Researchers develop a new approach to improve future diagnosis of Alzheimer’s Disease

Tuesday 27 September 2022

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Coventry University researchers have developed a new tool which could help diagnose Alzheimer’s Disease.

Alzheimer’s Disease (AD) is one of the most common neurodegenerative diseases, affecting 50 million patients worldwide and that figure is expected to increase by 50% by 2050.

Current AD diagnosis methods like cognitive, physical and radiological assessments can be often subjective, time-consuming and invasive to the patient.

Through this research it is hoped that patient experiences will be enhanced by receiving a more accurate and quicker diagnosis.

Dr Fei He and Dominik Klepl, researchers within the Centre for Computational Science and Mathematical Modelling at Coventry University, have developed a unique diagnosis approach which analyses the brain dynamics from electroencephalography (EEG) signals which measure brain electrical activity.

This ground-breaking research uses an energy landscape concept from statistical physics to model the patients’ EEG signals and suggests that the findings could be used to improve diagnosis of Alzheimer’s Disease.

This approach performs significantly better than alternative baseline diagnosis models and offers high levels of accuracy.

The energy landscape of the brain is a method of analysis that can be used to quantify the dynamics of brain transitions between stable states. These brain states illustrate different patterns of brain activities, i.e., the activation or depression in different brain regions at a specific time.

The EEG dynamics in those suffering with Alzheimer’s is more constrained than non-sufferers, with the energy landscape of the brain showing more localised activity.

The results indicate that Alzheimer’s patients’ EEG signals are less complex, showing the increased difficulty of changing between brain states in comparison to non-sufferers.

In the future, this approach could be used for analysing other neurological disorders, including Parkinson’s disease.

Dr Fei He said: 

Our research shows the importance of studying the global dynamics of the brain in characterising neurological disorders, such as Alzheimer’s Disease. The energy landscape technique together with EEG could offer promising tools to support the diagnosis and characterise the severity of Alzheimer’s Disease of a patient. This work also demonstrates the importance of multi-disciplinary research, such as integrating techniques from statistical physics, signal processing and machine learning, in tackling global challenges like neurodegenerative disease.

Dr Fei He, Assistant Professor, Research Centre for Computational Science and Mathematical Modelling

This research forms part of a collaborative project, featuring: Dr Ptolemaios G. Sarrigiannis, Department of Neurophysiology, Royal Devon and Exeter NHS Foundation Trust, Dr Daniel J. Blackburn, Department of Neuroscience, University of Sheffield, Dr Min Wu, Institute for Infocomm Research, A*STAR, Singapore and Dr Matteo De Marco, Department of Life Sciences, Brunel University London.

For more details see the recent article, which was selected for the cover of IEEE Journal of Biomedical and Health Informatics:

Discover more about the Centre for Computational Science and Mathematical Modelling.