13D051NVM - Neuroimaging techniques

Course specification
Course title Neuroimaging techniques
Acronym 13D051NVM
Study programme Electrical Engineering and Computing
Type of study doctoral studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 9.0 Status elective
      Condition none
      The goal Applying modern methods of acquisition, processing and analysis of brain signals in order to study the structure, function and connection of the brain. Improving existing methods.
      The outcome Students will acquire a broad knowledge and practical experience of how modern neuroimaging techniques and their application improve understanding of brain functions and disorders. They will be able to choose the appropriate modality for acquisition and participate in a multidisciplinary team that design a research experiment in the field of neuroscience.
      Contents of lectures A review of medical modalities for brain imaging (EEG, PET, structural, diffusion and functional MRI, etc.). Methods of preprocessing, statistical analysis and modeling of brain signals. Linear and nonlinear models of signals. Multivariate decomposition in brain imaging. Functional connectivity analysis for fMRI data. Machine learning methods for neuroimaging.
      Contents of exercises
      1. H. Ombao, M. Lindquist, W. Thompson, J. Aston, (Eds.) "Handbook of Neuroimaging Data Analysis", CRC Press,Taylor & Francis Group, 2017. (Original title)
      2. S.A. Huettel, A.W. Song, G. McCarthy, "Functional Magnetic Resonance Imaging", Second Ed., Salvador Dali, Gala-Salvador Dali, 2008. (Original title)
      3. U. Windhorst, H. Johansson, "Modern Techniques in Neuroscience Reserach", Springer, 1999. (Original title)
      4. I. Rish, M.G. Murphy, "Machine Learning and Interpretation in Neuroimaging", Springer International Pu, 2016. (Original title)
      5. Selected articles from journals Neuroimage (ISSN 1053-8119), Human Brain Mapping (ISSN 1065-9471) etc. (Original title)
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      Methods of teaching lectures, experiments in laboratory and clinics, project work, application of methods from recommended papers
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures Test paper
      Practical lessons Oral examination 30
      Seminars 70