Neuroimaging Analysis

Data is displayed for academic year: 2023./2024.

Laboratory exercises

Course Description

Principles of magnetic resonance imaging for obtaining structural, functional and diffusion images of the brain. Basic concepts from neuroscience. Computer processing of structural MR images of the brain. Segmentation and analysis of anatomical structures in the brain. 3D representation of the brain and analysis and determination of features of the cortical surface of the brain. Computer processing of functional MR images (fMRI) of the brain. Analysis of independent components for processing resting-state fMRI (rs-fMRI) images. Computer processing of diffusion MR (dMRI) brain images and tensor analysis. Tractography.

Study Programmes

University graduate
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Learning Outcomes

  1. Describe the acquisition of magnetic resonance (MR) images.
  2. Differentiate features of structural, functional and diffusion images of the brain.
  3. Apply computing tools for processing brain MR images.
  4. Use general linear model for the analysis of functional MR images.
  5. Apply independent component analysis for processing resting-state functional MR images.
  6. Analyze diffusion images using tensors and tractography.
  7. Independently design a processing pipeline for brain MR image analysis to solve a research question.

Forms of Teaching


Lectures are conducted with the help of power-point presentations with explanations on the board. Lectures are available in electronic form online.

Independent assignments

During the course there will be short homework assignments and activity during the term will be graded.


During the course, laboratory exercises are planned, which include practical and experimental work. At the end of each exercise there is a short exam on the performed activities and understanding of the material.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 30 % 50 % 30 %
Mid Term Exam: Written 0 % 25 % 0 %
Final Exam: Written 0 % 35 %
Final Exam: Oral 10 %
Exam: Written 0 % 50 %
Exam: Oral 20 %

Homeworks bring bonus points.

Week by Week Schedule

  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). Single subject analysis. Modelling. Statistics.
  8. Midterm exam.
  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. Diffusion tractography.
  14. Complementary approaches in neuroimaging analysis.
  15. Final exam.


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

For students


ID 240807
  Summer semester
L2 English Level
L1 e-Learning
30 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory

Grading System

87 Excellent
75 Very Good
60 Good
50 Sufficient