227-0973-00L  Translational Neuromodeling

Lehrveranstaltungen

Katalogdaten

Kurzbeschreibung	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.
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 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.
Literatur	See TNU website: https://www.tnu.ethz.ch/en/teaching
Voraussetzungen / Besonderes	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)
Kompetenzen	Fachspezifische Kompetenzen Konzepte und Theorien geprüft Verfahren und Technologien geprüft Methodenspezifische Kompetenzen Analytische Kompetenzen geprüft Entscheidungsfindung gefördert Medien und digitale Technologien geprüft Problemlösung gefördert Projektmanagement gefördert Soziale Kompetenzen Kommunikation gefördert Kooperation und Teamarbeit gefördert Menschenführung und Verantwortung gefördert Persönliche Kompetenzen Anpassung und Flexibilität gefördert Kreatives Denken geprüft Kritisches Denken gefördert Integrität und Arbeitsethik gefördert Selbstbewusstsein und Selbstreflexion gefördert Selbststeuerung und Selbstmanagement gefördert

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte	8 KP
Prüfende	K. Stephan
Form	benotete Semesterleistung
Prüfungssprache	Englisch
Repetition	Repetition nur nach erneuter Belegung der Lerneinheit möglich.
Zulassungsbedingung	Good knowledge of principles of statistics, good programming skills (MATLAB is required; Julia an additional bonus).
Zusatzinformation zum Prüfungsmodus	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.

Lernmaterialien

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Einschränkungen

Angeboten in

Studiengang	Bereich	Typ
Biomedical Engineering Master	Kernfächer der Vertiefung	W
Biomedical Engineering Master	Wahlfächer der Vertiefung	W
Biomedical Engineering Master	Wahlfächer der Vertiefung	W
DAS in Data Science	Neural Information Processing	W
Data Science Master	Interdisziplinäre Wahlfächer	W
Elektrotechnik und Informationstechnologie Master	Vertiefungsfächer	W
Elektrotechnik und Informationstechnologie Master	Empfohlene Fächer	W
Neural Systems and Computation Master	Theoretische und Computergestützte Neurowissenschaften	W
Rechnergestützte Wissenschaften Master	Biologie	W