Cognitive and neural precursors of mental health problems in youth

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Poor affective-control characterises most mental health problems. Yet little is known about its development. This project investigates how performance on two experimental measures of affective-control relate to the development of mental health symptoms (specifically, anxiety, depression, suicidality) in data (N=20,000) from the 2-year Future Proofing project. This unique project links birth data to symptom development in 12-14 year-olds. The candidate will additionally have the opportunity to look at how brain development, assessed in a second existing longitudinal dataset (N=100), relates to affective-control and symptoms of mental health at age 16-17years. These datasets offer an exceptional opportunity for a motivated student.

The ideal candidate will have a background in one or more of the following fields cognitive science, psychology, neuroscience, developmental science, computational biology, bioinformatics, computer science or engineering with a strong interest in cognition and mental health. The candidate will also demonstrate strong organisational skills and an ability to communicate in an interdisciplinary team of both national and international collaborators. Previous research experience and programming experience are desirable.

The candidate can expect to develop an excellent understanding of the interactions between cognitive functioning and mental health, in particular the role of cognition in the onset of mental health problems. The candidate will also develop expertise in several data modelling techniques including machine learning, structural equation modelling and mixed-linear modelling. Throughout the PhD the candidate will be given the opportunity to develop a strong understanding and experience in translational research and science communication.

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Black Dog Institute

Computer Science and Engineering