227-0973-00L Translational Neuromodeling
|jährlich wiederkehrende Veranstaltung
No presence required.
Creative work on a self-chosen project outside the regular weekly exercises.
|This 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.
|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.
|This 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). 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. 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 use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question. Group work (up to 3 students) is permitted.
|See TNU website:
|Voraussetzungen / Besonderes
|Good knowledge of principles of statistics, good programming skills (MATLAB or Python)
|Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
|Leistungskontrolle als Semesterkurs
|Repetition nur nach erneuter Belegung der Lerneinheit möglich.
|Good knowledge of principles of statistics,
good programming skills (MATLAB and/or Python).
|Zusatzinformation zum Prüfungsmodus
|Students are required to use one of the examples discussed in the course as a basis for developing their own generative model and use it for simulations and/or inference in application to a clinical question (a real or fictitious one).
This model is to be submitted as open source code (in MATLAB or Python), and the motivation and results are presented in a 10 min oral presentation followed by critical discussion. Group work (up to 3 students) is permitted. The submitted code must be executable without any dependencies on specific operating systems or local setups (e.g., no absolute pathnames).
Grading will depend on (i) originality of the question that is addressed, (ii) clarity, technical correctness and practicability of the code, (iii) the quality of the oral presentation and discussion in the report.
The code is to be submitted by 28 May 2020; the oral presentations take place on 29 May 2020 (12-18h).
Admission to the final project is subject to students having successfully completed at least 50% of the exercises during the semester.
|Es werden nur die öffentlichen Lernmaterialien aufgeführt.
|Keine Informationen zu Gruppen vorhanden.
|Keine zusätzlichen Belegungseinschränkungen vorhanden.