Profiling and Prediction in Youth Depression and Suicide

A key focus of this research program is addressing the key issues of heterogeneity and poor predictive ability in youth depression and suicidal behaviours by defining profiles or concepts of youth mental illness that are grounded in underlying biological mechanisms, as well as by developing prediction algorithms for disease onset/progression, treatment response and suicide attempts. This research integrates clinical, psychosocial, neurobiological and genetic data through computational modeling and machine learning methods.

Current projects include:

ENIGMA Major Depressive Disorder (MDD) consortium

Led by A/Prof. Schmaal, the ENIGMA MDD consortium is a collaboration between over 30 research institutes from 14 different countries worldwide that share neuroimaging data from over 3,200 people with MDD and 8,000 healthy individuals. ENIGMA MDD aims to combine datasets in order to increase statistical power to further elucidate brain alterations associated with depression at different stages of life and different stages of disease (http://enigma.ini.usc.edu/ongoing/enigma-mdd-working-group/).
An example of a current project is a project that aims to identify subtypes of depression based on brain structural alterations.

MQ Help Overcome and Prevent the Emergence of Suicide (HOPES)

Globally, suicide is the second most common cause of death for adolescents and young adults. To improve preventative intervention treatment for suicide thoughts and behaviours, it is critical to identify neurobiological mechanisms and psychosocial risk factors that confer increased risk. Our research includes a multidisciplinary international research consortium that aims to elucidate neurobiological mechanisms and behavioural phenotypes that underlie risk for suicide thoughts and behaviours during adolescence. With funding from the MQ, we recently initiated the ENIGMA Suicidal Thoughts and Behaviours (STB) consortium, co-led by Dr. Schmaal. ENIGMA STB pools existing neuroimaging and clinical data from approximately 23,000 individuals with and without mental disorders worldwide. Examples of current projects include projects aimed at identifying the neurobiological and transdiagnostic mechanisms underlying suicidal ideation and attempts in young people with mental disorders, identifying novel suicide risk subtypes based on different configurations of biopsychosocial risk factors and developing clinical support tools for the prediction of future suicide attempts based on biopsychosocial measures.


Profiling and prediction of youth depression

Current classifications of mental illness have low validity and specificity in young people. A major issue of current diagnostic criteria is that they are agnostic about underlying biological mechanisms. This research aims to identify homogeneous and biologically based profiles of youth depression and evaluate their predictive value for response to different treatments.


Profiling of youth affective disorders

The diagnosis of adolescent affective disorders currently relies on symptom-based classification schemes. Drawbacks of current symptom-based classifications are that they assume mental disorders are discrete and dissociable entities, and that they are agnostic about underlying biological mechanisms. There is a clear need for developing an alternative neurobiologically informed diagnostic framework for adolescent depression and anxiety. Our research aims to establish the neurobiological correlates underlying variation in disease profile and disease course of affective disorders in young people.