227-0973-00L  Translational Neuromodeling

SemesterSpring Semester 2022
LecturersK. Stephan
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
227-0973-00 VTranslational Neuromodeling3 hrs
Tue09:15-12:00HG G 26.1 »
K. Stephan
227-0973-00 UTranslational Neuromodeling2 hrs
Fri14:15-16:00ETZ E 6 »
03.06.08:15-13:00HG F 26.1 »
K. Stephan
227-0973-00 ATranslational Neuromodeling
No presence required.
Creative work on a self-chosen project outside the regular weekly exercises.
1 hrsK. Stephan

Catalogue data

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:
Link
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)

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits8 credits
ExaminersK. Stephan
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Admission requirementGood knowledge of principles of statistics,
good programming skills (MATLAB, Julia, or Python).
Additional information on mode of examinationStudents 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, Julia or Python), 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 (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) the quality and clarity of the oral presentation, (iv) the quality and clarity of the written project report.
The code is to be submitted by 02.06.2022; the oral presentations take place on 03.06.2022
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

Places24 at the most
Waiting listuntil 06.03.2022

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