227-0973-00L  Translational Neuromodeling

SemesterFrühjahrssemester 2022
DozierendeK. Stephan
Periodizitätjährlich wiederkehrende Veranstaltung


227-0973-00 VTranslational Neuromodeling3 Std.
Di09:15-12:00HG G 26.1 »
K. Stephan
227-0973-00 UTranslational Neuromodeling2 Std.
Fr14:15-16:00ETZ E 6 »
03.06.08:15-13:00HG F 26.1 »
K. Stephan
227-0973-00 ATranslational Neuromodeling
No presence required.
Creative work on a self-chosen project outside the regular weekly exercises.
1 Std.K. Stephan


KurzbeschreibungThis 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.
LernzielTo 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.
InhaltThis 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. 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 either develop a novel generative model (and demonstrate its properties in simulations) or devise novel applications of an existing model to empirical data in order to address a clinical question. Group work (up to 3 students) is required.
LiteraturSee TNU website:
Voraussetzungen / BesonderesGood knowledge of principles of statistics, good programming skills (the majority of the open source software tools used is in MATLAB; for project work, Julia or Python can also be used)


Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte8 KP
PrüfendeK. Stephan
Formbenotete Semesterleistung
RepetitionRepetition nur nach erneuter Belegung der Lerneinheit möglich.
ZulassungsbedingungGood knowledge of principles of statistics,
good programming skills (MATLAB, Julia, or Python).
Zusatzinformation zum PrüfungsmodusStudents are required to use one of the examples discussed in the course as a basis for either developing their own generative model or for applying an existing model to a clinical question in an original manner.
The model/ analysis 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 (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 (i) originality of the question that is addressed, (ii) quality and degree of completion of the modeling, (iii) clarity and functionality of the code, (iv) 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 02.06.2022; the oral presentations take place on 03.06.2022
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.


Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.


Keine Informationen zu Gruppen vorhanden.


PlätzeMaximal 24
WartelisteBis 06.03.2022

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