Groundbreaking research has shown electroencephalography (EEG) could play a key role in diagnosing Alzheimer’s disease.
Using EEG to analyse brain dynamics, measuring the electrical activity of the brain, has been shown to perform significantly better than alternative baseline diagnosis models and offers high levels of accuracy.
This pioneering research from Coventry University 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.
Alzheimer’s affects 50 million patients worldwide, with predictions that figure could increase by 50 per cent by 2050. Current 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 led the research.
“Our research shows the importance of studying the global dynamics of the brain in characterising neurological disorders, such as Alzheimer’s disease,” says De He.
“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.”
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, such as the activation or depression in different brain regions at a specific time.
The EEG dynamics in those with Alzheimer’s is more constrained than people without the neurological condition, 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 those without Alzheimer’s.
In the future, this approach could be used for analysing other neurological disorders, including Parkinson’s disease, say the research team.
