Checking date: 12/05/2020


Course: 2019/2020

Neuroimaging
(18063)
Study: Master in Information Health Engineering (359)
EPI


Coordinating teacher: DESCO MENENDEZ, MANUEL

Department assigned to the subject: Department of Bioengineering and Aerospace Engineering

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Students are expected to have completed
- Deep learning - Biomedical image processing (in case the student had not taken a similar subject in the bachelor degree)
Competences and skills that will be acquired and learning results.
Basic competences CB6 Having and understanding the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context CB7 Students know how to apply their acquired knowledge and problem-solving skills in new or unfamiliar settings within broader (or multidisciplinary) contexts related to their field of study. CB8 Students are able to integrate knowledge and to face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments. CB9 Students know how to communicate their conclusions and the knowledge and ultimate reasons behind them to specialised and non-specialised audiences in a clear and unambiguous way. CB10 Students have the learning skills that will enable them to continue studying in a way that will be largely self-directed or autonomous. General competences CG2 Ability to apply the knowledge of skills and research methods related to engineering. CG3 Ability to apply the knowledge of research skills and methods related to Life Sciences. CG4 Ability to contribute to the widening of the frontiers of knowledge through an original research, part of which merits publication referenced at an international level. CG5 Ability to perform a critical analysis and an evaluation and synthesis of new and complex ideas. CG6 Ability to communicate with the academic and scientific community and with society in general about their fields of knowledge in the modes and languages commonly used in their international scientific community. Specific competences CE6 Ability to understand the basis of the main technologies involved in biomedical imaging systems. CE7 Ability to solve a biomedical problem from an engineering perspective based on the acquisition and processing of biomedical images
Description of contents: programme
1. Introduction to neuroimaging ¿ Introduction: Course presentation; Neuroimaging methods ¿ General concepts: data formats, processing tools (parallel, cloud), reliability (multiple comparison corrections) ¿ Practical session 1: Neuroimaging Software and Basics 2. Preprocessing, processing and single-subject methods ¿ Structural neuroimaging and spectroscopy ¿ Practical session 2: Structural MRI ¿ MRI diffusion and connectivity ¿ Practical session 3: Diffusion MRI ¿ Functional MRI (Tasks, BOLD, Design, processing,Resting-state) ¿ Connectivity and graph theory ¿ Practical session 4: fMRI 3. Multi-subject methods ¿ Statistics and multi-subject analysis ¿ Machine learning ¿ Practical session 5: MRI Project 4. Closure: Can you believe all these results?
Learning activities and methodology
AF3 Theoretical practical classes AF4 Laboratory practices AF5 Tutorials AF6 Team work AF7 Student individual work AF8 Partial and final exams Activity code total hours number presencial hours number % Student Presence AF3 134 134 100% AF4 42 42 100% AF5 24 0 0% AF6 120 0 0% AF7 248 0 0% AF8 16 16 100% SUBJECT TOTAL 600 184 30,66%
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • 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 and the academic weekly planning may change due academic events or other reasons.