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

Translational Neuromodeling

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

Last Updated: 2026-06-03 00:14:06

Abstract

This course provides an introduction to Translational Neuromodeling (the development of computational assays of neuronal and cognitive processes) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and Bayesian models of neuroimaging data and behaviour, incl. exercises and project work.

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 computational assays of neuronal and cognitive processes) 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 and depression 13. Generative embedding: 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 apply one or several of the models they learned in the course to empirical data and address a clinical question. Group work (3 students per group) is required. PLEASE NOTE: for the exercises, knowledge of *both* MATLAB and Julia 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 models introduced by the course and apply it to empirical data in order to address a clinically relevant question.The model/analysis is to be submitted as open source code (in MATLAB or Julia). The motivation and results are presented in a 15 min oral presentation followed by 15 min critical discussion. Group work (up to 3 students) is required. The submitted code must be executable without any dependencies on specific operating systems or local setups.Grading will depend on the (i) originality of the chosen modeling approach, (ii) quality and degree of completion of the modeling, (iii) clarity and functionality of the code, (iv) quality and clarity of the oral presentation, (iv) quality and clarity of the written project report.The code is to be submitted by 28 May 2026 (23:59 CET) at the latest; the oral presentations take place on 29 May 2026 (exact time to be determined).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.

Registration & Places

Limited places (Special selection)
Signup End
01.03.2026

Course Components

Type Title Time & Place Hours
lecture Translational Neuromodeling
A maximum of 30 students will be permitted.
No time listed 3 h weekly
exercise Translational Neuromodeling
  • Fri 14:15-16:00 (ETZ E 7)
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