Klaas Stephan: Catalogue data in Spring Semester 2015 |
| 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 the design and analysis of neuroimaging data (fMRI, EEG). It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich. Examples may include mass-univariate and multivariate analyses of fMRI data, dynamic causal modeling of fMRI and EEG data. | ||||
| Learning objective | 1. Consolidation of theoretical knowledge (obtained in the 'Methods & models for fMRI data analysis' lecture) 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 the design and analysis of neuroimaging data (fMRI, EEG). It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich. Examples may include mass-univariate and multivariate analyses of fMRI data, dynamic causal modeling of fMRI and EEG data. | ||||
| 227-0970-00L | Research Topics in Biomedical Engineering | 1 credit | 2K | K. P. Prüssmann, M. Rudin, 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 | ||||
| 227-0971-00L | Computational Psychiatry | 3 credits | 2S | K. Stephan | |
| Abstract | Current methods and concepts for deciphering mechanisms of maladaptive behaviour, such as aberrant learning and decision-making in healthy individuals and psychiatric patients. The key goal is to connect methodological training with biological and clinical knowledge about the phenomenology and pathophysiology of psychiatric and neurological diseases. | ||||
| Learning objective | To understand current concepts about computational and physiological mechanisms of maladaptive behaviour and psychiatric diseases. | ||||
| Content | In this seminar, we discuss current methods and concepts for deciphering mechanisms of maladaptive behaviour, such as aberrant learning and decision-making in healthy individuals and psychiatric patients. The key goal is to connect methodological training (in computational and statistical techniques for analyzing behavioural, fMRI and EEG data) with biological and clinical knowledge about the phenomenology and pathophysiology of psychiatric and neurological diseases. This seminar aims at bridging the gap between mathematical modelers and clinical neuroscientists, enabling more effective communication and joint translational research. To this end, each semester a novel topic is chosen which is examined both from clinical/biological and modeling perspectives. | ||||
| 227-0973-00L | Translational Neuromodeling | 3 credits | 2V | K. Stephan | |
| Abstract | This lecture deals with computational modeling of neuronal and cognitive processes for diagnostic applications in psychiatry ("Translational Neuromodeling"). A particular focus is on Bayesian methods and generative models, e.g. dynamic system models for inferring neuronal mechanisms from neuroimaging data, and hierarchical learning models for inference on cognitive mechanisms from behaviour. | ||||
| Learning objective | To obtain an understanding of the goals and methods of translational neuromodeling, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data. | ||||
| Content | This lecture deals with computational modeling of neuronal and cognitive processes for diagnostic applications in psychiatry ("translational neuromodeling"). A particular focus is on Bayesian methods and generative models, e.g. dynamic causal models (DCMs) for inferring neuronal mechanisms from neuroimaging data, and hierarchical learning models for inference on cognitive mechanisms from behavioural data. The course illustrates the application of these models to various psychiatric diseases and outlines a general research strategy. | ||||
| Literature | See TNU website: http://www.biomed.ee.ethz.ch/research/tnu/teaching | ||||
| Prerequisites / Notice | Basic knowledge of Bayesian statistics, MATLAB programming skills | ||||
| 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 | ||||