Universität Wien

200144 SE Seminar in Applied Psychology: Mind and Brain (2021W)

fMRi data preprocessing

4.00 ECTS (2.00 SWS), SPL 20 - Psychologie
Continuous assessment of course work
REMOTE

Dieses Anwendungsseminar kann für alle Schwerpunkte absolviert werden!

Anwendungsseminare können nur fürs Pflichtmodul B verwendet werden! Eine Verwendung fürs Modul A4 Freie Fächer ist nicht möglich.
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 20 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

The course is split in theoretical sessions and sessions in which the students will be analyzing data at home in small groups.

  • Thursday 07.10. 13:15 - 14:45 Digital
  • Thursday 14.10. 13:15 - 14:45 Digital
  • Thursday 21.10. 13:15 - 14:45 Digital
  • Thursday 28.10. 13:15 - 14:45 Digital
  • Thursday 04.11. 13:15 - 14:45 Digital
  • Thursday 11.11. 13:15 - 14:45 Digital
  • Thursday 18.11. 13:15 - 14:45 Digital
  • Thursday 25.11. 13:15 - 14:45 Digital
  • Thursday 02.12. 13:15 - 14:45 Digital
  • Thursday 09.12. 13:15 - 14:45 Digital
  • Thursday 16.12. 13:15 - 14:45 Digital
  • Thursday 13.01. 13:15 - 14:45 Digital
  • Thursday 20.01. 13:15 - 14:45 Digital
  • Thursday 27.01. 13:15 - 14:45 Digital

Information

Aims, contents and method of the course

The course will teach the basics of data analyses for fMRI studies with a focus on preprocessing (using FSL) as well as further analyses of resting state data. Although previous experience with fMRI data analyses is not required, fMRI preprocessing is advanced. Students should all have a fundamental understanding of data analyses and statistics and should enjoy trouble-shooting on the computer and analyzing data. The students are required to install an FSL package on their own computer. They will learn about the basics of MRI and fMRI, about how important preprocessing and noise reduction is, responsible conduct of research, and a career in academia. The course is split in theoretical sessions and practical sessions in which the students will be analyzing data at home in small groups.

Assessment and permitted materials

Assignments for each practical session (40%) will be graded as well as a mini paper at the end of class (40%). Participation and group discussion in the theoretical sessions will be graded with 20%. Students fail class if they do not hand in all assignments.

Minimum requirements and assessment criteria

Interest in neuroimaging, data analyses, and coding. Students should all have a fundamental understanding of data analyses and statistics and should enjoy trouble-shooting on the computer and analyzing data. English is a requirement.

Examination topics

Reading list

Association in the course directory

Last modified: We 21.06.2023 00:18