New thesis about the heterogeneity within Alzheimer's disease
Hi there, Konstantinos Poulakis, PhD student at the Division of Clinical Geriatrics! On May 28 you will defend your thesis “Alzheimer’s disease heterogeneity assessment with MRI biomarkers and unsupervised statistical learning”. What is the main focus of your thesis?
The overall aim of this thesis is to utilize neuroimaging markers together with machine learning methods to define and characterize subtypes of Alzheimer’s disease.
Which are the most important results?
Using longitudinal brain grey matter data, we discovered two main atrophy pathways that are expressed in Alzheimer's disease (mediotemporal vs. cortical). Moreover, using longitudinal brain white matter integrity data we identified and characterized groups of individuals with different white matter aging trajectories and cognitive performance in the population.
How can this new knowledge contribute to the improvement of people’s health?
Our results help to answer some relevant questions about aging of the white and grey matter in the brain and the heterogeneity in Alzheimer's disease. Ultimately, our data can help to identify individuals at risk of future cognitive decline and make personalized future predictions of atrophy and cognition changes in Alzheimer's disease patients, that can assist as a tool for dementia care planning.
What's in the future for you? Will you keep on conducting research?
Yes, I will continue doing research in the field of dementia and neuroimaging.