Title: Predicting psychosis-onset and symptomatic relapse through online assessment of belief-updating and speech organisation
Funder: The Academy of Medical Sciences

Abstract: I aim to use large-scale, online testing to determine whether altered belief-updating and disorganised speech can predict psychosis-onset in people at clinical high risk of psychosis, and relapse in people with a first psychotic episode. If successful, this approach would allow for stratification of treatments to people who are in most need which can improve clinical outcomes and thus reduce health and societal costs. I will also compare belief-updating and speech in the clinical groups to a group of healthy controls to determine if the findings replicate findings from smaller laboratory-based studies. Belief-updating and speech organisation are important candidate markers for predicting outcomes in psychosis. The key positive symptoms of psychosis, hallucinations and delusions are thought to arise from the way in which people update beliefs, whereas disorganised speech is a sensitive early marker of formal thought disorder, a disabling symptom of psychosis. Importantly, belief-updating and speech can be measured online which makes these cognitive markers accessible and cost-effective, thus allowing for their collection at scale. I will capitalise on a novel multicentre study to test 150 people at clinical high risk of psychosis and 150 with a fist episode of psychosis. Clinical outcome measures will be obtained from this large multicentre study, and from patients’ health records, with their explicit consent. I will use machine learning analyses to determine if altered belief-updating and speech can predict psychosis-onset at the level of the individual in people at clinical high risk, and relapse in people with a first episode of psychosis.

Lay abstract: Psychosis is a severe mental health condition that is characterised by odd perceptions (hallucinations), beliefs (delusions) and confused thoughts. To mitigate the ill consequences of psychotic disorders on people’s life, the NHS and the economy, clinicians increasingly focus on early intervention. Here, the aim is to prevent psychosis from occurring in people at risk of the condition, and to prevent a recurrence of symptoms, called relapse, in treated people with a first episode of psychosis. However, we have to further improve our ability to predict psychosis-onset and relapse. This requires researchers to build on mechanisms known to underlie psychosis and determine whether these can sensitively detect whether someone will develop psychosis or experience relapse. Previous work consistently showed that hallucinations and delusions might be the end results of changes in the way that people update their beliefs, whereas confused thoughts can be detected early from subtle changes in speech. Belief-updating and speech can be measured online using people’s own smartphones and computers thus lowering the threshold to take part. I will ask 150 people who are at risk of psychosis, and 150 people with a first episode, to complete online belief-updating and speech tasks. After a year, I will check which participants have experienced transition to psychosis, and which have experienced relapse. I will then use advanced machine learning analyses to investigate if performance on the belief-updating and speech tasks was predictive of clinical outcomes. If successful, this will help to better allocate treatment to people who most need it.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: MENTAL HEALTH