Neuroimaging & Metabolic Biomarker Predictors of Recovery from Depression and Psychosis
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. Treatments such as CBT and antidepressant medication can be effective in young people with depression, yet presently it is impossible to accurately predict who will need longer-term interventions or monitoring.
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, the diagnosis of depression is based on the phenomenological evaluation of symptoms and behaviour. However, there remains significant debate about the heterogeneity depressive symptoms and their function as prognostic indicators. Neuroimaging and metabolic markers hold significant diagnostic and prognostic potential to aid these difficulties. Using machine learning can help disentangle complex psychopathology and provide a deeper understanding of the phenomenological profile of depression within psychosis.
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 (https://www.pronia.eu/) 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.
Professor Rachel Upthegrove
Professor Stephen Wood
Professor Nikolaos Koutsouleris
Dr. Lianne Schmaal
Dr. Renate Reniers