BRC Biomedical PhD Student
Brain dynamics as confirmatory biomarker of dementia with Lewy bodies versus Alzheimer’s disease - An electrophysiological study
Ramtin Mehraram
This research focuses on assessing how the electrical activity of the brain is affected in the different forms of dementia. With this research he aims to contribute in a significant way to the research on biomarkers for dementia, and to provide new insights on the different underlying pathologies.
Weighted network measures reveal differences between dementia types: An EEG study – Published and available online: https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.24896
The Study
At early stages, because of their apparent similarities, Dementia with Lewy Bodies (DLB) is often wrongly diagnosed as Alzheimer’s Disease (AD). As clinical scores assess the gravity of the symptoms, biological clues help to determine the type and progression of the disease. These biological clues are called “biomarkers”.
This project relates to the design of a biomarker for dementia with Lewy bodies based on electroencephalography (EEG). EEG consists in the recording of the electrical activity from the brain by placing sensors on the scalp. EEG is advantageous due to its low cost, its easy application and fast analysis of the recorded data.
Recent studies have found some abnormalities related to DLB in the EEG data.
The aims are:
1) To assess the EEG functional connectivity alteration in dementia types
2)To find the source of the abnormalities recorded from the scalp in the brain using a source finding technique.
3)To assess structural alteration of the brain associated with dementia and their correlation with the functional changes
Project milestones
- Literature review on the current findings and developments in EEG and Lewy body diseases including dementia.
- Full network analysis of EEG and frequency analysis for DLB diagnostic differentiation – Identification of patterns
- Structural analysis of grey and white matter from the dementia cohort and standard correlations with EEG network markers.
- Machine learning from the previous two milestones in order to find biomarkers.