Can online markers of decision-making predict psychosis-onset in people at risk of psychosis?

Lead Supervisor
Dr Kelly Diederen
Lecturer in Psychosis Studies
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London
kelly.diederen@kcl.ac.uk

Co-supervisor
Dr Tom Spencer
Clinical Senior Lecturer in Early Intervention Honorary Consultant Psychiatrist OASIS Lewisham and OASIS Croydon Education co-Lead
Division of Academic Psychiatry Department of Psychosis Studies Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King’s College London
tom.spencer@kcl.ac.uk

Project Details

Positive symptoms of psychosis, hallucinations and delusions, are believed to result from key alterations in the way individuals learn and make decisions. During decision-making, discrepancies between predictions and novel information facilitate learning, via the generation of prediction errors (PEs). This process is encoded by dopamine, a neurotransmitter crucial in the aetiology of psychosis.

Individuals experiencing psychotic symptoms, display aberrant PE signalling leading to an increase of salience towards neutral events or unlikely observations. While positive psychotic symptoms are thought to reflect the endpoints of aberrant learning and inference processes, it is insufficiently clear whether these disturbances are already evident in the prodromal phase, when individuals can be identified as at clinical high risk for psychosis (CHR-P). This is important as pathogenic studies in schizophrenia patients are often limited by illness and treatment-related confounds and as the CHR-P group is highly diverse in its clinical outcomes, with only ~1/3 transitioning to psychosis.

The ability to stratify this group to improve the prediction of psychosis would provide large benefits for treatment, provided that there is an effective way to do so in terms of cost, time and availability. Decision-making is an excellent candidate as it can be assessed on a large-scale with limited costs, through online testing, which replicates findings from laboratory testing, improves ecological validity and reduces selection bias as participation is unconstrained by geographical area.

The main objective of this PhD is to determine whether altered decision-making predicts transition to psychosis within 2 years in 300 individuals at CHR-P (and 100 healthy controls). The secondary objective is to investigate whether decision-making predicts functional outcomes (e.g., vocational status). The student will analyse data on decision-making acquired online (via Gorilla.sc) as part of an ongoing multicentre study. Statistical analysis will use generalised linear models and structural equation modelling to test the prospective relationship between decision-making and clinical and functional outcomes. Machine learning will be used to estimate the risk of future psychosis in the CHR-P group at the level of the individual. There is also an opportunity to analyse decision-making data in people with established psychotic illness, and in people with subclinical psychotic symptoms in the general population.

Year 1:
Learning to analyse decision-making data using reinforcement learning, and Bayesian modelling under supervision of Dr Diederen and Spencer who have already successfully employed these analyses.
Training in general academic skills (e.g., conference presentations, literature review and writing scientific manuscripts) as well as specific analyses (e.g. prediction modelling, machine-learning) and coding (e.g., R and Matlab).
Write the upgrade report and prepare for the upgrade viva
There is also the possibility to identify additional datasets and analytical techniques for the student to work on

Year 2:
Data analyses
Presenting at a conference (e.g., the annual meeting of the Schizophrenia International Research Society)
Preparing manuscripts for submission

Year 3:
Submitting manuscripts
Presenting at a conference (e.g., the meeting of the European Conference on Schizophrenia Research)
Dissertation writing
Exploring future career steps/ applying for postdoctoral fellowships

Datasets

ePREDICT – 300 individuals at clinical high risk of psychosis, 300 people at first episode psychosis, 100 healthy controls. People are followed up for a period of two years with decision-making assessed every six months. Clinical assessments and functional outcomes measured at baseline, 12 months and 24 months. Ethical approval obtained via REC. 

Decipher Online study. Online assessment of decision-making (using a more extensive set of tasks than the ePREDICT study) in 450 people in the general population. Psychiatric symptom scores, with a focus on subclinical psychotic symptoms assessed using well validated questionnaires. Ethical approval obtained via REMAS. 

UCLA Consortium for Neuropsychiatric Phenomics: healthy individuals (130 subjects) and individuals with neuropsychiatric disorders including schizophrenia (50 subjects) and bipolar disorder (49 subjects). This dataset includes an extensive set of clinical assessments, personality questionnaires and decision-making tasks. The dataset is shared through the OpenfMRI project, which provides automatic and immediate ethical approval to work on the data. For details, please see: POLDRACK, Russell A., et al. A phenome-wide examination of neural and cognitive function. Scientific data, 2016, 3.1: 1-12.)

Keywords

Psychosis, schizophrenia, decision-making, computational psychiatry, reinforcement learning