Course catalogue doctoral education - VT18

    Startpage
  • Ansökan kan ske mellan 2017-10-16 och 2017-11-15
Application closed
Print
Title Functional magnetic resonance imaging: data analysis and experimental design
Course number 2985
Program Neurovetenskap
Language English
Credits 1.5
Date 2017-11-07 -- 2017-11-23
Responsible KI department Institutionen för 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 functional magnetic resonance imaging (fMRI). 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.
Learning outcomes After attending the course the student should be able to: 1) follow the usual preprocessing steps of fMRI; 2) give an overview of different methods to analyze the data and explain when to use them; 3) conduce fMRI analysis using several methods; 4) describe different aspects of experimental design to have in consideration when creating a fMRI study; 5) give a brief overview of the usage of magnetic resonance imaging to study brain structure and 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 fMRI data. We will briefly introduce the basis of the blood-oxygen-level dependent (BOLD) signal and how it is measured. Structural measures of gray and white matter will also be introduced as well as other techniques to measure functional activity non-invasively. The image processing steps, before statistical analysis, will be explained. The application of general linear model analysis to fMRI data will be explained, including random effects analysis and correction for multiple comparisons. We will review experimental design considerations for developing a fMRI paradigm. The study of functional connectivity using fMRI data will be introduced. Finally, we will also introduce machine learning techniques and graph theoretical analysis 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 Poldrack, Mumford and Nichols, Handbook of Functional MRI Data Analysis, Cambridge University Press, New York, 2011. Beckmann, Modelling with independent components, NeuroImage 62: 891-901, 2012. Haynes, A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives, Neuron 87: 257-270, 2015. Lindquist and Wager, Principles of functional Magnetic Resonance Imaging, in Handbook of Neuroimaging Data Analysis. London: Chapman & Hall, CRC Press, 2014. Rubinov and Sporns, Complex network measures of brain connectivity: Uses and interpretations, NeuroImage 52: 1059-1069, 2010. Van Dijk et al. Intrinsic Functional Connectivity As a Tool For Human Connectomics: Theory, Properties, and Optimization, Journal of Neurophysiology 103: 297-321, 2010.
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 The course will be from 9.00 to 16.00 on the following days: Tuesday 2017-11-07, Thursday 2017-11-09, Tuesday 2017-11-14, Thursday 2017-11-16 and Thursday 2017-11-23.
Additional course leader
Earlier evaluation of the course Not available
Course responsible Rita Almeida
Institutionen för neurovetenskap

rita.almeida@ki.se
Contact person Peter Fransson
Institutionen för klinisk neurovetenskap

Peter.Fransson@ki.se


Jonathan Berrebi
Institutionen för klinisk neurovetenskap

Jonathan.Berrebi@ki.se