Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task

Alexandra Josefsson, Agustín Ibáñez, Mario A. Parra, Javier Escudero

Research output: Contribution to journalArticle

Abstract

The early diagnosis of Alzheimer’s disease (AD) is particularly challenging. Mild cognitive impairment (MCI) has been linked to AD and electroencephalogram (EEG) recordings are able to measure brain activity directly with high temporal resolution. In this context, with appropriate processing, the EEG recordings can be used to construct a graph representative of brain functional connectivity. This work studies a functional network created from a non-linear measure of coupling of beta-filtered EEG recordings during a short-term memory binding task. It shows that the values of the small-world characteristic and eccentricity are, respectively, lower and higher in MCI patients than in controls. The results show how MCI leads to EEG functional connectivity changes. They expect that the network differences between MCIs and control subjects could be used to gain insight into the early stages of AD.

LanguageEnglish
Pages27-31
Number of pages5
JournalIET Healthcare Technology Letters
Volume6
Issue number2
Early online date6 Feb 2019
DOIs
Publication statusPublished - 30 Apr 2019

Fingerprint

Entropy
Short-Term Memory
Electroencephalography
Joints
Alzheimer Disease
Brain
Early Diagnosis
Cognitive Dysfunction

Keywords

  • EEG
  • brain connectivity
  • joint-distribution entropy
  • visual short-term memory binding

Cite this

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abstract = "The early diagnosis of Alzheimer’s disease (AD) is particularly challenging. Mild cognitive impairment (MCI) has been linked to AD and electroencephalogram (EEG) recordings are able to measure brain activity directly with high temporal resolution. In this context, with appropriate processing, the EEG recordings can be used to construct a graph representative of brain functional connectivity. This work studies a functional network created from a non-linear measure of coupling of beta-filtered EEG recordings during a short-term memory binding task. It shows that the values of the small-world characteristic and eccentricity are, respectively, lower and higher in MCI patients than in controls. The results show how MCI leads to EEG functional connectivity changes. They expect that the network differences between MCIs and control subjects could be used to gain insight into the early stages of AD.",
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Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task. / Josefsson, Alexandra; Ibáñez, Agustín; Parra, Mario A.; Escudero, Javier.

In: IET Healthcare Technology Letters, Vol. 6, No. 2, 30.04.2019, p. 27-31.

Research output: Contribution to journalArticle

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