Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis

TL;DR

This study introduces the first application of energy landscape analysis to EEG data for Alzheimer's disease, revealing distinct brain dynamics and enabling high-accuracy prediction of AD.

Contribution

It pioneers the use of energy landscape analysis on EEG data to characterize AD-related brain dynamics and improve diagnostic prediction accuracy.

Findings

AD patients' brain networks are more constrained with more local minima.

Energy landscape features can predict AD with high accuracy.

Significant differences in energy landscape properties between AD and healthy controls.

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 data. 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 healthy age-matched counterparts, significant differences were found. The dynamics of AD patients' brain networks were 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.