Prediction-Error in psychiatric disorders: From Maladaptive decision-making to the neurobiology of psychosis and schizophrenia (PhD)

My research interests are in psychiatric disorders and computational models of psychopathologies (with a particular interest for Schizophrenia and the mechanisms of Psychosis). The aim of this Ph.D. is to study computationally and in collaboration with experimentalists, how a disturbed prediction error signal (also known as the dopaminergic system originating from the midbrain) can result in the variety of symptoms observed in patients with schizophrenia (i.e. Cognitive symptoms and Positive Symptoms). Individuals with psychiatric disorders often display impaired performances in a wide range of cognitive tasks (i.e. set shifting, avoidance conditioning, decision making). In the first part of this study, we assessed how the disruptions of the prediction-error signal observed in the majority of patients can lead to the deteriorated performances seen in decision-making. In this study, we produced a computational model based on the TD-learning algorithm that integrates inter-individual behavioral traits (i.e. Risk seeking, Reward Sensitivity, Cognitive Flexibility) in combination with learning and decision-making processes in a rodent version of the Iowa Gambling Task. In the remaining part of this Ph.D., we wish to assess a novel theoretical framework (not been implemented to date) to understand the etiology of psychotic symptoms in Schizophrenia. The framework recently proposed by Corlett et al. [1] relies on the principles of the Bayesian brain combined with a disrupted prediction error signal. There, the Bayesian Brain refers to the ability of the nervous system to operate in situations of uncertainty in a fashion that is optimal according to Bayesian statistics. This novel framework could provide a fresh account to the causes and evolution of delusions and hallucinations in patients with schizophrenia.

[1] Corlett PR, Honey GD, Krystal JH, Fletcher PC. Glutamatergic Model Psychoses: Prediction Error, Learning, and Inference. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology. 2010:1-22.

Related Themes

Related Publications and Presentations

  • Ondrej Mandula, and Rainer Heintzmann, “Line Scan – Structured Illumination Microscopy High-resolution imaging in thick fluorescent samples”, Focus on Microscopy, 2012.
  • Maria Dauvermann, Heather C Whalley, Lianna Romanuik, Vincent Valton, DG Owens, E.C. Johnstone, Stephen Lawrie, and T.W. Moorhead, “The application of nonlinear dynamic causal modelling for fMRI in subjects at high genetic risk of schizophrenia”, NeuroImage, 2013.
  • Vincent Valton, Alain Marchand, Francoise Dellu-Hagedorn, and Peggy Series, “Modeling maladaptive decision-making in a rat version of the Iowa Gambling Task”, BMC Neuroscience, 2011, 12(S1), P294.
  • Vincent Valton, Alain Marchand, Francoise Dellu-Hagedorn, and Peggy Series, “Maladaptive decision-making in a rat version of the Iowa Gambling Task”, COSYNE, 2012.

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