Woo-Young Ahn
Anne Collins
Tore Erdmann
Michael J. Frank
Stefan Frässle
Herman Galioulline
Sam Gershman
Ana Grosu
Helene Haker Rössler
Olivia Harrison
Conor Heins
Jakob Heinzle
Alexander Hess
Sandra Iglesias
Imre Kertesz
Andre Marquand
Christoph Mathys
Rosalyn Moran
John Murray
Matthias Müller-Schrader
Matthew Nassar
Thomas Parr
Inês Pereira
Frederike Petzschner
Albert Powers
Lionel Rigoux
Marion Rouault
Saige Rutherford
Philipp Schwartenbeck
Jakob Siemerkus
Ryan Smith
Klaas Enno Stephan
Peter Thestrup Waade
Birte Toussaint
Ashley Tyrer
Katharina V. Wellstein
Thomas Wolfers
Ariel Zylberberg
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.
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
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”.
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.
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.
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.
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Woo-Young Ahn | Seoul National University, South Korea |
Anne Collins | Berkeley, USA |
Tore Erdmann | Scuola Internazionale Superiore di Studi Avanzati, Italy |
Michael J. Frank | Brown University, USA |
Stefan Frässle | University of Zurich & ETH Zurich, Switzerland |
Herman Galioulline | University 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 Iglesias | University of Zurich & ETH Zurich, Switzerland |
Jakob Heinzle | University of Zurich & ETH Zurich, Switzerland |
Alex Hess | University of Zurich & ETH Zurich, Switzerland |
Imre Kertesz | University of Zurich & ETH Zurich, Switzerland |
Andre Marquand | Donders Institute, Netherlands |
Christoph Mathys | Aarhus University, Denmark |
Rosalyn Moran | King's College London, United Kingdom |
John Murray | Yale School of Medicine, USA |
Matthias Müller-Schrader | University of Zurich & ETH Zurich, Switzerland |
Matthew Nassar | Brown University, USA |
Thomas Parr | UCL London, UK |
Inês Pereira | University of Zurich & ETH Zurich, Switzerland |
Frederike Petzschner | Brown University, USA |
Albert Powers | Yale School of Medicine, USA |
Lionel Rigoux | Max Planck Institute Cologne, Germany |
Marion Rouault | École Normale Supérieure, France |
Saige Rutherford | Radboud University Medical Center, Netherlands |
Philipp Schwartenbeck | UCL London, UK |
Jakob Siemerkus | University of Zurich & ETH Zurich, Switzerland |
Ryan Smith | Laureate Institute for Brain Research, USA |
Klaas Enno Stephan | University of Zurich & ETH Zurich, Switzerland |
Peter Thestrup Waade | Aarhus University, Denmark |
Birte Toussaint | University of Zurich & ETH Zurich, Switzerland |
Ashley Tyrer | Aarhus University, Denmark |
Katharina V. Wellstein | University of Zurich & ETH Zurich, Switzerland |
Thomas Wolfers | Donders Institute, Netherlands |
Ariel Zylberberg | University of Rochester, USA |
The Computational Psychiatry Course does not receive any sponsoring from the pharmaceutical industry.
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.