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 discusses (hierarchical) Bayesian models of neuroimaging data and behaviour in detail.
Lernziel
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.
Inhalt
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, e.g. dynamic causal models (DCMs) for inferring neuronal mechanisms from neuroimaging data, and hierarchical Bayesian models for inference on cognitive mechanisms 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. In the practical exercises, students are asked to program their own generative model (in MATLAB) and use it for simulations and inference from real fMRI or behavioural data.
Die Leistungskontrolle wird nur am Semesterende nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung möglich.
Zusatzinformation zum Prüfungsmodus
Students are asked to program their own generative model (in MATLAB) and use it for simulations and inference from real fMRI or behavioural data, together with a presentation and critical discussion of their work in a report. Group work (up to 3 students) is permitted. Grading will depend on (i) clarity and technical correctness of the code, (ii) the quality and sophistication of the model and the simulations, and (iii) the quality of the presentation and discussion in the report.
Leistungskontrolle als Semesterkurs