Computational Psychiatry Course 2025

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, 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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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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This interactive, hands-on workshop introduces participants to machine-learning (ML) approaches within the rapidly growing field of precision psychiatry. Combining theoretical concepts with practical exercises, participants will engage directly with NeuroMiner, a powerful yet accessible tool developed specifically for clinical neuroscience and neuroimaging research. Attendees will learn about core ML concepts relevant to psychiatric research, such as nested cross-validation, the curse of dimensionality, overfitting prevention, external validation, and explainable AI (XAI). Special attention will be given to specific challenges in neuroimaging and psychiatric data analysis, including site-correction and data fusion techniques. The practical session will guide participants step-by-step through the ML pipeline implementation using NeuroMiner, offering valuable hands-on experience in creating robust and reliable predictive models. Workshop participants will also gain insights into model evaluation strategies through engaging and collaborative exercises. Finally, participants will discuss practical, ethical, and regulatory considerations for integrating ML into clinical psychiatry. Participants will be introduced to the TRIPOD-AI guidelines, ensuring high-quality reporting and communication of ML-driven research results.

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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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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, 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 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, 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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