This course is designed to provide students across fields (neuroscience, psychiatry, physics, biology, psychology....) 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) coming from diverse backgrounds (neuroscience, psychology, medicine, engineering, physics, etc.), who would like to apply modeling techniques to study learning, decision-making or brain physiology in patients with psychiatric disorders. The course will teach not only the theory of computational modeling, but also demonstrate software in application to example data sets.
We strongly believe in open source and open science, therefore, the content of the course will be made freely accessible on the web.
Registration will open on 28.02.2020!
5 days + 1 day (Practical Tutorials)
7th – 12th September 2020
28. February 2020
23. August 2020
Master Students, PhDs, PostDocs, Clinicians and anyone interested in Computational Psychiatry
We are offering CP Course Stipends for students who cannot afford to pay the course fee. If you are interested klick here for more information. Application is due on 17.03.2020!
3 ECTS for University of Zurich & ETH Zurich students
This course is part of the HS (fall term) 2020 and will be available on ETH MyStudies and the UZH course catalogue in due time. For ECTS you will need to sign up there in addition to registering here.
Short oral examination (date to be announced) at the TNU.
Main Course (day 1-5):
CHF 500 for external participants
CHF 100 for University of Zurich & ETH staff & students
Practical Tutorials (day 6):
CHF 50 per Practical Tutorial Session for external as well as for University of Zurich & ETH staff & students
Please note that we offer a 20% reduction on the main course fee if you register before 30.04.2020.
The CPC is divided into two parts: The main course (day 1-5) and in-depth practical tutorials (day 6).
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, model inversion and model selection) and will finish with a first introduction to a possible learning model (Reinforcement Learning).
The third day will include models of perception (Psychophysics, Bayesian Models od Perception, Predicitve Coding) and action selection (Markov Decision Processes, Active Inference, Drift Diffusion Models).
The fourth day (only half a day) will include Machine Learning (basics and advanced) and models of connectivity (Dynamic Causal Modeling and Advanced Models of Connectivity).
The fifth day will feature a series of talks on practical applications of computational models to problems from psychiatry.
The sixth day of the course will provide in-depth practical sessions of a subset of the presented models for a smaller fraction of students (seperate registration).
The practical tutorials on the sixth day will provide 3-hour, small-group, in-depth and hands-on sessions on a specific modelling approach. 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.
In this tutorial, we will recap the theory underlying the HGF (a Hierarchical Bayesian Inference Model) and introduce the model in an accessible way. We will then discuss practical issues when fitting computational models to behavioral data and go through code examples using the HGF Toolbox.
This tutorial provides a practical guide on developing computational models. You will be taught to build simple simulations using Matlab, and will use these to explore the behavioral and electrophysiological manifestations of active inference. We aim to develop an intuitive understanding of the theoretical principles, without requiring detailed technical knowledge.
In this tutorial, you 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 also be provided. You will also learn the most important things you need to check when reporting modeling results in publications.
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 (e.g. Q-learning, DCM). No previous experience with modelling is required, but basic knowledge of Matlab is recommended.
Would you like to learn more about normative modelling in psychiatry? In this tutorial you will learn the “why” and the “how” of simple normative models. You will take home a better understanding of this technique, machine learning, and some transferable Python programming skills.
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.
Regression dynamic causal modeling (rDCM) represents a novel variant of DCM for fMRI that allows inferring effective (directed) connectivity in whole-brain networks with high computational efficiency. In this tutorial, you will familiarize yourself with the rDCM toolbox, get insights into the theoretical concepts and practical applications of the model, and learn how to set-up your own whole-brain effective connectivity analyses. Experience with (standard) DCM and Matlab programming is beneficial for this tutorial.
In this tutorial, you will learn how to use the HUGE-toolbox to perform model-based group-level analysis on task fMRI data. HUGE is a hierarchical extension of dynamic causal model (DCM) for fMRI which provides a unified framework for performing fitting of DCM and group-level analysis, such as empirical Bayesian analysis or stratification, i.e. the identification of subgroups, of heterogeneous cohorts. For this tutorial, we expect that you have experience with classical fMRI analysis and classical DCM, and a basic level of familiarity with the Matlab programming language.
|Woo-Young Ahn||Seoul National University, South Korea|
|Sonia Bishop||UC Berkeley, United States|
|Michael Breakspear||QIMR Berghofer, Australia|
|Jean Daunizeau||Brain and Spine Institute, ICM, France|
|Tore Erdmann||Scuola Internazionale Superiore di Studi Avanzati, Italy|
|Stefan Frässle||University of Zurich & ETH Zurich, Switzerland|
|Marta Garrido||University of Melbourne, Australia|
|Jakob Heinzle||University of Zurich & ETH Zurich, Switzerland|
|Marcus Herdener||University of Zurich, Switzerland|
|Philipp Homan||University Hospital of Psychiatry Zurich, Switzerland|
|Sandra Iglesias||University of Zurich & ETH Zurich, Switzerland|
|Sahib Khalsa||Laureate Institute for Brain Research, United States|
|Roland von Känel||University Hospital Zurich, Switzerland|
|Andre Marquand||Donders Institute, Netherlands|
|Christoph Mathys||Scuola Inernazionale Superiore di Studi Avanzati, Italy|
|Gina Paolini||Klinik für Psychiatrie und Psychotherapie, Clienia Schlössli AG Switzerland|
|Thomas Parr||UCL London, UK|
|Mads Lund Pedersen||University of Oslo, Norway|
|Frederike Petzschner||Brown University, USA|
|Lionel Rigoux||Max Planck Institute Cologne, Germany|
|Helen Schmidt||University of Zurich & ETH Zurich, Switzerland|
|Philipp Schwartenbeck||UCL London, UK|
|Jakob Siemerkus||University of Zurich & ETH Zurich, Switzerland|
|Klaas Enno Stephan||University of Zurich & ETH Zurich, Switzerland|
|Lilian Weber||University of Zurich & ETH Zurich, Switzerland|
|Thomas Wolfers||Donders Institute, Netherlands|
|Yu Yao||University of Zurich & ETH Zurich, Switzerland|
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.