Translational Neuromodeling
Summary
This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and discusses (hierarchical) Bayesian models of neuroimaging data and behaviour in detail.
Lecture slides and exercise sheets will be published below on the day of the respective lecture. Solutions to the exercises are due at 2pm on the day of the exercise. Please send-in your solutions via email to tnu-teaching(at)biomed.ee.ethz.ch or hand them in at the beginning of the exercise session. To download the slides, please use the password distributed during the first lecture. Slides and exercises will be distributed via moodle.
Please note that admission to the final project is subject to having successfully completed at least 50% of the exercises during the semester. For more information, please refer to the course catalogue.
Tentative Schedule
Lectures take place on Tuesdays from 9:15am to 12:00pm in HG G26.1 (ETH main building). Exercises take place on Fridays from 2:15pm to 4:00pm in ETZ E6 (Gloriastrasse).
| Date | Lecture | Date | Exercise |
|---|---|---|---|
| 02/18 | Introduction to Translational Neuromodeling & Computational Psychiatry | 02/21 | No exercise |
| 02/25 | Psychiatric nosology and pathophysiology | 02/28 | Basics of statistical modeling |
| 03/03 | Generative models and principles of Bayesian inference | 03/06 | No exercise |
| 03/10 | A Bayesian framework for understanding psychiatric and psychosomatic diseases | 03/13 | Exercise 1: Bayesian inference |
| 03/17 | Generative models of behavioural data: HGF and Predictive Coding | 03/20 | Crash course: fMRI data analysis |
| 03/24 | Generative models of fMRI data: DCM for fMRI | 03/27 | Exercise 2: HGF |
| 03/31 | Generative models of EEG data: DCM for EEG | 04/03 | Exercise 3: DCM for fMRI |
| 04/07 | Computational concepts of schizophrenia, autism, and depression | 04/10 | No exercise (Easter week) |
| 04/14 | No lectures (Easter week) | 04/17 | No exercise (Easter week) |
| 04/21 | Clinical applications in Computational Psychiatry | 04/24 | Exercise 4: DCM for EEG |
| 04/28 | Variational Bayes | 05/01 | No exercise (public holiday) |
| 05/05 | Bayesian model selection | 05/08 | Exercise 5: Variational Bayes |
| 05/12 | Markov Chain Monte Carlo | 05/15 | Exercise 6: Bayesian model selection |
| 05/19 | Project work (No presence required) | 05/22 | Project work/ Exercise MCMC |
| 05/26 | Project work (No presence required) | 05/29 | Project presentations (2pm - 6pm) |
Project
General Information
For the project you may work in groups of up to 3 students. Please sign-up your group until 19 May 2020 by sending an email to the TAs containing the names of all group members and a tentative project title (if available). If you do not sign-up until May 19th, we will assume you are working on your project alone. Each group is expected to prepare a presentation of its project and hand in a report one week after the presentation. Please note that admission to the final project is subject to having successfully completed at least 50% of the exercises during the semester.
Additional Information
If you have questions concerning the lecture or exercise, you can contact the TAs via email: tnu-teaching(at)biomed.ee.ethz.ch.
For further information, please consult the course catalogue.