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.

Highlights

  • It is useful to identify people with mild cognitive impairment (MCI) as they would be at higher risks of developing Alzheimer’s disease (AD)

  • We show 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

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Summary

Introduction

It is useful to identify people with mild cognitive impairment (MCI) as they would be at higher risks of developing Alzheimer’s disease (AD). This is important given that, nowadays, 35.6 million people live with dementia worldwide [1], posing a big strain on the economy and society. Further understanding of MCI could contribute to developing an earlier diagnosis of AD, something that could help dementia patients and their caregivers to make better, informed decisions about their lives. An earlier diagnosis means earlier access to information and support [2]. The earlier diagnosis means that patients can take part in further research [2]

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