227-0973-00L  Translational Neuromodeling

SemesterSpring Semester 2018
LecturersK. Stephan
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
227-0973-00 GTranslational Neuromodeling4 hrs
Fri12:15-16:00ETZ E 6 »
23.02.12:15-16:00ETZ F 91 »
K. 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 discusses (hierarchical) Bayesian models of neuroimaging data and behaviour in detail.
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 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.
LiteratureSee TNU website:
Link
Prerequisites / NoticeBasic statistical knowledge, MATLAB programming skills

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersK. Stephan
Typeend-of-semester examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered at the end after the course unit. Repetition only possible after re-enrolling.
Additional information on mode of examinationStudents 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.

Learning materials

 
Main linkTranslational Neuromodeling
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Biomedical Engineering MasterRecommended Elective CoursesWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Neural Systems and Computation MasterNeural Computation and Theoretical NeurosciencesWInformation