Computational Psychiatry Course (CPC) Zurich 2024

In this tutorial, we will recap the theory behind the Hierarchical Gaussian Filter (HGF) and introduce the model in an accessible way. We will then discuss practical issues when fitting computational models to behavioural data in general and specific to the HGF. We will work through excercises to learn how to analyze data with the HGF using the HGF toolbox in Julia and Python. 

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In this tutorial we will review the theory behind active inference and how to implement it with a partially obpbservable Markov decision process (POMDP). We will then do excercises building generative models of common behavioral tasks, learn how to rub simulations, and illustrate the useful properties of this modeling framework and when it is and isn't applicable. Finally, we will ork through excercises to learn how to fit active inference models to behavioral data and use parametert estimates as individual differences measures in common computational psychiatry contexts. All tutorial excercises will be conducted in MATLAB

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In this tutorial, participant wil learn how to use the hBayesDM package (supporting R and Python) for modeling various reinforcement learning and decision making tasks. A short overview of (hierarchical) Bayesian modeling will also be provided. Participants will learn important steps and issues to check when reporting modeling results in publications.

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In this tutorial, you will apply computational modeling to a real-live example. Starting from a simple experimental design (delay discounting task), you will learn how to:

• choose and implement the right model for your task
• fit it to empirical data (and get parameter estimates)
• perform hypothesis testing using model selection 
• validate your analysis using simulation and diagnostic tools

You will also learn the basics of the VBA toolbox which contains all the tools to simulate, estimate, and diagnose your models, as well as a collection of ready-to-use models (e.g. Q-learning, DCM). No previous experience with modeling is required, but basic knowledge of MATLAB is recommendet. 

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Would you like to learn more about modeling individual differences and heterogeneity in psychiatry? In this tutorial, we will abandon the classical patients vs. healthy control framework. You will be guided through how to run an analysis using normative modeling implemented in the PCNtoolkit (using Google Colab notebooks)

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In this tutorial, we will examine specific features of EEG data that can be used to optimize a cell and receptor specific model of brain connectivity. EEG data acquired from an event-related (ERP) visual memory study will be examined. The assumptions and parametrizations of the neural mass models will be explained and students will learn to use the SPM graphical user interface and to write batch code in Matlab to perfrom Dynamic Causal Modeling of EEG.

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In this tutorial you will learn how to use the SPM software to perform a dynamic causal modeling (DCM) analysis in MATLAB. We will first guide you through all steps of a basic DCM analysis of a single subject: Data extraction, Model setup, Model inversion and, finally, inspection of Results. We will then proceed to look at a group of subjects. Here, we will focus on model comparison and inspection of model parameters. We will provide a point-by-point recipe on how to perform the analysis. However, it is of advantage if you have worked with neuroimaging (fMRI) data and MATLAB before.

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In this tutorial, you will learn how to use the regression dynamic causal modeling (rDCM) toolbox to perform effective (directed) connectivity analyses in whole-brain networks. We will provide you with the necessary theoretical background of the rDCM approach and detail practical aspects that are relevant for whole-brain connectivity analyses. After having laid the foundation, a hands-on part will familiarize you with the code and provide in-depth training on how to apply the model to empirical fMRI data. The goal of this tutorial is to familiarize you with the theoretical and practical aspects of rDCM, which will allow you to seamlessly integrate the approach into your own research. We will provide clear instructions on how to perform the analyses. However, experience with the analysis of fMRI data (already some experience with classical DCM for fMRI would be ideal) as well as experience with Julia are beneficial.

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The Brain Dynamics Toolbox is a Matlab toolbox for simulating dynamical systems in neuroscience. It allows custom dynamical models to be explored with minimal programming effort. This is an introductory tutorial for new users. The format will be a mix of on-line lectures and self-paced exercises. Participants will be guided through the process of running an existing model and visualising the dynamics using both the graphical controls and the Matlab command line. Upon completion, participants will be able to automate a parameter sweep and produce a bifurcation diagram. No previous modelling experience is required but basic knowledge of Matlab is assumed.

Language: MATLAB

Toolbox: bdtoolbox.org 

Installation guide: tba

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In this tutorial, we will recap the theory behind the Hierarchical Gaussian Filter (HGF) and introduce the model in an accessible way. We will then discuss practical issues when fitting computational models to behavioural data in general and specific to the HGF. We will work through excercises to learn how to analyze data with the HGF using the HGF toolbox in Julia and Python. 

Add to Calendar

In this tutorial we will review the theory behind active inference and how to implement it with a partially obpbservable Markov decision process (POMDP). We will then do excercises building generative models of common behavioral tasks, learn how to rub simulations, and illustrate the useful properties of this modeling framework and when it is and isn't applicable. Finally, we will ork through excercises to learn how to fit active inference models to behavioral data and use parametert estimates as individual differences measures in common computational psychiatry contexts. All tutorial excercises will be conducted in MATLAB

Add to Calendar

In this tutorial, participant wil learn how to use the hBayesDM package (supporting R and Python) for modeling various reinforcement learning and decision making tasks. A short overview of (hierarchical) Bayesian modeling will also be provided. Participants will learn important steps and issues to check when reporting modeling results in publications.

Add to Calendar

In this tutorial, you will apply computational modeling to a real-live example. Starting from a simple experimental design (delay discounting task), you will learn how to:

• choose and implement the right model for your task
• fit it to empirical data (and get parameter estimates)
• perform hypothesis testing using model selection 
• validate your analysis using simulation and diagnostic tools

You will also learn the basics of the VBA toolbox which contains all the tools to simulate, estimate, and diagnose your models, as well as a collection of ready-to-use models (e.g. Q-learning, DCM). No previous experience with modeling is required, but basic knowledge of MATLAB is recommendet. 

Add to Calendar

In this tutorial, we will examine specific features of EEG data that can be used to optimize a cell and receptor specific model of brain connectivity. EEG data acquired from an event-related (ERP) visual memory study will be examined. The assumptions and parametrizations of the neural mass models will be explained and students will learn to use the SPM graphical user interface and to write batch code in Matlab to perfrom Dynamic Causal Modeling of EEG.

Add to Calendar

In this tutorial you will learn how to use the SPM software to perform a dynamic causal modeling (DCM) analysis in MATLAB. We will first guide you through all steps of a basic DCM analysis of a single subject: Data extraction, Model setup, Model inversion and, finally, inspection of Results. We will then proceed to look at a group of subjects. Here, we will focus on model comparison and inspection of model parameters. We will provide a point-by-point recipe on how to perform the analysis. However, it is of advantage if you have worked with neuroimaging (fMRI) data and MATLAB before.

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In this tutorial, you will learn how to use the regression dynamic causal modeling (rDCM) toolbox to perform effective (directed) connectivity analyses in whole-brain networks. We will provide you with the necessary theoretical background of the rDCM approach and detail practical aspects that are relevant for whole-brain connectivity analyses. After having laid the foundation, a hands-on part will familiarize you with the code and provide in-depth training on how to apply the model to empirical fMRI data. The goal of this tutorial is to familiarize you with the theoretical and practical aspects of rDCM, which will allow you to seamlessly integrate the approach into your own research. We will provide clear instructions on how to perform the analyses. However, experience with the analysis of fMRI data (already some experience with classical DCM for fMRI would be ideal) as well as experience with Julia are beneficial.

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In this tutorial, we will recap the theory underlying the hMeta-d model for quantifying metacognitive efficiency, our ability to monitor and evaluate our own decisions. We will introduce the model in an accessible way, then discuss practical issues when fitting computational models to behavioral data, and go through code examples relevant to computational psychiatry studies using the hMeta-d toolbox (in MATLAB).

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