MYAB0311c Biomedical Data Analysis - exercise

Faculty of Medicine
autumn 2025
Extent and Intensity
0/1/0. 1 credit(s). Type of Completion: z (credit).
Teacher(s)
Ing. Martin Gajdoš, Ph.D. (seminar tutor)
Ing. Martin Kojan, Ph.D. (seminar tutor)
Ing. Petr Kudlička (seminar tutor)
Ing. Martin Lamoš, Ph.D. (seminar tutor)
Ing. Radek Mareček, Ph.D. (seminar tutor)
Ing. Michal Mikl, Ph.D. (seminar tutor)
Ing. Lubomír Vojtíšek, PhD. (seminar tutor)
Guaranteed by
Ing. Michal Mikl, Ph.D.
Multimodal and Functional Imaging Laboratory – Centre for Neuroscience – Central European Institute of Technology
Supplier department: Multimodal and Functional Imaging Laboratory – Centre for Neuroscience – Central European Institute of Technology
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Biomedical Data Analysis course is based on the premise that an important aspect of neuroscience, whether in science and research or in the practical (clinical) application, is the ability to understand different investigative methods and the data we obtain as part of these data. The course aims to introduce students to the data that are the output of different investigative methods and the principles and possibilities of processing and evaluating these data. After completing the course, the student will be able to distinguish between different types of data in neuroscience, will be able to choose an appropriate method of processing them and will be aware of the limitations that individual data (and the investigative methods by which the data were obtained) have in terms of interpretation.
Learning outcomes
Upon successful completion of the course, the student will:
- know the types of data that are the output of individual investigative methods in neurosciences
- know the appropriate data formats and possible programs used to process the data
- know the limitations of each investigative method and the data obtained
- know how to select the appropriate tool and procedure to process a given type of neuroscience data
Syllabus
  • Introduction to the exercise, familiarization with the software tools that will be used during the exercise. Presentation of the assessment of the work on the exercise or group assignments and the conditions for the award of credit.
  • Working with data in DICOM format and conversion to NIFTI format, BIDS structure, data from other modalities beyond MR and complex data/metadata organization in neuroscience studies.
  • Multimodal data fusion, subtraction of PET and SPECT images
  • Processing of fMRI data in Matlab and SPM, pre-processing of fMRI data, quality control of fMRI data
  • Statistical models of fMRI data in SPM - use of general linear model
  • Working with fMRI results in Matlab, SPM (data export, visualization, etc.)
  • Processing fMRI connectivity using SPM and other tools in Matlab
  • Basic processing of diffusion MR data
  • Processing of morphometric MR data in SPM and FreeSurfer
  • Processing of perfusion MR data
  • MR spectroscopy processing with LC model
  • Pre-processing of EEG data (working with EEG formats, detection and suppression of artifacts in EEG data, tools for working with EEG data).
  • EEG data analysis (evoked potential analysis, time-frequency analysis of EEG data).
  • Presentation of the results of independent group work on the processing of a selected type of data and award of credit.
Literature
    recommended literature
  • • Jan Jiri. Medical Image Processing, Reconstruction and Restoration - Concepts and Methods. CRC Press 2005. ISBN 9780429114731
  • Diffusion MRI : from quantitative measurement to in-vivo neuroanatomy. Edited by Heidi Johansen-Berg - Timothy E. J. Behrens. Second edition. Amsterdam: Elsevier, 2014, xii, 614. ISBN 9780123964601. info
  • HUETTEL, Scott A.; Allen W. SONG and Gregory MCCARTHY. Functional magnetic resonance imaging. Third editon. Sunderland, Massachusetts: Sinauer Associates, 2014, xvi, 573. ISBN 9780878936274. info
  • POLDRACK, Russell A.; Jeanette A. MUMFORD and Thomas E. NICHOLS. Handbook of functional MRI data analysis. New York: Cambridge University Press, 2011, x, 228. ISBN 9780521517669. info
  • Statistical parametric mapping : the analysis of functional brain images. Edited by K. J. Friston. 1st ed. Amsterdam: Elsevier, 2007, vii, 647. ISBN 9780123725608. info
Teaching methods
Practical seminars, short discussions with students, independent work on computers, assignment of independent homework in groups
Assessment methods
In order to obtain credit, participation in all exercises is necessary (an exception is an excuse for health reasons), or substitution of absence by preparation of a short report on the topic of the exercise. It is also necessary to hand in the completed assignments, which are given during the exercise or as independent group work.
The maximum number of points from the exercise is 20 and these points enter into the overall assessment of the course Biomedical Data Analysis together with the following written test and oral examination.
Language of instruction
Czech
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