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Bayesian modeling, Data Science, and Python

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

I noticed that as my personal website at my former university went down that my PhD thesis could not be found anywhere, so I'm posting it here.

During my PhD I explored how machine learning and computational modeling of the brain can be used to improve our understanding, and diagnostics, of psychiatric illnesses. See this blog post by Siobhan Cronin for her take on the thesis.

Download the thesis.

Abstract: "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 use- fulness 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. In chapter 1 I describe the current crisis in research and treat- ment of mental illness and argue that computational psychiatry provides the tools to solve some long-standing issues that hindered progress in this area. I describe these tools by reviewing the current literature on computational psychiatry and demon- strate their usefulness on two real-world data sets. To provide a coherent scope, I will focus on response inhibition as it provides a rich literature in each of the di↵erent levels of analysis with clear links to psychopathology. In chapter 2 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 (chapter 3) to estimate a decision making model in a hier- archical Bayesian manner which improves parameter recovery in a simulation study. In chapter 4 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 manipulations. 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 su↵ering from Huntington’s disease (chapter 5) and depression (chapter 6). Having identified neurobiological correlates of certain model parameters allows to then formulate the- ories not only about cognitive processes impacted by these disorders but also which neuronal mechanism are likely to be involved. In addition, I demonstrate that the description of subjects’ performance by computational model parameters can lead to better classification accuracy of disease state when compared to traditionally used summary statistics."

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