Depression is the leading cause of disability world-wide with 300 million people being affected. Adolescents and young people are particularly vulnerable to developing this illness. In the UK, the one-year prevalence of depression in adolescents is 5%. While around 60% of young people with depression fully recover, a large proportion will have ongoing difficulties. Depression is also the most common co-morbidity seen with other mental disorders such as psychosis. Complex psychopathology presents clinicians with the challenge of correctly identifying co-morbidities, avoiding misdiagnoses, and tailoring therapeutic options for the individual.
Currently, clinicians treat depression with co-morbid psychosis the same way they treat major depressive disorder on the presumption that they have the same neural basis. Data from the PRONIA study, an EUFP7 funded 8 centre study recruiting recent onset depression and recent onset psychosis participants will be used to identify common and distinct neuroimaging, clinical, and metabolic features of depression across diagnostic groups. Features that provide significant information will be used to build predictive machine learning models that predict recovery from depression with co-morbid psychosis as well as diagnostic machine learning models.
PhD student: Paris Lalousis
Supervisors: Professor Rachel Upthegrove, Professor Stephen Wood, Professor Nikolaos Koutsouleris, Dr. Lianne Schmaal, Dr. Renate Reniers