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Title: Towards a mechanistic understanding of the neurobiological mechanisms underlying psychosis
Author: Haarsma, Joost
ISNI:       0000 0004 7651 7436
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2018
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Psychotic symptoms are prevalent in a wide variety of psychiatric and neurological disorders. Yet, despite decades of research, the neurobiological mechanisms via which these symptoms come to manifest themselves remain to be elucidated. I argue in this thesis that using a mechanistic approach towards understanding psychosis that borrows heavily from the predictive coding framework, can help us understand the relationship between neurobiology and symptomology. In the first results chapter I present new data on a biomarker that has often been cited in relation to psychotic disorders, which is glutamate levels in the anterior cingulate cortex (ACC), as measured with magnetic resonance spectroscopy. In this chapter I aimed to replicate previous results that show differences in glutamate levels in psychosis and health. However, no statistically significant group differences and correlations with symptomology were found. In order to elucidate the potential mechanism underlying glutamate changes in the anterior cingulate cortex in psychosis, I tested whether a pharmacological challenge of Bromocriptine or Sulpiride altered glutamate levels in the anterior cingulate cortex. However, no significant group differences were found, between medication groups. In the second results chapter I aimed to address a long-standing question in the field of computational psychiatry, which is whether prior expectations have a stronger or weaker influence on inference in psychosis. I go on to show that this depends on the origin of the prior expectation and disease stage. That is, cognitive priors are stronger in first episode psychosis but not in people at risk for psychosis, whereas perceptual priors seem to be weakened in individuals at risk for psychosis compared to healthy individuals and individuals with first episode psychosis. Furthermore, there is some evidence that these alterations are correlated with glutamate levels. In the third results chapter I aimed to elucidate the nature of reward prediction error aberrancies in chronic schizophrenia. There has been some evidence suggesting that schizophrenia is associated with aberrant coding of reward prediction errors during reinforcement learning. However it is unclear whether these aberrancies are related to disease years and medication use. Here I provide evidence for a small but significant alteration in the coding of reward prediction errors that is correlated with medication use. In the fourth results chapter I aimed to study the influence of uncertainty on the coding of unsigned prediction errors during learning. It has been hypothesized by predictive coding theorists that dopamine plays a role in the precision-weighting of unsigned prediction error. This theory is of particular relevance to psychosis research, as this might provide a mechanism via which dopamine aberrancies, might lead to psychotic symptoms. I found that blocking dopamine using Sulpiride abolishes precision-weighting of unsigned prediction error, providing evidence for a dopamine mediated precision-weighting mechanism. In the fifth results chapter I aimed to extend this research into early psychosis, to elucidate whether psychosis is indeed associated with a failure to precision-weight prediction error. I found that first episode psychosis is indeed associated with a failure to precision-weight prediction errors, an effect that is explained by the experience of positive symptoms. In the sixth results chapter I explore whether the degree of precision-weighting of unsigned prediction errors is correlated with glutamate levels in the anterior cingulate cortex. Such a correlation might be plausible given that psychosis has been associated with both. However, I did not find such a relationship, even in a sample of 137 individuals. Thus I concluded that anterior cingulate glutamate levels might be more related to non-positive symptoms associated with psychotic disorders. In summary, a mechanistic approach towards understanding psychosis can give us valuable insights into the disease mechanisms at play. I have shown here that the influence of expectations on perception is different across disease stage in psychosis. Furthermore, aberrancies in prediction error mechanisms might explain positive symptoms in psychosis, a process likely mediated by dopaminergic mechanisms, whereas evidence for glutamatergic mediation remains absent.
Supervisor: Murray, Graham Sponsor: Neuroscience in Psychiatry Network
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: Psychosis ; Psychiatry ; Cognitive Neuroscience ; Computational Psychiatry ; Dopamine ; Glutamate ; Reinforcement learning