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 Parra; Javier Escudero

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

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Author(s): Alexandra Josefsson¹ ; Agustín Ibáñez^{2, 3, 4, 5, 6} ; Mario Parra^{5, 7} ; Javier Escudero¹
- Affiliations: 1: School of Engineering , Institute for Digital Communications, The University of Edinburgh , EH9 3FB, Edinburgh , UK ;
  2: Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro University , Buenos Aires , Argentina ;
  3: National Scientific and Technical Research Council (CONICET) , Buenos Aires , Argentina ;
  4: Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez , Santiago , Chile ;
  5: Universidad Autónoma del Caribe , Barranquilla , Colombia ;
  6: Centre of Excellence in Cognition and its Disorders, Australian Research Council (ACR) , Sydney , Australia ;
  7: School of Psychological Sciences and Health , University of Strathclyde , Glasgow , UK
Source: Volume 6, Issue 2, April 2019, p. 27 – 31
DOI: 10.1049/htl.2018.5060 , Online ISSN 2053-3713

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This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

Received 11/08/2018, Accepted 03/01/2019, Revised 31/10/2018, Published 29/03/2019

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

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