Klaas Stephan: Catalogue data in Autumn Semester 2016 |
| 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  Prerequisite: Successful completion of course "Methods & Models for fMRI Data Analysis" (227-0969-00L). | 3 credits | 2V | K. Stephan | |
| Abstract | This seminar teaches problem solving skills for computational neuroimaging, based on joint analyses of neuroimaging and 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. 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 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, based on joint analyses of neuroimaging and 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. 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 have successfully completed at least one of the following courses: 'Methods & models for fMRI data analysis', 'Translational Neuromodeling', 'Computational Psychiatry' | ||||
| 227-0969-00L | Methods & Models for fMRI Data Analysis | 6 credits | 4V | K. Stephan | |
| Abstract | This course teaches methods and models for fMRI data analysis, covering all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, statistical inference, multiple comparison corrections, event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data. | ||||
| Learning objective | To obtain in-depth knowledge of the theoretical foundations of SPM and DCM and of their application to empirical fMRI data. | ||||
| Content | This course teaches state-of-the-art methods and models for fMRI data analysis. It covers all aspects of statistical parametric mapping (SPM), incl. preprocessing, the general linear model, frequentist and Bayesian inference, multiple comparison corrections, and event-related designs, and Dynamic Causal Modelling (DCM), a Bayesian framework for identification of nonlinear neuronal systems from neurophysiological data. A particular emphasis of the course will be on methodological questions arising in the context of studies in psychiatry, neurology and neuroeconomics. | ||||
| 227-0970-00L | Research Topics in Biomedical Engineering | 0 credits | 2K | M. Rudin, S. Kozerke, K. P. Prüssmann, M. Stampanoni, K. Stephan, J. Vörös | |
| Abstract | Current topics in Biomedical Engineering presented by speakers from academia and industry. | ||||
| Learning objective | Getting insight into actual areas and problems of Biomedical Engineering an Health Care. | ||||
| 227-0971-00L | Computational Psychiatry | 3 credits | 4S | K. Stephan | |
| Abstract | This five-day course teaches state-of-the-art methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decision-making) and brain physiology (e.g., effective connectivity) of relevance for psychiatric disorders. The course not only provides theoretical background, but also demonstrates open source software in application to concrete examples. | ||||
| Learning objective | This course aims at bridging the gap between mathematical modelers and clinical neuroscientists by teaching computational techniques in the context of clinical applications. The hope is that the acquisition of a joint language and tool-kit will enable more effective communication and joint translational research between fields that are usually worlds apart. | ||||
| Content | This five-day course teaches state-of-the-art methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decision-making) and brain physiology (e.g., effective connectivity) of relevance for psychiatric disorders. The course not only provides theoretical background, but also demonstrates open source software in application to concrete examples. | ||||
| 227-0974-00L | TNU Colloquium  | 0 credits | 2K | K. Stephan | |
| Abstract | This colloquium for MSc and PhD students at D-ITET discusses current research topics in Translational Neuromodeling, a new discipline concerned with the development of mathematical models for diagnostics of brain diseases. The range of topics is broad, incl. statistics and computational modeling, experimental paradigms (fMRI, EEG, behaviour), and clinical questions. | ||||
| Learning objective | see above | ||||