12. September - 17. September 2022
ABOUT THE CPC


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.

The goal of the Computational Psychiatry Course (CPC) is to create a scientific and educational space for students, scientists, and other professionals to share and advance the state of knowledge in CP. Everyone is welcome at the CPC. To this end, we encourage all participants to treat each other respectfully. This Code of Conduct defines a set of guidelines to facilitate this.

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.

COURSE REGISTRATION

Welcome to the registration for the Computational Psychiatry Course 2022!

The CPC is divided into two parts. The main course (Days 1-5) will be held in a hybrid format, that is, the lectures during the week will be held for an on-site & online audience. The in-depth practical tutorials (Day 6) will be held either online or on-site, that is, they will not accommodate mixed (online & on-site) audiences. Please check the tutorial list below for more information on each tutorial.

On-site tickets for external attendees cost:
- CHF 350 for the main course only
- CHF 400 for the main course plus two practical tutorials on the last day of the course

Online tickets for external attendees cost:
- CHF 50 for the main course only
- CHF 100 for the main course plus two practical tutorials on the last day of the course

Members of the host institutions (UZH and ETH Zurich) can book the course free of charge. Please contact us before you register so that we can give you special access.


COURSE DETAILS

Duration
5 days (Lectures) + 1 day (Practical Tutorials)
(between 8 AM CEST and 6 PM CEST)

Date
12th – 17th September 2022

Designed For
Master Students, PhDs, PostDocs, Clinicians and anyone interested in Computational Psychiatry

Perfomance Assessment
Oral examination after the course, date TBA.

ECTS for University of Zurich & ETH Zurich students
You will be awarded 3 ECTS for completing the oral examination successfully. This course is part of the HS (fall term) 2022 and will be available on ETH MyStudies and the UZH course catalogue in due time. For ECTS you will need to sign up in ETH myStudies in addition to registering here. UZH students must register and sign up in ETH myStudies as “UZH Fachstudent/in”.

COURSE STRUCTURE

This year, we are planning to organize a hybrid version of the course (if the situation permits). Specifically, lectures during the week will be held for an on-site & online audience.
The CPC is divided into two parts: main course (Days 1-5), which will be held in a hybrid format, and in-depth practical tutorials (Day 6). Tutorials will be held either online or on-site, that is, they will not accommodate mixed (online & on-site) audiences. Please check the tutorial list below for more information on each tutorial.


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 (Predictive Coding), an introduction to the HGF (hierarchical gaussian filter), 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.




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. The practical sessions cover only open-source software packages. The code can be found under the respective links below. If you sign up, you will receive an installation guide and further information before the course takes place.
You are allowed to book ONE morning and ONE afternoon tutorial. For practical tutorials that have morning and afternoon sessions: these sessions will cover exactly the same content.

  • Practical Session A with Tore Erdmann, Alex Hess & Peter Thestrup Waade
    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 Julia).

    Sessions: morning and afternoon (in Zurich)


  • Practical Session B with Ryan Smith
    Active Inference using SPM

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

    Sessions: afternoon (online)


  • Practical Session C with Conor Heins & Ana Grosu
    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.

    Sessions: morning and afternoon (in Zurich)


  • Practical Session D with Woo-Young Ahn
    Reinforcement Learning using the hBayesDM Package

    In this tutorial, participants will learn how to use a Bayesian package called hBayesDM (supporting R and Python) 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.

    Sessions: morning and afternoon (online)


  • Practical Session E with Ariel Zylberberg
    The Drift-diffusion model of decision-making

    In this tutorial, students will learn the theory and practice behind the drift-diffusion model, as it is usually applied to explain behavior (choice, response time, confidence) in simple decision-making tasks. Participants will implement computational simulations to study the properties of the drift-diffusion model, and fit experimental data using MATLAB code provided by the instructor. We will also discuss some of the limitations of the model and common mistakes made when interpreting the model parameters.

    Sessions: afternoon (online)


  • Practical Session F with Lionel Rigoux & Matthias Müller-Schrader
    Modelling Crash-Course using the VBA Toolbox

    In this hands-on tutorial, you will apply computational modelling to a real-life 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 and
    - validate your analysis using simulations 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 modelling is required, but basic knowledge of MATLAB is recommended.

    Sessions: morning and afternoon (in Zurich)


  • Practical Session G with Saige Rutherford & Thomas Wolfers
    Machine Learning Tutorial 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 (using cloud-hosted Python notebooks in Google Colab).

    Sessions: morning and afternoon (in Zurich)


  • Practical Session H with Ashley Tyrer & Herman Galioulline
    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 an event-related (ERP) visual memory study will be examined. The assumptions and parametrizations of the neural mass models will be explained. Students will learn to use the SPM graphical user interface and to write batch code in MATLAB to perform Dynamic Causal Modeling of EEG.

    Sessions: morning and afternoon (in Zurich)


  • Practical Session I 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.

    Sessions: morning and afternoon (in Zurich)


  • Practical Session J with Marion Rouault & Sandra Iglesias
    Modeling metacognition using the hMeta-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 relevant to computational psychiatry studies using the hMeta-d toolbox (in MATLAB).

    Sessions: morning and afternoon (location TBA)


  • Practical Session K with Stefan Frässle & Imre Kertesz
    Advanced Models of Connectivity: regression DCM using the Tapas rDCM toolbox

    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 MATLAB are beneficial.

    Sessions: morning (in Zurich) and afternoon (online)


  • Practical Session L with John Murray
    Biophysical Network Models
    More information will follow.

    Sessions: afternoon (online)
SPEAKERS 2022
Woo-Young AhnSeoul National University, South Korea
Anne CollinsBerkeley, USA
Tore ErdmannScuola Internazionale Superiore di Studi Avanzati, Italy
Michael J. FrankBrown University, USA
Stefan FrässleUniversity of Zurich & ETH Zurich, Switzerland
Herman GalioullineUniversity of Zurich & ETH Zurich, Switzerland
Sam Gershman Harvard University, USA
Ana Grosu University of Zurich & ETH Zurich, Switzerland
Helene Haker Rössler University of Zurich & ETH Zurich, Switzerland
Olivia Harrison University of Otago, New Zealand
Conor Heins Max Planck Institute of Animal Behavior and University of Konstanz, Germany
Sandra IglesiasUniversity of Zurich & ETH Zurich, Switzerland
Jakob HeinzleUniversity of Zurich & ETH Zurich, Switzerland
Alex HessUniversity of Zurich & ETH Zurich, Switzerland
Imre KerteszUniversity of Zurich & ETH Zurich, Switzerland
Andre MarquandDonders Institute, Netherlands
Christoph MathysAarhus University, Denmark
Rosalyn MoranKing's College London, United Kingdom
John MurrayYale School of Medicine, USA
Matthias Müller-SchraderUniversity of Zurich & ETH Zurich, Switzerland
Matthew NassarBrown University, USA
Thomas ParrUCL London, UK
Inês PereiraUniversity of Zurich & ETH Zurich, Switzerland
Frederike PetzschnerBrown University, USA
Albert PowersYale School of Medicine, USA
Lionel RigouxMax Planck Institute Cologne, Germany
Marion RouaultÉcole Normale Supérieure, France
Saige RutherfordRadboud University Medical Center, Netherlands
Philipp SchwartenbeckUCL London, UK
Jakob SiemerkusUniversity of Zurich & ETH Zurich, Switzerland
Ryan Smith Laureate Institute for Brain Research, USA
Klaas Enno StephanUniversity of Zurich & ETH Zurich, Switzerland
Peter Thestrup WaadeAarhus University, Denmark
Birte ToussaintUniversity of Zurich & ETH Zurich, Switzerland
Ashley TyrerAarhus University, Denmark
Katharina V. WellsteinUniversity of Zurich & ETH Zurich, Switzerland
Thomas WolfersDonders Institute, Netherlands
Ariel ZylberbergUniversity of Rochester, USA


The Computational Psychiatry Course does not receive any sponsoring from the pharmaceutical industry.

COURSE MATERIAL
PAST COURSES

The Translational Neuromodeling Unit (TNU) has been organizing the Computational Psychiatry Course in Zurich since 2015. All materials from previous courses can be found here.

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CPC TEAM
Inês Pereira

Organizer

Prof. Klaas Enno Stephan

Organizer

 
Katharina V. Wellstein

Organizer

Alexander Hess

Contact Person

 
Nicole Jessica Zahnd

Contact Person

László Demkó

Contact Person

 
Heidi Brunner

Administration