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Klaas Stephan: Katalogdaten im Frühjahrssemester 2019

NameHerr Prof. Dr. Klaas Stephan
LehrgebietTranslationale Neuromodellierung und Komputationale Psychiatrie
Adresse
Professur f. Transl. Neuromodeling
ETH Zürich, WIL G 203
Wilfriedstrasse 6
8032 Zürich
SWITZERLAND
Telefon+41 44 634 91 25
Fax+41 44 634 91 31
E-Mailstephan@biomed.ee.ethz.ch
DepartementInformationstechnologie und Elektrotechnik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
227-0967-00LComputational Neuroimaging Clinic Information 3 KP2VK. Stephan
KurzbeschreibungThis seminar teaches problem solving skills for computational neuroimaging (incl. associated computational analyses of behavioural data). It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g., concerning mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data.
Lernziel1. Consolidation of theoretical knowledge (obtained in one of the following courses: 'Methods & models for fMRI data analysis', 'Translational Neuromodeling', 'Computational Psychiatry') in a practical setting.
2. Acquisition of practical problem solving strategies for computational modeling of neuroimaging data.
InhaltThis seminar teaches problem solving skills for computational neuroimaging (incl. associated computational analyses of behavioural data). It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g., concerning mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data.
Voraussetzungen / BesonderesThe participants are expected to be familiar with general principles of statistics and have successfully completed at least one of the following courses:
'Methods & models for fMRI data analysis',
'Translational Neuromodeling',
'Computational Psychiatry'
227-0970-00LResearch Topics in Biomedical Engineering Information 0 KP2KK. P. Prüssmann, M. Stampanoni, K. Stephan, J. Vörös
KurzbeschreibungCurrent topics in Biomedical Engineering presented mostly by external speakers from academia and industry.
Lernzielsee above
227-0973-00LTranslational Neuromodeling Information 8 KP3V + 2U + 1AK. 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 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.
LiteraturSee TNU website:
https://www.tnu.ethz.ch/en/teaching.html
Voraussetzungen / BesonderesKnowledge of principles of statistics, programming skills (MATLAB or Python)
227-0974-00LTNU Colloquium Belegung eingeschränkt - Details anzeigen 0 KP2KK. Stephan
KurzbeschreibungThis colloquium discusses research topics in Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and Computational Psychiatry/Psychosomatics (the application of these models to concrete clinical questions). The range of topics is broad, incl. computational techniques, experimental paradigms (fMRI, EEG, behaviour), and clinical questions.
Lernzielsee above