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

Tools

Josefsson, Alexandra and Ibáñez, Agustín and Parra, Mario A. and Escudero, Javier (2019) Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task. IET Healthcare Technology Letters, 6 (2). pp. 27-31. ISSN 2053-3713 (https://doi.org/10.1049/htl.2018.5060)

[thumbnail of Josefsson-etal-HTL2019-Network-analysis-through-the-use-of-joint-distribution-entropy-on-EEG]
Preview
 Text. Filename: Josefsson_etal_HTL2019_Network_analysis_through_the_use_of_joint_distribution_entropy_on_EEG.pdf
Accepted Author Manuscript
Download (316kB)| Preview

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.

ORCID iDs

Josefsson, Alexandra, Ibáñez, Agustín, Parra, Mario A. ORCID logoORCID: https://orcid.org/0000-0002-2412-648X and Escudero, Javier;
CORE (COnnecting REpositories)