Characterising Alzheimers Disease with EEG-based Energy Landscape Analysis

Dominik Klepl, Fei He, Min Wu, Matteo De Marco, Daniel Blackburn, Ptolemaios Georgios Sarrigiannis

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
31 Downloads (Pure)

Abstract

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, with around 50 million patients worldwide. Accessible and non-invasive methods of diagnosing and characterising AD are therefore urgently required. Electroencephalography (EEG) fulfils these criteria and is often used when studying AD. Several features derived from EEG were shown to predict AD with high accuracy, e.g. signal complexity and synchronisation. However, the dynamics of how the brain transitions between stable states have not been properly studied in the case of AD and EEG. Energy landscape analysis is a method that can be used to quantify these dynamics. This work presents the first application of this method to both AD and EEG. Energy landscape assigns energy value to each possible state, i.e. pattern of activations across brain regions. The energy is inversely proportional to the probability of occurrence. By studying the features of energy landscapes of 20 AD patients and 20 age-matched healthy counterparts (HC), significant differences are found. The dynamics of AD patients' EEG are shown to be more constrained - with more local minima, less variation in basin size, and smaller basins. We show that energy landscapes can predict AD with high accuracy, performing significantly better than baseline models. Moreover, these findings are replicated in a separate dataset including 9 AD and 10 HC above 70 years old.

Original languageEnglish
Pages (from-to)992-1000
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume26
Issue number3
Early online date18 Aug 2021
DOIs
Publication statusPublished - 1 Mar 2022

Bibliographical note

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Funder

Funding Information: This work was supported by the Alzheimer's Research U.K. under Grant ARUK-PPG20114B-25

Keywords

  • Alzheimer's disease
  • EEG
  • Energy landscape
  • Machine learning
  • Maximum entropy model
  • Network

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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