Klaas Stephan: Katalogdaten im Frühjahrssemester 2018 |
Name | Herr Prof. Dr. Klaas Stephan |
Lehrgebiet | Translationale 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 |
stephan@biomed.ee.ethz.ch | |
Departement | Informationstechnologie und Elektrotechnik |
Beziehung | Ordentlicher Professor |
Nummer | Titel | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|
227-0967-00L | Computational Neuroimaging Clinic | 3 KP | 2V | K. Stephan | |
Kurzbeschreibung | 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. | ||||
Lernziel | 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. | ||||
Inhalt | 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. . | ||||
Voraussetzungen / Besonderes | 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' | ||||
227-0970-00L | Research Topics in Biomedical Engineering | 0 KP | 2K | K. P. Prüssmann, M. Rudin, M. Stampanoni, K. Stephan, J. Vörös | |
Kurzbeschreibung | Current topics in Biomedical Engineering presented mostly by external speakers from academia and industry. | ||||
Lernziel | see above | ||||
227-0973-00L | Translational Neuromodeling | 6 KP | 4G | K. Stephan | |
Kurzbeschreibung | 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 discusses (hierarchical) Bayesian models of neuroimaging data and behaviour in detail. | ||||
Lernziel | 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. | ||||
Inhalt | 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, e.g. dynamic causal models (DCMs) 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. In the practical exercises, students are asked to program their own generative model (in MATLAB) and use it for simulations and inference from real fMRI or behavioural data. | ||||
Literatur | See TNU website: https://www.tnu.ethz.ch/en/teaching.html | ||||
Voraussetzungen / Besonderes | Basic statistical knowledge, MATLAB programming skills | ||||
227-0974-00L | TNU Colloquium | 0 KP | 2K | K. Stephan | |
Kurzbeschreibung | 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. | ||||
Lernziel | see above |