The study uses novel statistical approaches to identify and replicate latent constructs within a large and complex dataset. The study focuses on a dimensional psychopathology that cuts across traditional psychiatric diagnoses. The study uses a comprehensive approach across multiple units of analysis for phenotyping.
The final end point of this multilevel latent variable analysis will be standardised assessments that can be developed into clinical tools to help clinicians predict outcomes and select the best intervention for each individual, thereby reducing the burden of mental disorders, and taking psychiatry a step closer towards personalised medicine. The data will be used to generate computational models based on Bayesian statistics. The study goal is to determine how disorders of affect, substance use and eating behaviour organise across different levels of analysis (molecules, genes, cells, neural circuits, physiology, behaviour and self-report) to predict symptom severity, treatment outcome and long-term prognosis. Each participant will undergo interview, behavioural, biomarker and neuroimaging assessments over the course of 1 year. Methods and analysis The T-1000 is a naturalistic study that will recruit, assess and longitudinally follow 1000 participants, including healthy controls and treatment-seeking individuals with mood, anxiety, substance use and eating disorders.
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