Psychosis defines a category of serious and long-term mental disorders, like schizophrenia, characterised by loss of contact with reality and symptoms such as hallucinations, delusions and thought disorder. Psychosis is extremely expensive to treat and one of the leading causes of disability worldwide.
A data-driven method
We are working on a data-driven method to assess non-verbal synchrony, which is a core component of social functioning and social cognition. Our approach is based on a set of socio-motor biomarkers measured during a joint-action task, where the participant mirrors the movement of a computer avatar. Work carried out in our group reports that such biomarkers can accurately discriminate between patients with chronic schizophrenia and controls. Our proof of concept work demonstrates that data-driven approaches developed in interdisciplinary collaborations between psychiatric epidemiologists and mathematicians have the potential to address healthcare challenges. We are now using our movement-based test in a group at risk of or with newly diagnosed psychosis because they will be largely free of side effects due to chronic illness and/or medication. By further refining our methods and algorithms for detecting subtle differences in movement and coordination we hope to create an individual cognitive deficit-signature which could be used for designing individually tailored therapies.
This project is a pilot study to investigate the use of social movement differences as a diagnostic aid for psychosis, using an interactive virtual reality avatar. Participants are asked to take part in a mirror-movement task which assesses interactional synchrony. This project involves participants from the Bristol Early Intervention for Psychosis team.
Virtual Reality Avatar
We are using a virtual reality avatar to find out if people with first episode psychosis or at risk of psychosis perform differently at a mirror movement task, compared to people not at risk of psychosis.
A new approach to psychosis prediction using data driven tools would add useful information to current methods (psychometric interviews and clinical judgement). More accurate prediction tools would improve outcomes for patients but would also help to allocate NHS resources more effectively. Furthermore, quantitative approaches may reveal new aspects of psychotic aetiology.
Blog written by Sarah Sullivan, Research Fellow, Centre for Academic Primary Care and Centre for Academic Mental Health
Also working on this project: Piotr Slowinski, Research Fellow, College of Engineering, Mathematics and Physical Sciences, University of Exeter.
This project was funded by the Jean Golding Institute Seed Corn Funding Scheme 2018. To find out about other projects supported by this scheme, take a look at the Jean Golding Institute Projects.