REGISTRATION

Welcome to the registration for the Computational Psychiatry Course 2021!

Volatile times require flexible decision making: Due to the rapid rise of the Delta variant of the coronavirus in Switzerland and many other countries we have decided that we will conduct the CPC Zurich 2021 in a fully virtual instead of a hybrid format. This was not an easy decision to take, but assures the safety for everyone involved and reduced the uncertainty for our speakers and attendees.

The CP Course offers a main course with lectures and talks plus practical tutorials on the last day of the course.
You can attend max. two Tutorials, one in the morning and one afternoon. If you would like to register for the tutorials please send us an email so that we can give you access to the tickets (cpcourse (at) biomed.ee.ethz.ch).

The main course ticket costs CHF 30, the tutorials cost an additional CHF 20.

Members of the host institutions (UZH and ETH Zurich) will receive a special access code, which will allow them to book the course free of charge. Please contact us and provide us with your Legi Number (UZH/ETH ID card number) in order to get your access code (cpcourse [at] biomed.ee.ethz.ch).

Information regarding refunds: Terms and Conditions

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ABOUT THE CPC ZURICH 

This course is organized by the Translational Neuromodeling Unit (TNU), University of Zurich & ETH Zurich and is designed to provide students from different fields with the necessary toolkit to master challenges in computational psychiatry research.

The CPC Zurich is meant to be practically useful for students at all levels (MDs, Master, PhD, Postdoc, PI) and from diverse backgrounds (neuroscience, psychology, medicine, engineering, physics, etc.), who would like to apply modeling techniques to study cognition or brain physiology in mental health. The course will teach not only the theory of computational modeling, but also demonstrate open source software in application to example data sets.

Pre-requisites: The course is split into several parts. The first day features an introduction to Psychiatry and Psychosomatic Medicine. Days 2-4 will cover computational methods in detail. Day 5 presents concrete applications. The final day 6 (to be booked separately) consists of practical tutorials with open source software. Some background knowledge in statistics and computational methods is needed to master the more technical parts (Day 2-4). If you lack this background it is recommended that you prepare for this course. Here is a list of helpful (but not mandatory) introductory resources to get you started.

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The CPC is divided into two parts:
The main course (Day 1-5) and in-depth practical tutorials (Day 6), which can only be attended in person on site.

MAIN COURSE

The first day will cover topics in Psychiatry providing a conceptual basis for the type of questions that Computational Psychiatry will need to address.

The second day will explain basic modelling principles (basic mathematical terminology, step-by-step guide on how to build a model, model fitting and model selection) and will finish with a first introduction to models of perception (Psychophysics, Bayesian Models of Perception).

The third day will continue with reinforcement learning, models of perception (Predicitve Coding), action selection (Markov Decision Processes, Active Inference, Drift Diffusion Models) and will end with an introduction to models of metacognition

The fourth day will include models of connectivity (Dynamic Causal Modeling for fMRI and EEG and biophysical network models) and Machine Learning (basics and advanced).

The fifth day will feature a series of talks on practical applications of computational models to problems from psychiatry.

More information about the program will follow shortly, see our website for details.

PRACTICAL TUTORIALS

The practical tutorials on the sixth day will provide 3-hour, small-group, in-depth and hands-on sessions on a specific modelling approach. Tutorials will take place on-site and will not be available to an online audience. To get the most out of the tutorial, students are advised to bring their own laptops along. The practical sessions cover only open-source software packages. The code can be found under the respective links below.

               
                                                                           

• Practical Session A   with Tore Erdmann, Alexander Hess, & Lilian Weber
      Bayesian Learning using the Hierarchical Gaussian Filter (HGF, TNU Tapas)
   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 behavioral data in general and specific to the HGF. We will work through exercises to learn how to analyze data with the HGF using the HGF-toolbox (in Matlab).
                                               
• Practical Session B   with Conor Heins
  Active Inference using the PYMDP Toolbox
  This tutorial provides a practical guide on developing computational models using `pymdp`, a Python package for solving partially-observed Markov Decision Processes (POMDPs) with Active Inference. Students will build simple simulations in interactive, cloud-hosted Python notebooks (Google Colab). We aim to help students build generative models for POMDPs and to develop a conceptual understanding of the theoretical principles behind active inference, without requiring detailed technical knowledge.
                                                
• Practical Session C   with Woo-Young Ahn, Jaeyeong Yang, & Hoyoung Doh
   Reinforcement Learning  using the hBayesDM Package
   In this tutorial, participants will learn how to use a Bayesian package called hBayesDM for modeling various reinforcement learning and decision making (RLDM) tasks. A short overview of (hierarchical) Bayesian modeling will be also provided. Participants will also learn important steps and issues to check when reporting modeling results in publications.
                                            
• Practical Session D   with Mads Lund Pedersen
  Drift Diffusion Models using the HDDM Toolbox
  The tutorial will provide a practical introduction to analyzing decision making data with the drift diffusion model (DDM) using the open source  python toolbox HDDM. We will go through practical steps of applying the models to data, from importing data to running and validating models. We will  also briefly go over extensions to the DDM, including the reinforcement learning drift diffusion model (RLDDM).
                                      
• Practical Session E   with Lionel Rigoux & Matthias Müller-Schrader
  Model Inversion using the Variational Bayes Toolbox
  This hands-on tutorial is a crash course on practical computational modelling. You will build a simple model (delay discounting) and learn how to apply it on empirical data to perform parameter estimation and model selection. We will use 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 (eg. Q-learning, DCM). No previous experience with modelling is required, but basic knowledge of Matlab is recommended.
                                              
• Practical Session F   with Thomas Wolfers & Saige Rutherford
  Machine Learning using the PCNtoolkit 
  Would you like to learn more about modeling individual differences and heterogeneity in psychiatry? In this tutorial, we will abandon the classical patient vs. healthy control framework. You will be guided through how to run an analysis using normative modeling implemented in the  PCNtoolkit.
                                              
• Practical Session G   with Rosalyn Moran
  Dynamic Causal Modelling for EEG using the SPM-DCM Package
  This tutorial 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 event related (ERP) and temporally extended / resting state studies (Spectral responses) will be examined.  The neural mass models - their assumptions and parametrizations will be compared.  Students will learn to use the SPM graphical user interface and to write batch code in MATLAB to perform Dynamic Causal Modeling of EEG.
                                             
• Practical Session H   with Jakob Heinzle & Birte Toussaint  
  Dynamic Causal Modelling for fMRI using the SPM-DCM Package
  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.
                                                                           
• Practical Session I   with Marion Rouault & Ashraya Indrakanti
  Metacognition using the hMtea-d Toolbox
  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 using the hMeta-d toolbox.  
                                     
• Practical Session J with Stefan Frässle
  Advanced Models of Connectivity: rDCM using Tapas rDCM
  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 from functional magnetic resonance imaging (fMRI) data. 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 allow you to obtain a better feeling for the behavior of the model as well as provide you with in-depth experience 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 MATLAB are beneficial.