the asymmetry in the brain network organization of the two hemispheres in case of a lesion due to stroke (ischemic or hemorrhagic), starting from high-density EEG (HD-EEG) signals. The secondary objective is to show how HD-EEG can detect such asymmetry better than standard low-density EEG. A group of seven stroke patients was recruited and underwent HD-EEG recording in an eye-closed resting state condition. The permutation disalignment index (PDI) was used to quantify the coupling strength between pairs of EEG channels, and a complex network model was constructed for both the right and left hemispheres. The complex network analysis allowed to compare the small-worldness (SW) of the two hemispheres. The impaired hemisphere exhibited a larger SW (p < 0.05). The analysis conducted using traditional EEG did not allow to observe such differences. In the future, SW could be used as a biomarker to quantify longitudinal patient improvement.

Original languageEnglish
Title of host publicationSmart Innovation, Systems and Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Pages475-483
Number of pages9
Volume151
Edition2020
DOIs
Publication statusPublished - 2020

Publication series

NameSmart Innovation, Systems and Technologies
Volume151
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Keywords

  • Complex network analysis
  • High-density electroencephalography
  • Permutation disalignment index
  • Stroke

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)

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