Klaas Stephan: Catalogue data in Spring Semester 2022

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 Clinic3 credits2VK. Stephan
AbstractThis seminar teaches problem solving skills for computational neuroimaging (incl. associated computational analyses of 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., concerning 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 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.
ContentThis seminar teaches problem solving skills for computational neuroimaging (incl. associated computational analyses of 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., concerning mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data.
Prerequisites / NoticeThe participants are expected to be familiar with general principles of statistics and have successfully completed at least one of the following courses:
'Methods & models for fMRI data analysis',
'Translational Neuromodeling',
'Computational Psychiatry'
227-0973-00LTranslational Neuromodeling Restricted registration - show details 8 credits3V + 2U + 1AK. Stephan
AbstractThis 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 teaches (hierarchical) Bayesian models of neuroimaging data and behaviour, incl. exercises.
ObjectiveTo 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.
ContentThis course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for inferring mechanisms of brain diseases from neuroimaging and behavioural data) 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, for example, dynamic causal models for inferring neuronal processes from neuroimaging data, and hierarchical Bayesian models for inference on cognitive processes 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.

Lecture topics include:
1. Introduction to Translational Neuromodeling and Computational Psychiatry/Psychosomatics
2. Psychiatric nosology
3. Pathophysiology of psychiatric disease mechanisms
4. Principles of Bayesian inference and generative modeling
5. Variational Bayes (VB)
6. Bayesian model selection
7. Markov Chain Monte Carlo techniques (MCMC)
8. Bayesian frameworks for understanding psychiatric and psychosomatic diseases
9. Generative models of fMRI data
10. Generative models of electrophysiological data
11. Generative models of behavioural data
12. Computational concepts of schizophrenia, depression and autism
13. Generative embedding: Model-based predictions about individual patients

Practical exercises include mathematical derivations and the implementation of specific models and inference methods. In additional project work, students are required to either develop a novel generative model (and demonstrate its properties in simulations) or devise novel applications of an existing model to empirical data in order to address a clinical question. Group work (up to 3 students) is required.
LiteratureSee TNU website:
https://www.tnu.ethz.ch/en/teaching
Prerequisites / NoticeGood knowledge of principles of statistics, good programming skills (the majority of the open source software tools used is in MATLAB; for project work, Julia or Python can also be used)
227-0974-00LTNU Colloquium Information Restricted registration - show details 0 credits2KK. Stephan
AbstractThis 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.
Objectivesee above
227-0976-00LComputational Psychiatry & Computational Psychosomatics Restricted registration - show details
Does not take place this semester.
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. 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
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.html
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).
377-0601-00LPsychiatry & Computational Psychiatry Restricted registration - show details
Only for Human Medicine BSc
2 credits2GK. Stephan, H. Schmidt, J. Siemerkus
AbstractThe module Psychiatrie & Computational Psychiatry introduces the most common psychiatric disorders, including etiology, diagnostics and therapy. In addition, under the guidance of a clinician, the students will acquire practical skills for assessing psychopathology in patients. The module concludes with an introduction to concepts and practical applications of Computational Psychiatry.
ObjectiveAfter completing the module “Psychiatrie & Computational Psychiatry”, the students are expected to show proficiency in:
1. Taking a complete psychiatric history
2. Conducting a psychiatric interview based on methods of clinical communication
3. Developing a trustful and unbiased physician-patient contact
4. Conducting a psychopathological assessment according to the AMPD system
5. Knowing the diagnosis criteria according to ICD-10 for common psychiatric disorders
6. Identifying and explaining disorder specific symptoms
7. Knowing disorder specific treatments
8. Knowing the effects, indications and side-effects of psychiatric medications
9. Knowing the basic concepts of psychotherapeutic methods
10. Dealing with one’s own emotions during difficult patient interviews
11. Knowing the basic concepts and methods of Computational Psychiatry
ContentThe module “Psychiatrie & Computational Psychiatry” introduces the most common psychiatric disorders, including their epidemiology, etiology, pathogenesis, symptoms, diagnosis, and treatment. This applies to the following disorders:
- Depressive disorders and bipolar disorders
- schizophrenia spectrum disorders
- autism spectrum disorder
- addiction
- anxiety disorders
- obsessive-compulsive disorder
- dementia
- personality disorders

Additionally, the course conveys knowledge about the most common psychiatric emergencies and their treatments.

Further topics of the module include:
- Introduction to the concepts of ICD and DSM
- Communication and interaction with people with mental disorders
- Psychiatric history
- General and disorder-specific introduction to psychopathology
- Psychopathological assessment (according to AMDP) with focus on:
disorders of consciousness, disturbances of orientation, disturbances of attention and memory, formal thought disorders, worries and compulsions, delusions, disorders of perception, Ego (boundary) disturbances, disturbances of affect, disorders of drive and psychomotor activity, circadian disturbances
- Introduction to Computational Psychiatry, including general concepts and methods (e.g. Bayesian brain theory, mathematical models of brain activity measurements) and computational theories of schizophrenia, autism, psychosomatics, depression, fatigue and mindfulness
Prerequisites / NoticeVoraussetzung:
alle medizinischen und klinischen Module der 1. - 5. Semester