Klaas Stephan: Catalogue data in Autumn Semester 2021

Name Prof. Dr. Klaas Stephan
FieldTranslational 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
E-mailstephan@biomed.ee.ethz.ch
DepartmentInformation Technology and Electrical Engineering
RelationshipFull Professor

NumberTitleECTSHoursLecturers
227-0967-00LComputational Neuroimaging Clinic
Prerequisite: Successful completion of course "Methods & Models for fMRI Data Analysis", "Translational Neuromodeling" or "Computational Psychiatry"
3 credits2VK. Stephan
AbstractThis 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.
Objective1. 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.
ContentThis 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 / NoticeThe participants are expected to have successfully completed at least one of the following courses:
'Methods & models for fMRI data analysis',
'Translational Neuromodeling',
'Computational Psychiatry'
227-0969-00LMethods & Models for fMRI Data Analysis6 credits4VK. Stephan
AbstractThis 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.
ObjectiveTo obtain in-depth knowledge of the theoretical foundations of SPM
and DCM and of their practical application to empirical fMRI data.
ContentThis 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.
227-0970-00LResearch Topics in Biomedical Engineering
Does not take place this semester.
0 credits1KK. P. Prüssmann, S. Kozerke, M. Stampanoni, K. Stephan, J. Vörös
AbstractCurrent topics in Biomedical Engineering presented by speakers from academia and industry.
ObjectiveGetting insight into actual areas and problems of Biomedical Engineering an Health Care.
227-0971-00LComputational Psychiatry
Please note that participation in this course and the practical sessions requires additional registration at: Link
3 credits4SK. Stephan
AbstractThis 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.
ObjectiveThis 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.
ContentThis 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.
227-0974-00LTNU Colloquium Restricted registration - show details 0 credits2KK. Stephan
AbstractThis 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.
Objectivesee above
ContentThis 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.
227-0976-00LComputational Psychiatry & Computational Psychosomatics Restricted registration - show details
Number of participants limited to 24.

Information for UZH students:
Enrolment to this course unit only possible at ETH Zurich.
No enrolment to module BMT20002.

Please mind the ETH enrolment deadlines for UZH students: Link
2 credits4SK. Stephan
AbstractThis 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.
ObjectiveUnderstanding strengths and weaknesses of current trends in the development of clinically relevant computational tools and their application to problems in psychiatry and psychosomatics.
ContentThis 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.
LiteratureLiterature 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 / NoticeParticipants 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).