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

Healthcare Technology Letters

article

Alexandra Josefsson¹  Agustín Ibáñez²  Mario Parra⁵  Javier Escudero¹ 

[1] School of Engineering, Institute for Digital Communications, The University of Edinburgh;Institute of Cognitive and Translational Neuroscience (INCYT), INECO Foundation, Favaloro University;National Scientific and Technical Research Council (CONICET);Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez;Universidad Autónoma del Caribe;Centre of Excellence in Cognition and its Disorders, Australian Research Council (ACR);School of Psychological Sciences and Health, University of Strathclyde

关键词: electroencephalography;    medical signal processing;    diseases;    neurophysiology;    brain;    entropy;    cognition;    Alzheimer's disease;    mild cognitive impairment;    electroencephalogram recordings;    brain activity;    high temporal resolution;    brain functional connectivity;    functional network;    beta-filtered EEG recordings;    short-term memory binding task;    MCI patients;    EEG functional connectivity changes;    network differences;    network analysis;    joint-distribution entropy;    early diagnosis;   

DOI  :  10.1049/htl.2018.5060

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

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.

【 授权许可】

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