227-0973-00L Translational Neuromodeling
| Semester | Spring Semester 2023 |
| Lecturers | K. Stephan |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 227-0973-00 V | Translational Neuromodeling | 3 hrs |
| K. Stephan | ||||||
| 227-0973-00 U | Translational Neuromodeling | 2 hrs |
| K. Stephan | ||||||
| 227-0973-00 A | Translational Neuromodeling No presence required. Creative work on a self-chosen project outside the regular weekly exercises. | 1 hrs | K. Stephan |
Catalogue data
| Abstract | This course provides an introduction to Translational Neuromodeling (the development of computational assays of neuronal and cognitive processes) 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 and project work. | ||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | 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. | ||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course provides a systematic introduction to Translational Neuromodeling (the development of computational assays of neuronal and cognitive processes) 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 and depression 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. Please note that some of the exercises involve the use of open source software in Matlab. | ||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | See TNU website: https://www.tnu.ethz.ch/en/teaching | ||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Good knowledge of principles of statistics, good programming skills (the majority of the open source software tools used is in MATLAB; for project work, Julia can also be used) | ||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 8 credits |
| Examiners | K. Stephan |
| Type | graded semester performance |
| Language of examination | English |
| Repetition | Repetition only possible after re-enrolling for the course unit. |
| Admission requirement | Good knowledge of principles of statistics, good programming skills (MATLAB is required; Julia an additional bonus). |
| Additional information on mode of examination | Students are required to use one of the examples discussed in the course as a basis for either developing their own generative model or for applying an existing model to a clinical question in an original manner. The model/analysis is to be submitted as open source code (in MATLAB or Julia), and the motivation and results are presented in a 15 min oral presentation followed by 15 min critical discussion. Group work (up to 3 students) is required. The submitted code must be executable without any dependencies on specific operating systems or local setups. Grading will depend on the (i) originality of the question that is addressed, (ii) quality and degree of completion of the modeling, (iii) clarity and functionality of the code, (iv) quality and clarity of the oral presentation, (iv) quality and clarity of the written project report. The code is to be submitted by 1 June 2023 (23:59 CET); the oral presentations take place on 2 June 2023. Admission to the final project is subject to students having successfully obtained at least 40% of the points for each exercise (1 miss allowed) during the semester. |
Learning materials
| No public learning materials available. | |
| Only public learning materials are listed. |
Groups
| No information on groups available. |
Restrictions
| Places | 24 at the most |
| Waiting list | until 12.03.2023 |