227-0973-00L  Translational Neuromodeling

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


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)