Computational Psychiatry: Combining multiple levels of analysis to understand brain disorders

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Computational Psychiatry: Combining multiple levels of analysis to understand brain disorders
Wiecki, Thomas V (creator)
Frank, Michael (Director)
Serre, Thomas (Reader)
Sudderth, Erik (Reader)
Greenberg, Benjamin (Reader)
Brown University. Psychology (sponsor)
Copyright Date
The premise of the emerging field of computational psychiatry is to use models from computational cognitive neuroscience to gain deeper insights into mental illness. In this thesis my goal is to provide an overview of this endeavor and advance it by developing new software as well as quantitative methods. To demonstrate their usefulness I will apply these methods to real-world data sets. A central theme will be the bridging of multiple levels of analysis of the brain ranging from neuroscience and cognition to behavior. First, I describe the current crisis in research and treatment of mental illness and argue that computational psychiatry provides the tools to solve some long-standing issues that hindered progress in this area. To provide a coherent scope, I will focus on response inhibition as it provides a rich literature in each of the different levels of analysis with clear links to psychopathology. Next, I first establish a neuronal basis by presenting a biologically plausible neural network model of key areas involved in response inhibition. Capturing the high-level computations of this fairly complex model requires more abstract cognitive process models. Towards this goal we developed software to estimate a decision making model in a hierarchical Bayesian manner which improves parameter recovery in a simulation study. I then bridge the neuronal and cognitive level by fitting a psychological process model to the simulated behavioral output of the neural network model under certain biological anipulations. By analyzing which biological manipulation is best captured by changes in certain high-level computational parameters I start to link both levels of analysis. I then apply this same psychological process model to two data sets from selective response inhibition tasks administered to patients suffering from Huntington's disease and depression. Having identified neurobiological correlates of certain model parameters allows to then formulate theories not only about cognitive processes impacted by these disorders but also which neuronal mechanism are likely to be involved.
computational psychiatry
computational modeling
neural networks
drift diffusion model
bayesian modeling
Neural networks (Neurobiology)
Machine learning
Mental illness
Cognitive neuroscience
Thesis (Ph.D. -- Brown University (2014)
xxviii, 271 p.


Wiecki, Thomas V., "Computational Psychiatry: Combining multiple levels of analysis to understand brain disorders" (2014). Psychology Theses and Dissertations. Brown Digital Repository. Brown University Library.