227-0973-00L  Translational Neuromodeling

SemesterSpring Semester 2019
LecturersK. Stephan
Periodicityyearly recurring course
Language of instructionEnglish



Catalogue data

AbstractThis 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.
ObjectiveTo 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.
ContentThis 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). 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 mechanisms from neuroimaging data, and hierarchical Bayesian models for inference on cognitive mechanisms 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 or 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 permitted.
LiteratureSee TNU website:
https://www.tnu.ethz.ch/en/teaching.html
Prerequisites / NoticeKnowledge of principles of statistics, programming skills (MATLAB or Python)

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits8 credits
ExaminersK. Stephan
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationStudents 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 or Python), and the motivation and results are presented in a 10 min oral presentation followed by critical discussion. Group work (up to 3 students) is permitted. The submitted code must be executable without any dependencies on specific operating systems or local setups (e.g., no absolute pathnames). Grading will depend on (i) originality of the question that is addressed, (ii) clarity, technical correctness and practicability of the code, (iii) the quality of the oral presentation and discussion in the report. The code is to be submitted by 30 May 2019; the oral presentations take place on 31 May (12-18h).

Learning materials

 
Main linkTranslational Neuromodeling
Only public learning materials are listed.

Courses

NumberTitleHoursLecturers
227-0973-00 VTranslational Neuromodeling3 hrs
Tue09-12HG F 26.3 »
K. Stephan
227-0973-00 UTranslational Neuromodeling2 hrs
Fri14-16ETZ E 6 »
31.05.13-20ETZ E 6 »
K. Stephan
227-0973-00 ATranslational Neuromodeling
No presence required.
Creative task outside the regular weekly exercises.
1 hrsK. Stephan

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

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