Checking date: 16/05/2023

Course: 2023/2024

Master in Machine Learning for Health (Plan: 480 - Estudio: 359)

Coordinating teacher: DESCO MENENDEZ, MANUEL

Department assigned to the subject: Bioengineering Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
- Biomedical image processing (in case the student had not taken a similar subject in the bachelor degree)
Neuroimaging is nowadays an essential tool, not only for clinical diagnosis, but also for research in Neuroscience and cognitive processes. The numerous imaging methodologies and the complexity of the data generated lead to the need to use highly specialized image processing techniques, the use of which requires a highly multidisciplinary training. This course aims, first of all, to familiarize the student with the neurophysiological and neuropsychological knowledge necessary to process and interpret neuroimaging, as well as to impart a basic knowledge of the imaging techniques involved. On that bases, the fundamentals of preprocessing and processing of structural, diffusion, and functional magnetic resonance neuroimaging data are explained and the student is trained to be able to define his own analyses, conventional or in the context of machine learning, as well as to interpret the results from a critical and multidisciplinary point of view.
Skills and learning outcomes
Description of contents: programme
1. Introduction to neuroimaging 1.1. Introduction: Course presentation; basic concepts in neuroscience, neuroimaging methods 1.2. Brain and mental processes 1.3. General concepts: Data formats, MRI acquisition, processing tools 2. Structural MRI 2.1 Structural MRI processing 2.2 Practical session 1: MRI basics and structural MRI 3. Diffusion-Weighted Imaging 3.1 Introduction to diffusion-weighted imaging 4. Functional MRI 4.1 Functional MRI tasks and BOLD contrast 4.2 Preprocessing of functional MRI 4.3 Practical session on fMRI preprocessing 4.4 Task-based MRI and neuroimaging analysis 4.5 Resting-state fMRI and functional connectivity 4.6 Practical session on fMRI and analysis 5. Advanced topics 5.1 Machine learning in neuroimaging 5.2 Reliability: Can you believe on your results?
Learning activities and methodology
AF3 Theoretical practical classes AF4 Laboratory practices AF6 Team work AF7 Student individual work AF8 Partial and final exams Activity code total #hours presence #hours % Student Presence AF3 16,5 16,5 100% AF4 4,5 4,5 100% AF6 15 0 0% AF7 37 0 0% AF8 2 2 100% TOTAL SUBJECT 75 23 30,66%
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
Calendar of Continuous assessment
Basic Bibliography
  • Janine Bijsterbosch, Stephen Smith and Christian Beckmann. Resting State fMRI Functional Connectivity. Oxford Neuroimaging Primers. 2017
  • Mark Jenkinson and Michael Chappell. Introduction to Neuroimaging Analysis . Oxford Neuroimaging Primers. 2018
  • Russell A. Poldrack, Jeanette A. Mumford, Thomas E. Nichols. Handbook of Functional MRI Data Analysis. Cambridge University Press. 2011
  • Susumu Mori and J-Donald Tournier. Introduction to Diffusion Tensor Imaging: And Higher Order Models. Academic Press. 2013
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Derek K. Jones. Diffusion MRI: Theory, Methods, and Applications. Oxford University Press. 2011
  • Hernando Ombao, Martin Lindquist, Wesley Thompson and John Aston. Handbook of Neuroimaging Data Analysis. Chapman & Hall/CRC Handbooks of Modern Statistical Methods. 2016
  • Scott A. Huettel, Allen W. Song, and Gregory McCarthy. Functional Magnetic Resonance Imaging. Oxford University Press. 2014
Recursos electrónicosElectronic Resources *
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus may change due academic events or other reasons.