Pre-onset detection of Alzheimer's Disease (AD) by investigating brain dynamics

Problem Statement: Among all the types of dementia, Alzheimer's disease (AD) is the most common form with 70 % of those affected by dementia having AD. As the prevalence of AD increases with age, the number of people living with AD is expected to rise over the next decades due to better quality of life that results in increase in age across many countries. All this has resulted in an increased focus on ensuring pre-onset detection of AD and the corresponding intervention, which can lead to slowing the progression of the disease by providing adequate diagnostics.

Issues with Current Solutions: Preclinical AD happens 10 to 15 years before the onset of the disease resulting in changes in the brain without showing any actual symptoms of the disease like memory loss etc. Pre-onset means detecting AD in or before the preclinical stage. The existing state-of-the-art methods mainly focus on the detection of later stages of AD, and the detection of preclinical AD is still an open research problem. Hence, this research targets pre-onset detection of AD (that is, early detection of Preclinical AD) because that will have huge impact on the lives of people. This can lead to early intervention and may result in further slowing the progression of the disease.

Challenges:  At the stage of preclinical AD, the related signs and symptoms are not clear, and hence people at this stage do not seek any help. Therefore, a method for pre-onset detection of AD should be part of the regular health screening process and hence should be available in small clinical setups.

Solution: Method for detection of preclinical AD will involve investigating underlying brain mechanisms to monitor and track changes related to pre-onset detection of AD. Magnetic resonance imaging (MRI) will be used as a reference to investigate the brain dynamics however it cannot be used in practice due to its high-cost and specialized setup environment which limits its usage at the screening stage. Electroencephalogram (EEG) will be used in this research which is widely available, is low cost, has a good temporal resolution, and has high mobility. Therefore, this project aims to investigate the changes in underlying brain mechanisms using EEG to develop EEG-based neuromarker for pre-onset detection of AD. The neuromarker will involve the features extraction and corresponding development of the machine learning model.

Few Words About Supervision: I have recently moved to FIT at Brno University of Technology. I have decade long experience of working in the field of neuro-signal and neuroimage processing and I am currently in the process of setting up a research group in this area. This is a multidisciplinary project and it will involve working with clinicians. However, the core of the project is related to IT in terms of development of a new method. Please feel free to contact me at malik@fit.vutbr.cz