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227-0973-00L 8 Credits MSC , WBZ D-HEST , D-MAVT , D-MATH , D-PHYS , D-INFK , D-ITET
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Translational Neuromodeling

Lecturers & Examiners: Prof. Dr. Klaas Stephan
VVZ CR 5.0

Last Updated: 2026-02-05 15:55:09

Abstract

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 teaches (hierarchical) Bayesian models of neuroimaging data and behaviour, incl. exercises.

Objective

To obtain an understanding of the goals, concepts and methods of Translational Neuromodeling and Computational Psychiatry/Psychosomatics, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data.

Content

This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for inferring mechanisms of brain diseases from neuroimaging and behavioural data) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). The first part of the course will introduce disease concepts from psychiatry and psychosomatics, their history, and clinical priority problems. The second part of the course concerns computational modeling of neuronal and cognitive processes for clinical applications. A particular focus is on Bayesian methods and generative models, for example, dynamic causal models for inferring neuronal processes from neuroimaging data, and hierarchical Bayesian models for inference on cognitive processes from behavioural data. The course discusses the mathematical and statistical principles behind these models, illustrates their application to various psychiatric diseases, and outlines a general research strategy based on generative models. Lecture topics include: 1. Introduction to Translational Neuromodeling and Computational Psychiatry/Psychosomatics 2. Psychiatric nosology 3. Pathophysiology of psychiatric disease mechanisms 4. Principles of Bayesian inference and generative modeling 5. Variational Bayes (VB) 6. Bayesian model selection 7. Markov Chain Monte Carlo techniques (MCMC) 8. Bayesian frameworks for understanding psychiatric and psychosomatic diseases 9. Generative models of fMRI data 10. Generative models of electrophysiological data 11. Generative models of behavioural data 12. Computational concepts of schizophrenia, depression and autism 13. Model-based predictions about individual patients Practical exercises include mathematical derivations and the implementation of specific models and inference methods. In additional project work, students are required to use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question. Group work (up to 3 students) is required.

Resources

Literature

See TNU website: https://www.tnu.ethz.ch/en/teaching

General Information

Language
English
Levels
MSC , WBZ
Frequency
Yearly recurring

Examination

Type
graded semester performance
Students are required to use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question (a real or fictitious one).This model is to be submitted as open source code (in MATLAB, Julia or Python), and the motivation and results are presented in a 15 min oral presentation followed by 15 min critical discussion. Group work (4-5 students) is required. The submitted code must be executable without any dependencies on specific operating systems or local setups.Grading will depend on (i) originality of the question that is addressed, (ii) clarity, technical correctness and practicability of the code, (iii) the quality and clarity of the oral presentation, (iv) the quality and clarity of the written project report.The code is to be submitted by 03.06.2021; the oral presentations take place on 04.06.2021.Admission to the final project is subject to students having successfully obtained at least 40% of the points for each exercise (1 miss allowed) during the semester.

Course Components

Type Title Time & Place Hours
lecture Translational Neuromodeling
  • Tue 09:15-12:00 (HG G 26.1)
3 h weekly
exercise Translational Neuromodeling
  • Fri 14:15-16:00 (ETZ E 6)
2 h weekly
independent project Translational Neuromodeling
No presence required. Creative work on a self-chosen project outside the regular weekly exercises.
No time listed 1 h weekly

Offered In