There are many symptoms that are commonly seen across a number of different psychiatric disorders such as changes in sleep, mood, and motivation. The aim of this project is to differentiate between shared connectivity abnormalities (linked to common symptoms), and unique markers of disease.
Using a variety of existing datasets, this project involves the development of advanced data normalization tools.
Around 70% of patients suffering from depression do not respond to the first treatment option they try. This project aims to identify target connectivity markers to predict treatment response in Major Depressive Disorder, using population data from the UK Biobank to start.
A large number of physiological changes are associated with depression, including body weight changes, sleep changes, inflammation, and heart rate variability. This project will address these data confounds with advanced data analysis approaches.