Klaas Stephan: Catalogue data in Autumn Semester 2023 |
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 | ||||||||||||||||||||||||||||||||
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227-0967-00L | Computational Neuroimaging Clinic Prerequisite: Successful completion of course "Methods & Models for fMRI Data Analysis", "Translational Neuromodeling" or "Computational Psychiatry" | 3 credits | 2V | K. Stephan | ||||||||||||||||||||||||||||||||
Abstract | This seminar teaches problem solving skills for computational neuroimaging, based on 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 the setting of concrete research questions. 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 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' | |||||||||||||||||||||||||||||||||||
Competencies![]() |
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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 practical application to empirical fMRI data. | |||||||||||||||||||||||||||||||||||
Content | This course teaches state-of-the-art methods and models for fMRI data analysis in lectures and exercises. 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 clinical studies in psychiatry and neurology. Practical exercises serve to consolidate the skills taught in lectures. | |||||||||||||||||||||||||||||||||||
227-0971-00L | Computational Psychiatry Please note that participation in this course and the practical sessions requires additional registration at: http://www.translationalneuromodeling.org/cpcourse/ | 3 credits | 4S | K. Stephan | ||||||||||||||||||||||||||||||||
Abstract | This six-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 six-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. Furthermore, practical exercises provide in-depth exposure to different software packages. Please see http://www.translationalneuromodeling.org/cpcourse/ for details. | |||||||||||||||||||||||||||||||||||
227-0974-00L | TNU Colloquium ![]() | 0 credits | 2K | K. Stephan | ||||||||||||||||||||||||||||||||
Abstract | This colloquium for MSc/PhD students at D-ITET discusses research in Translational Neuromodeling (development of mathematical models for diagnostics of brain diseases) and application to Computational Psychiatry/Psychosomatics. The range of topics is broad, incl. computational (generative) modeling, experimental paradigms (fMRI, EEG, behaviour), and clinical questions. | |||||||||||||||||||||||||||||||||||
Learning objective | see above | |||||||||||||||||||||||||||||||||||
Content | This colloquium for MSc/PhD students at D-ITET discusses research in Translational Neuromodeling (development of mathematical models for diagnostics of brain diseases) and application to Computational Psychiatry/Psychosomatics. The range of topics is broad, incl. computational (generative) modeling, experimental paradigms (fMRI, EEG, behaviour), and clinical questions. | |||||||||||||||||||||||||||||||||||
227-0976-00L | Computational Psychiatry & Computational Psychosomatics ![]() Does not take place this semester. Information for UZH students: Enrolment to this course unit only possible at ETH Zurich. No enrolment to module 04SMBMT20002. Please mind the ETH enrolment deadlines for UZH students: Link | 2 credits | 4S | K. Stephan | ||||||||||||||||||||||||||||||||
Abstract | This seminar deals with the development of clinically relevant computational tools and/or their application to psychiatry and psychosomatics. It is complementary to the annual Computational Psychiatry Course and serves to build bridges between computational scientists and clinicians. It is designed to foster in-depth exchange, with ample time for discussion. | |||||||||||||||||||||||||||||||||||
Learning objective | Understanding strengths and weaknesses of current trends in the development of clinically relevant computational tools and their application to problems in psychiatry and psychosomatics. | |||||||||||||||||||||||||||||||||||
Content | This seminar deals with the development of computational tools (e.g. generative models, machine learning) and/or their application to psychiatry and psychosomatics. The seminar includes (i) presentations by computational scientists and clinicians, (ii) group discussion with focus on methodology and clinical utility, (iii) self-study based on literature provided by presenters. | |||||||||||||||||||||||||||||||||||
Literature | Literature for additional self-study of the topics presented in this seminar will be provided by the presenters and will be available online at https://www.tnu.ethz.ch/en/teaching | |||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Participants are expected to be familiar with general principles of statistics (including Bayesian statistics) and have successfully completed the course “Computational Psychiatry” (Course number 227-0971-00L). |