Finding psychosis subtypes using machine learning, clinical, genetic and multimodal imaging data

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This project will address a key challenge in psychiatric neuroscience: Can we change diagnostic boundaries to reflect underlying neurobiological subtypes of mental illnesses? Lately, the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) - the list of diagnostic criteria for psychiatric disorders - has received a lot of negative publicity, with many criticising psychiatric diagnoses for being too categorical and only based on symptoms, and not on underlying neurobiology or other markers. Whilst there is some truth to these claims, it remains very unclear what should replace the DSM-V. This study will use machine learning techniques (algorithms that can find similarities in very complex - e.g. imaging - data and group similar subjects into clusters) to find subtypes of psychotic disorders in large datasets comprised of subjects with diagnoses of schizophrenia, schizoaffective and bipolar disorders. The data themselves come from brain imaging (functional magnetic resonance imaging and electroencephalography) experiments, and psychological and symptom measures. Although numerous research groups are currently using machine learning to try to find new subtypes of psychiatric disorders, the downside of this approach can be that the complex data features that these algorithms use to differentiate between illness subtypes are not interpretable in a biological sense. For example, it would be impossible to know just from looking at distinct patterns of electroencephalography (EEG) data whether they were caused by a dysfunction in a specific type of neuron or receptor. We can try to solve this complex problem by using models of how neurons and their receptors generate brain imaging data: from these models, and the imaging data we have, we can estimate the function or dysfunction of specific cell types and receptors. By doing this in all our subjects, and adding this data to the machine learning classifier, we hope to obtain subtypes of psychotic disorders that are interpretable in a biological sense. The advantages of having illness subtypes that can be understood in biological terms are clear: they ought to lead to better research into the distinct causes of each illness subtype, and better development of subtype-specific treatments.