Klaas Stephan: Catalogue data in Spring Semester 2019 |
| Name | Prof. Dr. Klaas Stephan |
| Field | Translational Neuromodelling and Computational Psychiatry |
| Address | Professur f. Transl. Neuromodeling ETH Zürich, WIL G 203 Wilfriedstrasse 6 8032 Zürich SWITZERLAND |
| Telephone | +41 44 634 91 25 |
| Fax | +41 44 634 91 31 |
| stephan@biomed.ee.ethz.ch | |
| Department | Information Technology and Electrical Engineering |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 227-0967-00L | Computational Neuroimaging Clinic  | 3 credits | 2V | K. Stephan | |
| Abstract | This 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. | ||||
| Learning objective | 1. 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. | ||||
| Content | This 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. | ||||
| Prerequisites / Notice | The 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' | ||||
| 227-0970-00L | Research Topics in Biomedical Engineering | 0 credits | 2K | K. P. Prüssmann, M. Stampanoni, K. Stephan, J. Vörös | |
| Abstract | Current topics in Biomedical Engineering presented mostly by external speakers from academia and industry. | ||||
| Learning objective | see above | ||||
| 227-0973-00L | Translational Neuromodeling | 8 credits | 3V + 2U + 1A | K. Stephan | |
| 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. | ||||
| Learning 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 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. | ||||
| Literature | See TNU website: https://www.tnu.ethz.ch/en/teaching.html | ||||
| Prerequisites / Notice | Knowledge of principles of statistics, programming skills (MATLAB or Python) | ||||
| 227-0974-00L | TNU Colloquium  | 0 credits | 2K | K. Stephan | |
| Abstract | This 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. | ||||
| Learning objective | see above | ||||