Introduction to neuroimaging analysis

  • Course teacher Jelena Božek
  • Associate teachers -
  • ECTS credits 5
  • Number of hours: Lectures + Seminars + Exercises 30 0 15

Course objectives

The course will introduce students to the basics of image acquisition using magnetic resonance for acquiring structural, functional and diffusion images of the brain.

The goal of the course is to teach the methods for computer processing of these images and to give knowledge about scientific and clinical requirements for brain image analysis.

Students will be able to apply the acquired knowledge in the field of neuroscience and independently solve research questions.

Enrolment requirements and/or entry competences required for the course

-

Learning outcomes at the level of the programme to which the course contributes

  • Apply specific knowledge and skills from selected disciplines constituting cognitive science.
  • Integrate insights, methods, and levels of analysis across different disciplines into a unified framework for understanding the human mind and cognition in general.
  • Critically evaluate cognitive science findings and synthesize information to be employed in a collaborative professional environment.
  • Participate in data-driven innovation projects and apply appropriate data science tools.
  • Apply interdisciplinary approach in examining phenomena pertaining to cognition.

Course content (syllabus)

  1. Introduction to human brain anatomy.
  2. Introduction to brain image acquisition using magnetic resonance (MR). Basics of acquiring structural, functional and diffusion images.
  3. Introduction to structural MR image processing. Image registration and distortion correction.
  4. Segmentation and analysis of brain structures.
  5. Surface-based analysis.
  6. Functional MR image (fMRI) processing.
  7. General linear model (GLM).
  8. Single subject analysis. Modelling. Statistics.
  9. Group-level analysis. Multiple comparisons.
  10. Advanced GLM.
  11. Resting state fMRI (rs-fMRI) processing. Independent Component Analysis.
  12. Dual regression. Network modelling analysis.
  13. Diffusion Tensor Imaging (DTI) analysis.
  14. Diffusion tractography.
  15. Complementary approaches in neuroimaging analysis.

Student responsibilities

Attending lectures and laboratory exercises, homework, midterm and final exam.

Required literature

  • Mark Jenkinson, Michael Chappell (2018.), Introduction to Neuroimaging Analysis, Oxford University Press
  • Janine Bijsterbosch, Stephen M. Smith, Christian F. Beckmann (2018), Introduction to Resting State fMRI Functional Connectivity, Oxford University Press
  • Scott A. Huettel, Allen W. Song, and Gregory McCarthy (2014), Functional Magnetic Resonance Imaging, Oxford University Press
  • Heidi Johansen-Berg, Timothy E.J. Behrens (2013), Diffusion MRI - From Quantitative Measurement to In vivo Neuroanatomy, Elsevier

Optional literature

  • Thomas A. Woolsey, Joseph Hanaway, Mokhtar H. Gado (2017), The Brain Atlas - A Visual Guide to the Human Central Nervous System, John Wiley & Sons