Klaas Stephan: Catalogue data in Autumn Semester 2020

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
E-mail	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", "Translational Neuromodeling" or "Computational Psychiatry"	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 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.
227-0970-00L	Research Topics in Biomedical Engineering	0 credits	2K	K. P. Prüssmann, S. Kozerke, 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 Please note that participation in this course and the practical sessions requires additional registration until 23.8.2020 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 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 credits	4S	K. Stephan
Abstract	This seminar deals with the development of clinically relevant computational tools and/or their application to psychiatry and psychosomatics. Complementary to the annual Computational Psychiatry Course, it serves to build bridges between computational scientists and clinicians and 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/de/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).
701-0901-00L	ETH Week 2020: Health for Tomorrow Does not take place this semester. This lecture is cancelled for 2020. If possible the lecture will be conducted in Autumn Semester 2021.	1 credit	3S	S. Brusoni, A. Burden, R. Knutti, I. Mansuy, K. Stephan, A. Vaterlaus, E. Vayena
Abstract	ETH Week is an innovative one-week course designed to foster critical thinking and creative learning. Students from all departments as well as professors and external experts will work together in interdisciplinary teams. They will develop interventions that could play a role in solving some of our most pressing global challenges. In 2020, ETH Week will focus on the topic of health and well-being.
Learning objective	- Domain specific knowledge: Students have immersed knowledge about a certain complex, societal topic which will be selected every year. They understand the complex system context of the current topic, by comprehending its scientific, technical, political, social, ecological and economic perspectives. - Analytical skills: The ETH Week participants are able to structure complex problems systematically using selected methods. They are able to acquire further knowledge and to critically analyse the knowledge in interdisciplinary groups and with experts and the help of team tutors. - Design skills: The students are able to use their knowledge and skills to develop concrete approaches for problem solving and decision making to a selected problem statement, critically reflect these approaches, assess their feasibility, to transfer them into a concrete form (physical model, prototypes, strategy paper, etc.) and to present this work in a creative way (role-plays, videos, exhibitions, etc.). - Self-competence: The students are able to plan their work effectively, efficiently and autonomously. By considering approaches from different disciplines they are able to make a judgment and form a personal opinion. In exchange with non-academic partners from business, politics, administration, nongovernmental organisations and media they are able to communicate appropriately, present their results professionally and creatively and convince a critical audience. - Social competence: The students are able to work in multidisciplinary teams, i.e. they can reflect critically their own discipline, debate with students from other disciplines and experts in a critical-constructive and respectful way and can relate their own positions to different intellectual approaches. They can assess how far they are able to actively make a contribution to society by using their personal and professional talents and skills and as "Change Agents".
Content	The week is mainly about problem solving and design thinking applied to the complex world of health and well-being. During ETH Week students will have the opportunity to work in small interdisciplinary groups, allowing them to critically analyse both their own approaches and those of other disciplines, and to integrate these into their work. While deepening their knowledge about health and well-being, students will be introduced to various methods and tools for generating creative ideas and understand how different people are affected by each part of the system. In addition to lectures and literature, students will acquire knowledge via excursions into the real world, empirical observations, and conversations with researchers and experts. A key attribute of the ETH Week is that students are expected to find their own problem, rather than just solve the problem that has been handed to them. Therefore, the first three days of the week will concentrate on identifying a problem the individual teams will work on, while the last two days are focused on generating solutions and communicating the team's ideas.
Prerequisites / Notice	No prerequisites. Programme is open to Bachelor and Masters from all ETH Departments. All students must apply through a competitive application process at www.ethz.ch/ethweek. Participation is subject to successful selection through this competitive process.