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Alzheimer's disease (AD) is one of the most common neurodegenerativediseases, with around 50 million patients worldwide. Accessible andnon-invasive methods of diagnosing and characterising AD are therefore urgentlyrequired. Electroencephalography (EEG) fulfils these criteria and is often usedwhen studying AD. Several features derived from EEG were shown to predict ADwith high accuracy, e.g. signal complexity and synchronisation. However, thedynamics of how the brain transitions between stable states have not beenproperly studied in the case of AD and EEG data. Energy landscape analysis is amethod that can be used to quantify these dynamics. This work presents thefirst application of this method to both AD and EEG. Energy landscape assignsenergy value to each possible state, i.e. pattern of activations across brainregions. The energy is inversely proportional to the probability of occurrence.By studying the features of energy landscapes of 20 AD patients and 20 healthyage-matched counterparts, significant differences were found. The dynamics ofAD patients' brain networks were shown to be more constrained - with more localminima, less variation in basin size, and smaller basins. We show that energylandscapes can predict AD with high accuracy, performing significantly betterthan baseline models.

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