Course catalogue doctoral education - VT18

  • Ansökan kan ske mellan 2017-10-16 och 2017-11-15
Application closed
Title Imaging in neuroscience: with a focus on MEG and EEG methods
Course number 3035
Programme Neurovetenskap
Language English
Credits 1.5
Date 2018-02-05 -- 2018-02-09
Responsible KI department Institutionen för klinisk neurovetenskap
Specific entry requirements Background in medicine, biomedicine, biology, psychology, cognitive science, medical imaging, computational biology or similar.
Purpose of the course The main purpose of the course is to provide the students with a solid understanding of the tools available to analyze brain activity data measured with magnetoencephalography (MEG) and electroencephalography (EEG). The students will develop the ability to critically review results provided by different methods, to select the most adequate tools and experimental designs to answer different questions and to compare their relative advantages.
Intended learning outcomes After attending the course the student should be able to: 1) follow the usual preprocessing steps of MEG and EEG; 2) give an overview of different methods to analyze the data and explain when to use them; 3) conduct MEG and EEG analysis using several methods; 4) describe different aspects of experimental design to have in consideration when creating a MEG and EEG study; 5) give a brief overview of the usage of MEG and EEG to study brain function; 6) give a brief overview of other techniques to study brain function non-invasively and describe their relative merits and challenges.
Contents of the course The course focuses on experimental design and analysis of MEG and EEG data. We will briefly introduce the basis of the MEG and EEG signal at a neural level, and how it is measured by the different sensor technologies applied in MEG and EEG. The data processing steps, before statistical analysis, will be explained. The application of general linear model analysis, parametric and nonparametric tests of MEG and EEG data will be explained, including correction for multiple comparisons. We will review experimental design considerations for developing MEG and EEG paradigms. The study of functional connectivity using MEG and EEG data will be introduced. Finally, we will also introduce machine learning techniques for functional data.
Teaching and learning activities The students will attend lectures, implement different steps of the data preprocessing and analysis during the hands-on sessions, present and discuss results.
Compulsory elements All parts of the course are mandatory. Absence can be compensated for by completion of an assignment on the material covered in the missed course instance.
Examination The learning outcomes will be assessed throughout the course during the hands-on sessions where the students have to perform data analyses. The students will also complete a more extensive assignment based on one of the hands-on sessions. In the final day of the course the students will present and discuss their assignments with the rest of the group.
Literature and other teaching material BOOK Riitta Hari & Aina Puce (2017). MEG-EEG primer. New York, NY : Oxford University Press 2017 ARTICLES Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993). Magnetoencephalography theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics 65, 413. Pfurtscheller G1, Lopes da Silva FH. (1999). Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999 Nov;110(11):1842-57. Hyvärinen A1, Oja E. (2000). Independent component analysis: algorithms and applications. Neural Network. May-Jun;13(4-5):411-30. Maris E1, Oostenveld R. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods. Aug 15;164(1):177-90. Näätänen R1, Paavilainen P, Rinne T, Alho K I (2007). Clinical Neurophysiology. The mismatch negativity (MMN) in basic research of central auditory processing: a review. Dec;118(12):2544-90. Epub 2007 Oct 10. Cichy, R. M., Pantazis, D., & Oliva, A. (2014). Resolving human object recognition in space and time. Nature Neuroscience, 17(3), 455462.
Number of students 8 - 24
Selection of students Selection will be based on 1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation), 2) date for registration as a doctoral student (priority given to earlier registration date)
More information
Additional course leader Course responsible:
Daniel Lundqvist
Department of Clinical Neuroscience

Contact persons Lau Möller Andersen
Department of Clinical Neuroscience

Mikkel Vinding
Department of Clinical Neuroscience
Earlier evaluation of the course Not available
Course responsible Daniel Lundqvist
Institutionen för klinisk neurovetenskap
Contact person -